Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization

Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization cancers Article Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization 1 , 1 2 3 1 Georg Gihr *, Diana Horvath-Rizea , Patricia Kohlhof-Meinecke , Oliver Ganslandt , Hans Henkes , 4 5 5 5 , Wolfgang Härtig , Aneta Donitza , Martin Skalej and Stefan Schob * Katharinenhospital Stuttgart, Clinic for Neuroradiology, 70174 Stuttgart, Germany; [email protected] (D.H.-R.); [email protected] (H.H.) Katharinenhospital Stuttgart, Department for Pathology, 70174 Stuttgart, Germany; [email protected] Katharinenhospital Stuttgart, Clinic for Neurosurgery, 70174 Stuttgart, Germany; [email protected] Paul Flechsig Institute for Brain Research, University of Leipzig, 04103 Leipzig, Germany; [email protected] Department for Neuroradiology, Clinic and Policlinic for Radiology, University Hospital Halle (Saale), 06120 Halle (Saale), Germany; [email protected] (A.D.); [email protected] (M.S.) * Correspondence: [email protected] (G.G.); [email protected] (S.S.); Tel.: +49-711-2785-4454 (G.G.); +49-345-557-2342 (S.S.) Simple Summary: Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate Citation: Gihr, G.; Horvath-Rizea, D.; Kohlhof-Meinecke, P.; Ganslandt, O.; presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics Henkes, H.; Härtig, W.; Donitza, A.; on a cellular and subcellular scale. Skalej, M.; Schob, S. Diffusion Weighted Imaging in Gliomas: A Abstract: (1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Histogram-Based Approach for Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via Tumor Characterization. Cancers the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor 2022, 14, 3393. https://doi.org/ characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of 10.3390/cancers14143393 ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas Academic Editor: John Virostko (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate- dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) Received: 21 June 2022 promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Accepted: 9 July 2022 Statistical analysis was performed to elucidate associations between histogram features and WHO Published: 13 July 2022 grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles Publisher’s Note: MDPI stays neutral (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in with regard to jurisdictional claims in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for published maps and institutional affil- maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences iations. were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation Copyright: © 2022 by the authors. rate and clinically significant mutations in case of astrocytic gliomas. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: astrocytoma; glioblastoma; LGG; HGG; DWI; histogram; tumor biology; radiomics distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Cancers 2022, 14, 3393. https://doi.org/10.3390/cancers14143393 https://www.mdpi.com/journal/cancers Cancers 2022, 14, 3393 2 of 15 1. Introduction Gliomas, along with rarer tumors arising from neuroepithelial tissue, represent circa 28 percent of all brain tumors [1]. They derive from the enduring glia of the brain and are either grouped as astrocytoma or oligodendroglioma, depending on the glial cell type they originate from. Furthermore, based upon histopathologic tumor characteristics and with increasing importance related to genetic alterations, the World Health Organization (WHO) taxonomy subdivides gliomas from the lowest grade, I, to the highest grade, IV. Whereas WHO grade I and II gliomas, which usually present a rather benign tumor biology, are classified as low-grade gliomas (LGG), entities of WHO categories III and IV, which exhibit a more aggressive tumor behavior, are classified as high-grade gliomas (HGG). With about 14.6 percent of all newly diagnosed brain tumors, WHO grade IV astrocytoma (or glioblastoma, GBM) represents the most frequent entity, followed by WHO grade II astrocytoma (1.8 percent), then WHO grade III astrocytoma (1.7 percent) and WHO grade I astrocytoma (1.3%) [1]. In fact, gliomas are tumors of substantial heterogeneity, both in terms of the underlying histopathology, manifestation age and the associated clinical course. For example, WHO grade IV astrocytoma, being the most aggressive glioma, is associated with survival rates of 15 months on average [2], whereas WHO grade I (pilocytic) astrocytoma, the most frequent pediatric glioma, is associated with a rather uneventful further course [3]. Since the 2016 update of the WHO classification of CNS tumors, the diagnostic paradigm in case of gliomas shifted from a predominantly histological perspective to- wards a genetic- and molecular-based approach [4]. The classification of diffuse gliomas (WHO II-IV), in particular, now includes mutation analysis of the isocitrate dehydrogenase (IDH) 1 and 2 gene, the telomerase (TERT) promoter, the transcriptional regulator (ATRX) gene and the tumor suppressor gene TP53, as well as methylation analysis of the (MGMT) promoter and detection of 1p/19q co-deletions. Besides the prognostic value of this ge- netical tumor profile, it is essential for the final diagnosis and supersedes the histological diagnostics. For example, the diagnosis of oligodendroglioma is made only in the presence of a complete 1p/19q co-deletion, regardless of the underlying histological appearance [5]. Magnetic resonance imaging (MRI) is the most important imaging modality for gliomas. Presurgical tumor localization, treatment planning and the visualization of eloquent brain areas are the main tasks of MRI in this context, usually performed via conventional T2- and T1-weighted MRI sequences with 2D or 3D acquisition. However, through partially overlapping tumor morphology and significant interobserver variability, the diagnostic performance with those morphological MRI sequences remains limited for glioma grading, and hence, preoperative risk stratification. To overcome those limitations, advanced MRI approaches, for example MR spectroscopy, DWI and gadolinium-based perfusion techniques have been established and integrated in the majority of clinical MRI protocols. Especially DWI, which provides ADC maps that allow one to quantify the spatial extent of diffusion in vivo on a m scale [6], enables evaluation of the microscopic archi- tecture of a biological sample [7], which renders the technique an important oncological tool [8]. For example, DWI has been shown to be useful in assessing the growth potential of gliomas [9,10], in glioma grading [11], in estimating the clinical prognosis of glioma patients [12] or even in the differentiation of GBM from brain abscesses [13]. Most DWI studies conducted in the past have focused predominantly on first-order features of the ADC histogram profile, i.e., the mean, median, extreme values and percentiles. However, advanced HA includes an estimation of second-order features, i.e., skewness, entropy and kurtosis of the ADC. The latter allow an assessment of the shape of the ADC value distri- bution within the lesion and therefore facilitate assessment of tumor heterogeneity [14]. Recent studies have further substantiated the value of advanced HA for tumor grading and evaluation of individual tumor biology based on DWI [10,15–21] and even signal intensities of conventional T1- and T2-sequences [22–25]. Nevertheless, despite the recent developments in the field of advanced MRI techniques, there still are no reliable in vivo imaging biomarkers that could lead to an effective reduction Cancers 2022, 14, 3393 3 of 15 in tumor biopsies or even replace them, for example, in the case of suspected low-grade gliomas. Therefore, further imaging studies are necessary to reveal new and to evaluate already known imaging biomarkers. Considering the available data and the need for improved, non-invasive character- ization of gliomas, the aim of this study was to evaluate whether whole tumor HA of ADC profiles has the ability to (i) differentiate LGG from HGG, (ii) predict the proliferative activity of the glioma represented by Ki-67 immunolabelling and (iii) predict the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) MGMT methylation profile, as well as the IDH (isocitrate dehydrogenase) mutation profile. 2. Materials and Methods 2.1. Patients Our radiological database was reviewed for patients suffering from primary brain tumors/gliomas. A total of 114 patients who were treated in our center between January 2012 and February 2017 were included. Each diagnosis was confirmed histologically either by biopsy or open tumor surgery. The samples were further processed neuropathologically as outlined below. The patients included had sufficient pre-treatment MRI including DWI and did not exhibit signs of lesional hemorrhage or calcification. Eventually, 82 patients met those criteria. In total, 26 patients had LGG (WHO I: n = 7, WHO II: n = 19; 12 females, 14 males; average age: 34a) and 56 had HGG (WHO III: n = 11, WHO IV: n = 45; 22 females, 34 males; average age: 62a). Twenty-three percent (19/82) of the patients revealed an IDH-1 mutation and 71 percent (58/82) an IDH-1 wildtype genotype (for 5 patients no IDH-1 mutation status was available). The methylation of the MGMT promoter was positive in 39 percent (32/82); 43 percent (35/82) had tumors with an unmethylated MGMT promoter. In the remaining 15 patients, no MGMT promoter profile was available. In 9.8 percent (8/82) Ki-67 immunolabelling was not available. 2.2. MRI Protocol MRI was performed in all cases using a 1.5 T MAGNETOM scanner (Aera or Sym- phony, using a Tx/Rx CP head coil, Siemens, Erlangen, Germany). The scanning protocol consisted of axial T1-weighted (T1w) spin echo (SE) sequences (TR/TE: 453/17, flip angle: 90 , slice thickness: 5 mm, acquisition matrix: 320  179, field of view: 230  187 mm) be- fore and after the application of a gadolinium-based contrast agent (Gadobutrol, Gadovist, Bayer Schering Pharma, Leverkusen, Germany), an axial T2-weighted (T2w) turbo spin echo (TSE) sequence (TR/TE: 5390/99, flip angle: 150 , slice thickness: 5 mm, acquisition matrix: 512  291, field of view: 230  187 mm) and an axial DWI sequence (readout- segmented, multi-shot EPI sequence; TR/TE: 5500/103, flip angle 90 , slice thickness: 5 mm, acquisition matrix: 152  144, field of view: 230  230 mm). All images were digitalized and reviewed by two experienced readers (DHR, SS) who were blinded regarding the histopathological report. Image analysis was performed using a commercially available PACS workstation (Impax EE R20 XII). 2.3. Histogram Analysis of ADC Volumes T1w images, T2w images and ADC maps were anonymized and extracted from the institutional PACS as DICOM files. Subsequently, histogram analysis was performed via a custom-made, MATLAB-based analysis tool (programmed by N.G. using MATLAB, The Mathworks, Natick, MA, USA) as follows. In the case of contrast-enhancing tumors, T1w (post contrast) images, and in the case of non-enhancing tumors, T2w images, were uploaded to the visual interface displaying the anatomical image in order to correlate and tag the tumor of each patient entirely. Regions of interest (ROIs), which were manually drawn in T1w (post contrast) or T2w images along the corresponding border of the whole visible signal alteration (contrast-enhancing region or T2w hyperintense region) in every slice of detectable tumor, were propagated and co-registered to the corresponding ADC maps. Using the MATLAB library, the following features of the ADC histogram of the Cancers 2022, 14, 3393 4 of 15 whole tumor volume were computed: mean, minimum (min), maximum (max), 10th percentile (p10), 25th percentile (p25), 75th percentile (p75), 90th percentile (p90), modus, median, standard deviation (SD), skewness, kurtosis and entropy. 2.4. Immunolabelling and Polymerase Chain Reaction (PCR)—Molecular Neuropathology Testing was performed as described previously [10]: Tumor specimens were fixed in formaldehyde and embedded in paraffin for histology, immunolabelling and PCR. The embedded tissue was cut with 3m thickness and stained with hematoxylin and eosin (H&E). Immunolabelling was done employing antibodies raised against IDH1-R132H (1:20 diluted, cat. no. DIA-H09; Dianova, Hamburg, Germany) and MIB/Ki67 (1:800 diluted; cat. no. M7240; Dako Denmark A/S, Glostrup, Denmark). Histology was digitalized using a Leica microscope equipped with a DFC290 HD digital camera; LAS V4.4 was used for image processing (Leica Microsystems, Wetzlar, Germany). Necrosis and hemorrhage were absent in the investigated samples; the presence of viable tumor cells was also confirmed via microscopy. IDH1 immunolabelling resulting in strong cytoplasmic staining was interpreted as IDH1 positive. The proliferation index was calcu- lated by dividing the Ki67-immunolabelled (stained) cellular nuclei by all nuclei. The area exhibiting the greatest number of Ki67-reactive nuclei was chosen in each sample. To determine the methylation profile of the MGMT promoter, DNA from each glioma was isolated using 10 m-thick sections (derived from the paraffin-embedded samples). Extraction was performed with the Maxwell RSC FFPE Plus DNA Kit AS1720 (Promega, Madison, WI, USA). Unmethylated cytosine residues were converted to uracil by bisul- fite treatment using the EpiTect Bisulfite Kit (QIAGEN, Hilden, Germany). All steps were performed in accordance with the manufacturer ’s instructions. PCR with bisulfite- converted DNA was performed for amplification and the methylation profile was evaluated via pyrosequencing using the Therascreen MGMT Pyro Kit (QIAGEN, in accordance with the manufacturers protocol). A methylation of 10 percent or more was considered methylation positive. 2.5. Statistical Analysis Descriptive statistics, group comparison with significance testing and computation of correlations was performed with GraphPad Prism 8 (GraphPad Software, CA, USA). The Shapiro–Wilk test was performed to test for Gaussian vs. non-Gaussian distribu- tion. Group comparisons of normally distributed values was performed with Student’s T test; non-normally distributed data were tested employing the Mann–Whitney U Test. Correlations between parameters with normal distribution were computed using Pear- son’s Correlation Coefficient. For parameters exhibiting non-Gaussian distribution, the Spearman–Rho coefficient was computed. In all instances, p-values < 0.05 were interpreted as statistically significant. As a last step, aiming to assess the accuracy of the investigated histogram features, the receiver operating characteristics (ROC) curve analysis was calculated, including the area under the curve (AUC). For the estimation of suitable cutoff values, Youden’s index was calculated. 3. Results Figure 1 demonstrates the commonly used contrast-enhanced T1-weighted MRI scans of different representative glioma entities (WHO grade II and WHO grade IV), the calcu- lated ADC histogram of the respective tumor, as well as the corresponding histopathological images, consisting of HE staining and Ki-67 immunolabeling. For better comprehensibility, Table 1 summarizes the results of the descriptive statisti- cal analysis of all included cases. In brief, statistically significant differences between both entities, LGG vs. HGG, were identified for the following set of ADC features: minimum, lower percentiles (p10 and p25), median, modus and entropy (p < 0.05 in all instances). As expected, Ki-67 positive Cancers 2022, 14, 3393 5 of 15 immunolabelling was significantly stronger in HGG compared to LGG. The results of the Cancers 2022, 14, x FOR PEER REVIEW 5 of 16 comparative statistics considering LGG and HGG are listed in Table 2 and graphically demonstrated in Figure 2. Figure Figure 1.1. T1w T1wMR MRimag images es pos post t gadoli gadolinium, nium,c c orr orrespondi esponding ng ADC ADC histograms, histogram H&E s, H& images E imag and es and Ki67 Ki67 immunolabelling sections from patients with WHO grade II—(a–d) and WHO grade IV glioma (e– immunolabelling sections from patients with WHO grade II—(a–d) and WHO grade IV glioma (e–h). The upper left image shows a T1w image after intravenous application of a gadolinium-based contrast h). The upper left image shows a T1w image after intravenous application of a gadolin- agent revealing a WHO grade II glioma in the left frontal lobe, extending into the precentral gyrus. ium-based contrast agent revealing a WHO grade II glioma in the left frontal lobe, extend- The atypical morphologic appearance of this LGG with contrast medium enhancement and cystic ing into the precentral gyrus. The atypical morphologic appearance of this LGG with con- areas renders differentiation of this lesion difficult from high-grade neoplasms, based on standard trast medium enhancement and cystic areas renders differentiation of this lesion difficult MRI sequences alone (a). The lower left image depicts T1w imaging of a WHO grade IV glioma from high-grade neoplasms, based on standard MRI sequences alone (a). The lower left arising from the parafalcine region of the right frontal lobe (e). The set of images in the second image depicts T1w imaging of a WHO grade IV glioma arising from the parafalcine region column show histograms of the respective entire tumor ADC volume (b,f) The set of images in the of the right frontal lobe (e). The set of images in the second column show histograms of third column demonstrate representative H&E stainings (c,g). The images in the last column (d,h) the respective entire tumor ADC volume (b,f) The set of images in the third column demonstrate Ki67 immunolabeling of the lesions. The LGG (WHO grade II) showed a proliferation demonstrate representative H&E stainings (c,g). The images in the last column (d,h) index of 5%, the HGG had a proliferation index of 50%. demonstrate Ki67 immunolabeling of the lesions. The LGG (WHO grade II) showed a pro- liferation index of 5%, the HGG had a proliferation index of 50%. Table 1. ADC histogram features of all included gliomas. ADC Histogram Features Mean  Standard Deviation Minimum Maximum For better comprehensibility, Table 1 summarizes the results of the descriptive sta- 5 2 1 tistica ADC l analy ,s is 10 of all mm incl s uded cases. 141.11  31.97 66.56 230.91 mean 5 2 1 ADC , 10 mm s 44.00  28.36 0.1 101.30 min Table 1. ADC histogram features of all included gliomas. 5 2 1 ADC , 10 mm s 278.35  60.11 107.30 397.80 max 5 2 1 ADC Histogram Features Mean ± Standard Deviation Minimum Maximum ADC , 10 mm s 100.16  20.25 44.50 160.70 p10 −5 2 −1 ADCmean, ×10 mm s 141.11 ± 31.97 66.56 230.91 5 2 1 ADC , 10 mm s 115.86  26.49 49.80 203.60 p25 −5 2 −1 ADCmin, ×10 mm s 44.00 ± 28.36 0.1 101.30 5 2 1 163.91  44.76 75.30 274.00 ADC , 10 mm s p75 −5 2 −1 ADCmax, ×10 mm s 278.35 ± 60.11 107.30 397.80 5 2 1 −5 2 −1 ADC , 10 mm s 186.27  48.66 82.79 283.70 ADCp10, ×10 mm s 100.16 ± 20.25 44.50 160.70 p90 −5 2 −1 5 2 1 ADCp25, ×10 mm s 115.86 ± 26.49 49.80 203.60 ADC , 10 mm s 137.65  35.92 59.85 263.30 median −5 2 −1 ADCp75, ×10 mm s 163.91 ± 44.76 75.30 274.00 5 2 1 ADC , 10 mm s 137.66  52.57 46.30 277.00 modus −5 2 −1 ADCp90, ×10 mm s 186.27 ± 48.66 82.79 283.70 5 2 1 ADC SD, 10 mm s 34.87  15.64 8.77 77.46 −5 2 −1 ADCmedian, ×10 mm s 137.65 ± 35.92 59.85 263.30 Kurtosis 4.72  3.65 1.35 23.34 −5 2 −1 ADCmodus, ×10 mm s 137.66 ± 52.57 46.30 277.00 −5 2 −1 Skewness 0.60  0.95 2.07 3.80 ADC SD, 10 mm s 34.87 ± 15.64 8.77 77.46 Kurtosis 4.72 ± 3.65 1.35 23.34 Entropy 4.87  0.61 3.25 6.19 Skewness 0.60 ± 0.95 −2.07 3.80 Entropy 4.87 ± 0.61 3.25 6.19 In brief, statistically significant differences between both entities, LGG vs. HGG, were identified for the following set of ADC features: minimum, lower percentiles (p10 and p25), median, modus and entropy (p < 0.05 in all instances). As expected, Ki-67 positive immunolabelling was significantly stronger in HGG compared to LGG. The results of the Cancers 2022, 14, 3393 6 of 15 Cancers 2022, 14, x FOR PEER REVIEW 6 of 16 Table 2. ADC histogram features and Ki67 immunolabelling in LGG vs. HGG. Significant results are comparative statistics considering LGG and HGG are listed in Table 2 and graphically written in bold letters. demonstrated in Figure 2. Low-Grade Glioma High-Grade Glioma t-Test ADC Histogram Features Mean  SD Mean  SD p-Values Table 2. ADC histogram features and Ki67 immunolabelling in LGG vs. HGG. Significant results 5 2 1 are writ ADC ten i,n b 10 old lett mm ers s. 148.70 32.04 137.60 31.88 0.1446 mean 5 2 1 53.75 25.47 39.48 28.96 0.0433 ADC , 10 mm s min Low-Grade Glioma High-Grade Glioma t-Test ADC Histogram Features 5 2 1 Mean ± SD Mean ± SD p-Values ADC , 10 mm s 260.50 59.07 286.60 59.85 0.0604 max −5 2 −1 ADCmean, ×10 5 mm s 2 1 148.70 32.04 137.60 31.88 0.1446 ADC , 10 mm s 110.00 15.45 95.59 20.88 0.0024 p10 −5 2 −1 ADCmin, ×10 mm s 53.75 25.47 39.48 28.96 0.0433 5 2 1 129.30 26.59 109.60 24.47 0.0014 ADC , 10 mm s p25 −5 2 −1 ADCmax, ×10 mm s 260.50 59.07 286.60 59.85 0.0604 5 2 1 ADC , 10 −5 mm 2 −1s 167.80 42.13 162.10 46.58 0.4528 p75 ADCp10, ×10 mm s 110.00 15.45 95.59 20.88 0.0024 −5 2 −1 5 2 1 ADCp25, ×10 mm s 129.30 26.59 109.60 24.47 0.0014 ADC , 10 mm s 185.30 47.07 186.70 50.22 0.8996 p90 −5 2 −1 ADCp75, ×10 mm s 167.80 42.13 162.10 46.58 0.4528 5 2 1 ADC , 10 mm s 148.90 36.22 132.40 35.21 0.0450 median −5 2 −1 ADCp90, ×10 mm s 185.30 47.07 186.70 50.22 0.8996 5 2 1 153.20 46.25 130.40 54.60 0.0083 ADC , 10 mm s modus −5 2 −1 ADCmedian, ×10 mm s 148.90 36.22 132.40 35.21 0.0450 5 2 1 −5 2 −1 ADC SD, 10 mm s 30.11 13.84 37.07 16.19 0.0600 ADCmodus, ×10 mm s 153.20 46.25 130.40 54.60 0.0083 −5 2 −1 ADC SD, 10 mm s 30.11 13.84 37.07 16.19 0.0600 Kurtosis 4.23 2.76 4.94 4.03 0.6381 Kurtosis 4.23 2.76 4.94 4.03 0.6381 Skewness 0.32 0.89 0.72 0.96 0.0740 Skewness 0.32 0.89 0.72 0.96 0.0740 Entropy 5.19 0.70 4.73 0.51 0.0011 Entropy 5.19 0.70 4.73 0.51 0.0011 Ki-67 4.71 2.58 25.74 17.82 <0.0001 Ki-67 4.71 2.58 25.74 17.82 <0.0001 Figure 2. (a–f) demonstrates significant ADC HA differences between LGG and HGG features (p < 0.05). Figure 2. (a–f) demonstrates significant ADC HA differences between LGG and HGG features (p < 0.05). Cancers 2022, 14, 3393 7 of 15 Comparing IDH1-mutated with IDH1-wildtype gliomas, the following features of the ADC histogram achieved statistical significance: lower percentiles (p10 and p25), modus, standard deviation, maximum, skewness and entropy. The complete results are listed in Table 3 and those with significant differences are shown in Figure 3a–g. Cancers 2022, 14, x FOR PEER REVIEW 7 of 16 Table 3. ADC histogram features in gliomas with and without IDH-1 mutation. Significant results are written in bold letters. Comparing IDH1-mutated with IDH1-wildtype gliomas, the following features of IDH-1 Mutation IDH-1 Wildtype the ADC histogram achieved statistical significance: lower percentiles (p10 and p25), mo- ADC Histogram Features p-Values Mean  SD Mean  SD dus, standard deviation, maximum, skewness and entropy. The complete results are listed 5 2 1 in Ta ADC ble 3 an,d tho 10se w mm ith si sgnificant di 144.60 fferences are 22.65 shown in Fig 138.50 ure 3a–g. 33.03 0.4523 mean 5 2 1 ADC , 10 mm s 54.88 23.27 40.41 29.25 0.0571 min Table 3. ADC histogram features in gliomas with and without IDH-1 mutation. Significant results 5 2 1 are written in bold letters. ADC , 10 mm s 244.10 51.79 288.40 60.15 0.0022 max 5 2 1 IDH-1 Mutation IDH-1 Wildtype ADC , 10 mm s 112.50 18.52 95.64 19.89 0.0017 p10 ADC Histogram Features p-Values Mean ± SD Mean ± SD 5 2 1 ADC , 10 mm s 127.80 22.62 110.00 24.00 0.0058 −5 2 −1 p25 ADCmean, ×10 mm s 144.60 22.65 138.50 33.03 0.4523 −5 2 −1 5 2 1 ADCmin, ×10 mm s 54.88 23.27 40.41 29.25 0.0571 160.80 26.43 163.40 48.61 0.7899 ADC , 10 mm s p75 −5 2 −1 ADCmax, ×10 mm s 244.10 51.79 288.40 60.15 0.0022 5 2 1 ADC , 10 mm s 175.60 28.29 188.50 52.87 0.5170 −5 2 −1 p90 ADCp10, ×10 mm s 112.50 18.52 95.64 19.89 0.0017 −5 2 −1 5 2 1 ADCp25, ×10 mm s 127.80 22.62 110.00 24.00 0.0058 ADC , 10 mm s 144.60 25.65 133.20 35.53 0.1245 median −5 2 −1 ADCp75, ×10 mm s 160.80 26.43 163.40 48.61 0.7899 5 2 1 ADC , 10 mm s 146.60 31.42 132.80 56.84 0.0320 −5 2 −1 modus ADCp90, ×10 mm s 175.60 28.29 188.50 52.87 0.5170 −5 2 −1 5 2 1 ADCmedian, ×10 mm s 144.60 25.65 133.20 35.53 0.1245 ADC SD, 10 mm s 25.78 9.16 37.70 16.19 0.0030 −5 2 −1 ADCmodus, ×10 mm s 146.60 31.42 132.80 56.84 0.0320 Kurtosis 3.82 1.61 5.02 4.14 0.7296 −5 2 −1 ADC SD, ×10 mm s 25.78 9.16 37.70 16.19 0.0030 Kur Skewness tosis 3.82 0.07 1.61 0.82 5.02 0.80 4.14 0.93 0.7296 0.0028 Skewness 0.07 0.82 0.80 0.93 0.0028 Entropy 5.42 0.58 4.70 0.54 <0.0001 Entropy 5.42 0.58 4.70 0.54 <0.0001 Figure Figure 3 3. . Bo Boxplots xplots (a(– a g– ) g de ) demonstrating monstrating statistatistically stically signifi significant cant difference diffs er of ences ADC of hist ADC ogram histogram fea- features tures between IDH-1 mutated and IDH-1 wildtype gliomas (p < 0.05). between IDH-1 mutated and IDH-1 wildtype gliomas (p < 0.05). Cancers 2022, 14, 3393 8 of 15 When comparing gliomas with a methylated vs. unmethylated MGMT promoter, no statistically significant differences were detectable, as demonstrated in Table 4. Table 4. ADC histogram features in gliomas with and without MGMT promoter methylation. MGMT Promoter MGMT Promoter ADC Histogram Features Methylation Positive Methylation Negative p-Values Mean  SD Mean  SD 5 2 1 ADC , 10 mm s 138.30 31.98 139.60 30.61 0.8605 mean 5 2 1 ADC , 10 mm s 41.96 27.30 40.21 29.64 0.8835 min 5 2 1 ADC , 10 mm s 271.80 59.61 280.70 66.78 0.4633 max 5 2 1 ADC , 10 mm s 99.01 21.24 97.10 20.17 0.3837 p10 5 2 1 ADC , 10 mm s 112.60 25.37 112.50 24.19 0.9848 p25 5 2 1 161.70 45.81 163.00 44.18 0.6467 ADC , 10 mm s p75 5 2 1 ADC , 10 mm s 184.00 49.19 187.10 48.99 0.7434 p90 5 2 1 ADC , 10 mm s 133.70 34.32 135.90 34.58 0.7767 median 5 2 1 ADC , 10 mm s 131.00 50.25 135.50 53.61 0.9329 modus 5 2 1 ADC SD, 10 mm s 34.25 15.51 36.38 16.85 0.5618 Kurtosis 3.92 2.09 4.75 3.60 0.8176 Skewness 0.59 0.71 0.55 0.70 0.8430 Entropy 4.92 0.71 4.87 0.51 0.7397 The computation of correlations between ADC histogram features and Ki67 immuno- labelling revealed significant associations (p < 0.05) for the following ADC parameters: minimum, lower percentiles (p10 and p25), median, modus, maximum and skewness. Table 5 provides all results of the correlative analysis; the strongest correlation was detected for ADCp10. All significant correlations are graphically demonstrated in Figure 4. Table 5. Correlations between ADC histogram features and Ki67 immunolabelling in all included gliomas. Significant results are in bold. ADC Histogram Features Correlation r = 0.2044 5 2 1 ADC , 10 mm s mean p = 0.0807 r = 0.3107 5 2 1 ADC , 10 mm s min p = 0.0071 r = 0.2772 5 2 1 ADC , 10 mm s max p = 0.0168 r = 0.4506 5 2 1 ADC , 10 mm s p10 p < 0.0001 r = 0.4026 5 2 1 ADC , 10 mm s p25 p = 0.0004 r = 0.1103 5 2 1 ADC , 10 mm s p75 p = 0.3493 r = 0.0207 5 2 1 ADC , 10 mm s p90 p = 0.8613 r = 0.2835 5 2 1 ADC , 10 mm s median p = 0.0144 Cancers 2022, 14, 3393 9 of 15 Table 5. Cont. ADC Histogram Features Correlation r = 0.2782 5 2 1 ADC , 10 mm s modus p = 0.0164 r = 0.2957 5 2 1 ADC SD, 10 mm s p = 0.0105 r = 0.0953 Kurtosis p = 0.4192 r = 0.3118 Skewness p = 0.0068 Cancers 2022, 14, x FOR PEER REVIEW r = 0.0974 9 of 16 Entropy p = 0.4090 Figure Figure 4. 4. (a (a –– hh ) ) di displays splays the the correlati correlations ons between between ADC ADC histogram histogram feature featur s (X-axis) es (Xand -axis) the and corr the e- corre- sponding proliferation index Ki-67 (Y-axis) as well as the respective simple linear regression (red sponding proliferation index Ki-67 (Y-axis) as well as the respective simple linear regression (red line) line) of all significant correlations. of all significant correlations. To evaluate the test performance of the different histogram parameters as possible classifiers in terms of differentiating LGG from HGG and IDH1-mutated gliomas from IDH1-wildtype gliomas, receiver operating characteristic (ROC) analysis and calculation of the corresponding AUC values was performed using those ADC HA features that achieved significance in the comparative statistics. The greatest accuracy concerning the distinction between LGG and HGG was detected for the lowest percentile ADCp10 (AUC = 0.7332, (CI: 0.6214–0.8450), p = 0.0007) and concerning the distinction between IDH1- mutated and IDH1-wildtype gliomas for entropy (AUC = 0.8040, (CI: 0.6849–0.9231), p < 0.0001). The complete results of the ROC analysis are listed in Table 6; the corresponding ROC curves are displayed in Figure 5. Cancers 2022, 14, 3393 10 of 15 To evaluate the test performance of the different histogram parameters as possible classifiers in terms of differentiating LGG from HGG and IDH1-mutated gliomas from IDH1-wildtype gliomas, receiver operating characteristic (ROC) analysis and calculation of the corresponding AUC values was performed using those ADC HA features that achieved significance in the comparative statistics. The greatest accuracy concerning the distinction between LGG and HGG was detected for the lowest percentile ADCp10 (AUC = 0.7332, (CI: 0.6214–0.8450), p = 0.0007) and concerning the distinction between IDH1-mutated and IDH1-wildtype gliomas for entropy (AUC = 0.8040, (CI: 0.6849–0.9231), p < 0.0001). The Cancers 2022, 14, x FOR PEER REVIEW 11 of 16 complete results of the ROC analysis are listed in Table 6; the corresponding ROC curves are displayed in Figure 5. Figure 5. (a–m) provides the receiver operating characteristics (ROC) curves and the corresponding Figure 5. (a–m) provides the receiver operating characteristics (ROC) curves and the corresponding AUC values of all ADC features that achieved statistical significance comparing LGG to HGG and AUC values of all ADC features that achieved statistical significance comparing LGG to HGG and IDH1-mutated to IDH1-wildtype gliomas. IDH1-mutated to IDH1-wildtype gliomas. Cancers 2022, 14, 3393 11 of 15 Table 6. ROC analysis of different ADC histogram features as possible classifiers between LGG and HGG as well as between IDH1-mutated and IDH1-wildtype gliomas. ROC Analysis LGG versus HGG ADC Histogram Features AUC Confidence Interval p-Value 5 2 1 ADC , 10 mm s 0.6391 0.5107–0.7674 0.0436 min 5 2 1 ADC , 10 mm s 0.7332 0.6214–0.8450 0.0007 p10 5 2 1 ADC , 10 mm s 0.7050 0.5868–0.8233 0.0029 p25 5 2 1 0.6380 0.5099–0.7662 0.0452 ADC , 10 mm s median 5 2 1 ADC , 10 mm s 0.6806 0.5647–0.7966 0.0088 modus Entropy 0.7026 0.5671–0.8381 0.0033 ROC Analysis IDH1-mutated versus IDH1-wildtype gliomas 5 2 1 0.7314 0.6054–0.8573 0.0026 ADC , 10 mm s max 5 2 1 ADC , 10 mm s 0.7296 0.6116–0.8476 0.0028 p10 5 2 1 ADC , 10 mm s 0.6924 0.5653–0.8195 0.0122 p25 5 2 1 ADC , 10 mm s 0.6642 0.5413–0.7872 0.0325 modus 5 2 1 ADC SD, 10 mm s 0.7241 0.6017–0.8466 0.0035 Skewness 0.7486 0.6235–0.8737 0.0012 Entropy 0.8040 0.6849–0.9231 p < 0.0001 Finally, Youden’s index was calculated to define the optimal cutoff value for differen- tiation between LGG and HGG: ADCp10 values  0.0009805 indicate HGG (sensitivity: 0.81, specificity: 0.63). Furthermore, the calculation of Youden’s index was performed to define the optimal cutoff value for differentiation between IDH wildtype and IDH1 mutant: ADC entropy values  5.488 indicate an IDH1-wildtype profile (sensitivity: 0.73, specificity: 0.97). 4. Discussion Depending on the underlying tumor grade, gliomas comprise a more or less heteroge- neous tissue microarchitecture containing areas of different mitotic activity and cellularity. This tumor heterogeneity is not represented adequately by conventional T1w and T2w im- ages. In the case of HGG, for example, it has been reported that the peritumoral edema—a region that is not characterized by leaky neoplastic vessels and thus parenchymal contrast enhancement—contains aggressive and highly proliferating tumor cell islets [26]. Espe- cially those highly proliferative areas that remain unremarkable in conventional contrast- enhanced MRI sequences are often the origin of local tumor recurrence. Therefore, presurgical identification of hot spots with increased proliferation—within and outside the morphologically identifiable tumor region—as potential targets for biopsy and open surgery planning is pivotal. The ADC histogram of a tumor reflects the characteristically altered diffusion profile of proteins inside the extracellular matrix of a neoplasm, which is strongly associated with tumor-cell proliferation and higher tumor grades in a variety of neoplasms, with lower ADC values resulting from higher cellularity and tumor grade [27–30]. In line with these studies, our results showed significantly decreased first-order ADC histogram parameters; i.e., the minimum values, the lower percentiles as well as the median and mode for HGG, underlining a shift of the ADC continuum towards lower values in association with a higher tumor grade and greater proliferative activity of the respective tumor tissues. Entropy values of the ADC, representing a second-order feature, also exhibited significant differences between LGG and HGG with decreased values in the HGG group. This is a rather unexpected result, since previously published ADC histogram studies suggest ADC Cancers 2022, 14, 3393 12 of 15 entropy as potential imaging marker for tumor heterogeneity with higher values in case of higher tumor grade [10,31,32]. Entropy as a quantity in HA describes the randomness of values, possibly reflecting the degree of ‘chaos’ in the neoplastic microarchitecture. For example, ADC entropy values were reported to be significantly greater in WHO grade II gliomas in comparison to WHO grade I gliomas [10] and exhibited significant changes after treatment of cancer cells with cytoreductive agents [33]. We hypothesize that our finding substantiates an association of ADC entropy with a set of phenotypic features being characteristic of altered tumor behavior, and not an exclusive link between ADC entropy and architectural heterogeneity. In particular context of gliomas, advanced tumor biology has been related to a phenotypic shift from a proneural to mesenchymal subtype [34], which may potentially be reflected by corresponding changes in ADC entropy. However, this hypothesis is of purely theoretical nature and requires further investigations. In line with the generally accepted concept of increased proliferation in HGG, our study revealed a significantly greater extent of Ki67 immunoreactivity in HGG than in the low-grade neoplasms as shown in Table 2 and exemplarily demonstrated in Figure 1 by the Ki67 immunolabelling sections. IDH1 and -2 mutations appear early in gliomagenesis in the majority of LGG and sec- ondary HGG [35]. As important prognostic factor, IDH mutation status is a well-established part of the basic histopathological diagnosis in case of gliomas. Tumor entities with mutated IDH genes show better prognosis and favorable individual outcomes in comparison to IDH-wildtype gliomas [36–38]. Beside this prognostic relevance, IDH mutation could fur- thermore play a role as potential therapeutical target in the future and therefore influence the glioma therapy regime [39]. In the present study, we discovered a strong, significant dif- ference (p < 0.0001) concerning the entropy values of IDH1-wildtype versus IDH1-mutated tumors, with higher values being present in the latter. These findings are in accordance with a recent imaging study on the ADC histogram analysis of LGG [10]. The entropy of the ADC histogram appears to be a resilient imaging marker reflecting IDH gene mutation status across the different glioma grades, and thus represents a promising biomarker for future studies. Additionally, the present histogram study revealed significantly higher val- ues of ADCmax, ADC SD and skewness as well as lower values of ADCp10, ADCp25 and ADCmodus in the IDH1-wildtype group, which is best explained by the fact that most of the IDH1-wildtype tumors are WHO grade IV entities, together with the abovementioned asymmetrical shift of the ADC histogram profile towards lower values and a possibly broader scattering of ADC values in general. MGMT represents an important genomic repair mechanism, which has gained im- portance for the risk stratification of HGG. Silencing its gene expression due to promoter methylation during tumor development enhances the cyto-reductive efficacy of alkylating anti-cancer drugs and has been shown to increase survival rates in the case of GBM [40]. However, the prediction of the MGMT promoter profile via ADC histograms in gliomas showed partly conflicting and ambiguous results [41–45]. Consistent with the negative results of those works, our actual study did not reveal associations of ADC histogram features with the MGMT promoter profile. Finally, a number of histogram features showed significant, inverse correlations with Ki67 immunolabelling. It is a well-established fact that tumors with a higher tumor grade are associated with increased proliferative activity, accompanied by the higher expression of Ki67 and consecutively increased cellularity, with a reduction in intercellular space and thus extracellular diffusion restriction. Our correlative statistic results suggest a direct and robust correlation between tumor proliferation and tumor ADC histogram profile with a complete shifting of the ADC curve towards lower ADC values, expressed by lower percentiles, extreme values, median and modus, as well as higher skewness. These results are consistent with previous studies investigating different neoplastic intracranial lesions like lymphoma, meningioma and low-grade glioma, which also demonstrated comparable associations of distinct ADC fractions with Ki67 immunolabelling [10,16,17]. Cancers 2022, 14, 3393 13 of 15 Our study has a number of limitations, including its retrospective design using data exclusively from 1.5T scanners with therefore lower signal-to-noise ratios and subsequently ADC maps with reduced spatial information in comparison to 3T scanners. Additionally, the ADC was derived from only two b-values (0 and 1000 s/mm ). This may result in biased ADC values, as perfusion effects influence the ADC when using b-values below 200 s/mm . This limitation could be overcome by using DWI with a multiple-b-value approach, which not only makes the calculation of the ADC more accurate, but also provides additional parameters like the pseudo-diffusion coefficient that reflects the perfusion properties of the tissue. Furthermore, related to the preliminary nature of our investigation, the familywise alpha inflation due to serial hypothesis tests in the statistical analysis was not controlled. 5. Conclusions Glioma ADC histogram profiling could aid in the differentiation of LGG and HGG, facilitate the estimation of growth kinetics and allow clinicians to draw preliminary conclu- sions about the IDH gene profile of the lesion at hand. Therefore, ADC histogram profiling as very straightforward and easily available postprocessing radiomic approach should be implemented in standard presurgical diagnostics to improve the accuracy of diagnosis as well as to help detecting possible hotspots of increased proliferation within the tumor tissue for targeted biopsy. Author Contributions: G.G. and S.S. conceived and planned the study; G.G., D.H.-R. and S.S. collected and analyzed the data; P.K.-M. was responsible for the histopathological workup; G.G. performed the statistical analysis and wrote the manuscript with support from H.H., M.S., W.H., O.G. and A.D.; S.S. supervised the project. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the medical council of Baden- Württemberg (Ethik-Kommission Landesärztekammer Baden-Württemberg, F-2017-047). Informed Consent Statement: Due to the retrospective nature of the investigation and the largely poor prognosis of the included cases, the patient data were mainly collected post-mortem and therefore the patient consent was waived. Data Availability Statement: The relevant histogram data are contained in the tables of this ar- ticle. The primary histogram data on each patient level can be obtained on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. References 1. Ostrom, Q.T.; Cioffi, G.; Gittleman, H.; Patil, N.; Waite, K.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012–2016. Neuro Oncol. 2019, 21 (Suppl. S5), v1–v100. [CrossRef] [PubMed] 2. Stupp, R.; Mason, W.P.; van den Bent, M.J.; Weller, M.; Fisher, B.; Taphoorn, M.J.; Belanger, K.; Brandes, A.A.; Marosi, C.; Bogdahn, U.; et al. European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 2005, 352, 987–996. [CrossRef] [PubMed] 3. Fernandez, C.; Figarella-Branger, D.; Girard, N.; Bouvier-Labit, C.; Gouvernet, J.; Paz Paredes, A.; Lena, G. Pilocytic astrocytomas in children: Prognostic factors—a retrospective study of 80 cases. Neurosurgery 2003, 53, 544–553. [CrossRef] [PubMed] 4. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [CrossRef] 5. Wesseling, P.; Capper, D. WHO 2016 Classification of gliomas. Neuropathol. Appl. Neurobiol. 2018, 44, 139–150. [CrossRef] 6. Mori, S.; Barker, P.B. Diffusion magnetic resonance imaging: Its principle and applications. Anat Rec. 1999, 257, 102–109. [CrossRef] Cancers 2022, 14, 3393 14 of 15 7. Le Bihan, D. Apparent diffusion coefficient and beyond: What diffusion MR imaging can tell us about tissue structure. Radiology 2013, 268, 318–322. [CrossRef] 8. Charles-Edwards, E.M.; deSouza, N.M. Diffusion-weighted magnetic resonance imaging and its application to cancer. Cancer Imaging 2006, 6, 135–143. [CrossRef] 9. Yin, Y.; Tong, D.; Liu, X.Y.; Yuan, T.T.; Yan, Y.Z.; Ma, Y.; Zhao, R. Correlation of apparent diffusion coefficient with Ki-67 in the diagnosis of gliomas. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2012, 34, 503–508. [CrossRef] 10. Gihr, G.A.; Horvath-Rizea, D.; Hekeler, E.; Ganslandt, O.; Henkes, H.; Hoffmann, K.T.; Scherlach, C.; Schob, S. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: In Vivo Characterization of Tumor Architecture and Corresponding Neuropathology. Front. Oncol. 2020, 10, 206. [CrossRef] 11. Zhang, L.; Min, Z.; Tang, M.; Chen, S.; Lei, X.; Zhang, X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis. J. Neurol. Sci. 2017, 373, 9–15. [CrossRef] [PubMed] 12. Cui, Y.; Ma, L.; Chen, X.; Zhang, Z.; Jiang, H.; Lin, S. Lower apparent diffusion coefficients indicate distinct prognosis in low-grade and high-grade glioma. J. Neurooncol. 2014, 119, 377–385. [CrossRef] [PubMed] 13. Horvath-Rizea, D.; Surov, A.; Hoffmann, K.T.; Garnov, N.; Vörkel, C.; Kohlhof-Meinecke, P.; Ganslandt, O.; Bäzner, H.; Gihr, G.A.; Kalman, M.; et al. The value of whole lesion ADC histogram profiling to differentiate between morphologically indistinguishable ring enhancing lesions-comparison of glioblastomas and brain abscesses. Oncotarget 2018, 9, 18148–18159. [CrossRef] 14. Just, N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer 2014, 111, 2205–2213. [CrossRef] 15. Woo, S.; Cho, J.Y.; Kim, S.Y.; Kim, S.H. Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: A preliminary correlation study with histological grade. Acta Radiol. 2014, 55, 1270–1277. [CrossRef] [PubMed] 16. Surov, A.; Gottschling, S.; Mawrin, C.; Prell, J.; Spielmann, R.P.; Wienke, A.; Fiedler, E. Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters. Transl. Oncol. 2015, 8, 517–523. [CrossRef] 17. Schob, S.; Meyer, J.; Gawlitza, M.; Frydrychowicz, C.; Müller, W.; Preuss, M.; Bure, L.; Quäschling, U.; Hoffmann, K.T.; Surov, A. Diffusion-Weighted MRI Reflects Proliferative Activity in Primary CNS Lymphoma. PLoS ONE 2016, 11, e0161386. [CrossRef] 18. Schob, S.; Meyer, H.J.; Dieckow, J.; Pervinder, B.; Pazaitis, N.; Höhn, A.K.; Garnov, N.; Horvath-Rizea, D.; Hoffmann, K.T.; Surov, A. Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer. Int. J. Mol. Sci. 2017, 18, 821. [CrossRef] 19. Schob, S.; Meyer, H.J.; Pazaitis, N.; Schramm, D.; Bremicker, K.; Exner, M.; Höhn, A.K.; Garnov, N.; Surov, A. ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study. Mol. Imaging Biol. 2017, 19, 953–962. [CrossRef] 20. Gihr, G.A.; Horvath-Rizea, D.; Garnov, N.; Kohlhof-Meinecke, P.; Ganslandt, O.; Henkes, H.; Meyer, H.J.; Hoffmann, K.T.; Surov, A.; Schob, S. Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status. Mol. Imaging Biol. 2018, 20, 632–640. [CrossRef] 21. Gihr, G.; Horvath-Rizea, D.; Hekeler, E.; Ganslandt, O.; Henkes, H.; Hoffmann, K.T.; Scherlach, C.; Schob, S. Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile. PLoS ONE 2021, 16, e0249878. [CrossRef] [PubMed] 22. Meyer, H.J.; Schob, S.; Münch, B.; Frydrychowicz, C.; Garnov, N.; Quäschling, U.; Hoffmann, K.T.; Surov, A. Histogram Analysis of T1-Weighted, T2-Weighted, and Postcontrast T1-Weighted Images in Primary CNS Lymphoma: Correlations with Histopathological Findings-a Preliminary Study. Mol. Imaging Biol. 2018, 20, 318–323. [CrossRef] [PubMed] 23. Meyer, H.J.; Leifels, L.; Hamerla, G.; Höhn, A.K.; Surov, A. Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1 , and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma. Mol. Imaging Biol. 2019, 21, 740–746. [CrossRef] 24. Meyer, H.J.; Hamerla, G.; Höhn, A.K.; Surov, A. Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer. Acad. Radiol. 2019, 26, e208–e215. [CrossRef] 25. Gihr, G.A.; Horvath-Rizea, D.; Kohlhof-Meinecke, P.; Ganslandt, O.; Henkes, H.; Richter, C.; Hoffmann, K.T.; Surov, A.; Schob, S. Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas. Transl. Oncol. 2018, 11, 957–961. [CrossRef] [PubMed] 26. Lemée, J.M.; Clavreul, A.; Menei, P. Intratumoral heterogeneity in glioblastoma: Don’t forget the peritumoral brain zone. Neuro Oncol. 2015, 17, 1322–1332. [CrossRef] [PubMed] 27. Surov, A.; Meyer, H.J.; Wienke, A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: A meta-analysis. Oncotarget 2017, 8, 59492–59499. [CrossRef] 28. Chen, L.; Liu, M.; Bao, J.; Xia, Y.; Zhang, J.; Zhang, L.; Huang, X.; Wang, J. The correlation between apparent diffusion coefficient and tumor cellularity in patients: A meta-analysis. PLoS ONE 2013, 8, e79008. [CrossRef] 29. Surov, A.; Caysa, H.; Wienke, A.; Spielmann, R.P.; Fiedler, E. Correlation Between Different ADC Fractions, Cell Count, Ki-67, Total Nucleic Areas and Average Nucleic Areas in Meningothelial Meningiomas. Anticancer. Res. 2015, 35, 6841–6846. 30. Surov, A.; Meyer, H.J.; Wienke, A. Correlation between Minimum Apparent Diffusion Coefficient (ADC ) and Tumor Cellularity: min A Meta-analysis. Anticancer Res. 2017, 37, 3807–3810. [CrossRef] Cancers 2022, 14, 3393 15 of 15 31. Rosenkrantz, A.B. Histogram-based apparent diffusion coefficient analysis: An emerging tool for cervical cancer characterization? AJR Am. J. Roentgenol. 2013, 200, 311–313. [CrossRef] 32. Suo, S.; Zhang, K.; Cao, M.; Suo, X.; Hua, J.; Geng, X.; Chen, J.; Zhuang, Z.; Ji, X.; Lu, Q.; et al. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J. Magn. Reson Imaging 2016, 43, 894–902. [CrossRef] [PubMed] 33. Foroutan, P.; Kreahling, J.M.; Morse, D.L.; Grove, O.; Lloyd, M.C.; Reed, D.; Raghavan, M.; Altiok, S.; Martinez, G.V.; Gillies, R.J. Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy. PLoS ONE 2013, 8, e82875. [CrossRef] [PubMed] 34. Fedele, M.; Cerchia, L.; Pegoraro, S.; Sgarra, R.; Manfioletti, G. Proneural-Mesenchymal Transition: Phenotypic Plasticity to Acquire Multitherapy Resistance in Glioblastoma. Int. J. Mol. Sci. 2019, 20, 2746. [CrossRef] [PubMed] 35. Cohen, A.L.; Holmen, S.L.; Colman, H. IDH1 and IDH2 mutations in gliomas. Curr. Neurol. Neurosci. Rep. 2013, 13, 345. [CrossRef] 36. Houillier, C.; Wang, X.; Kaloshi, G.; Mokhtari, K.; Guillevin, R.; Laffaire, J.; Paris, S.; Boisselier, B.; Idbaih, A.; Laigle-Donadey, F.; et al. IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 2010, 75, 1560–1566. [CrossRef] [PubMed] 37. Xia, L.; Wu, B.; Fu, Z.; Feng, F.; Qiao, E.; Li, Q.; Sun, C.; Ge, M. Prognostic role of IDH mutations in gliomas: A meta-analysis of 55 observational studies. Oncotarget 2015, 6, 17354–17365. [CrossRef] 38. Olar, A.; Wani, K.M.; Alfaro-Munoz, K.D.; Heathcock, L.E.; van Thuijl, H.F.; Gilbert, M.R.; Armstrong, T.S.; Sulman, E.P.; Cahill, D.P.; Vera-Bolanos, E.; et al. IDH mutation status and role of WHO grade and mitotic index in overall survival in grade II-III diffuse gliomas. Acta Neuropathol. 2015, 129, 585–596. [CrossRef] 39. Karpel-Massler, G.; Nguyen, T.T.T.; Shang, E.; Siegelin, M.D. Novel IDH1-Targeted Glioma Therapies. CNS Drugs. 2019, 33, 1155–1166. [CrossRef] 40. Zhao, H.; Wang, S.; Song, C.; Zha, Y.; Li, L. The prognostic value of MGMT promoter status by pyrosequencing assay for glioblastoma patients’ survival: A meta-analysis. World J. Surg. Oncol. 2016, 14, 261. [CrossRef] 41. Pope, W.B.; Lai, A.; Mehta, R.; Kim, H.J.; Qiao, J.; Young, J.R.; Xue, X.; Goldin, J.; Brown, M.S.; Nghiemphu, P.L.; et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am. J. Neuroradiol. 2011, 32, 882–889. [CrossRef] [PubMed] 42. Romano, A.; Calabria, L.F.; Tavanti, F.; Minniti, G.; Rossi-Espagnet, M.C.; Coppola, V.; Pugliese, S.; Guida, D.; Francione, G.; Colonnese, C.; et al. Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: Correlation with MGMT promoter methylation status. Eur. Radiol. 2013, 23, 513–520. [CrossRef] [PubMed] 43. Gupta, A.; Prager, A.; Young, R.J.; Shi, W.; Omuro, A.M.; Graber, J.J. Diffusion-weighted MR imaging and MGMT methylation status in glioblastoma: A reappraisal of the role of preoperative quantitative ADC measurements. AJNR Am. J. Neuroradiol. 2013, 34, E10–E11. [CrossRef] [PubMed] 44. Choi, Y.S.; Ahn, S.S.; Kim, D.W.; Chang, J.H.; Kang, S.G.; Kim, E.H.; Kim, S.H.; Rim, T.H.; Lee, S.K. Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma. Radiology 2016, 281, 175–184. [CrossRef] [PubMed] 45. Han, Y.; Yan, L.F.; Wang, X.B.; Sun, Y.Z.; Zhang, X.; Liu, Z.C.; Nan, H.Y.; Hu, Y.C.; Yang, Y.; Zhang, J.; et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: A region of interest based analysis. BMC Cancer 2018, 18, 215. [CrossRef] [PubMed] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cancers Multidisciplinary Digital Publishing Institute

Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization

Download PDF
15 pages

Loading next page...
 
/lp/multidisciplinary-digital-publishing-institute/diffusion-weighted-imaging-in-gliomas-a-histogram-based-approach-for-oT8M3sdl8C

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Multidisciplinary Digital Publishing Institute
Copyright
© 1996-2022 MDPI (Basel, Switzerland) unless otherwise stated Disclaimer The statements, opinions and data contained in the journals are solely those of the individual authors and contributors and not of the publisher and the editor(s). MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Terms and Conditions Privacy Policy
ISSN
2072-6694
DOI
10.3390/cancers14143393
Publisher site
See Article on Publisher Site

Abstract

cancers Article Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization 1 , 1 2 3 1 Georg Gihr *, Diana Horvath-Rizea , Patricia Kohlhof-Meinecke , Oliver Ganslandt , Hans Henkes , 4 5 5 5 , Wolfgang Härtig , Aneta Donitza , Martin Skalej and Stefan Schob * Katharinenhospital Stuttgart, Clinic for Neuroradiology, 70174 Stuttgart, Germany; [email protected] (D.H.-R.); [email protected] (H.H.) Katharinenhospital Stuttgart, Department for Pathology, 70174 Stuttgart, Germany; [email protected] Katharinenhospital Stuttgart, Clinic for Neurosurgery, 70174 Stuttgart, Germany; [email protected] Paul Flechsig Institute for Brain Research, University of Leipzig, 04103 Leipzig, Germany; [email protected] Department for Neuroradiology, Clinic and Policlinic for Radiology, University Hospital Halle (Saale), 06120 Halle (Saale), Germany; [email protected] (A.D.); [email protected] (M.S.) * Correspondence: [email protected] (G.G.); [email protected] (S.S.); Tel.: +49-711-2785-4454 (G.G.); +49-345-557-2342 (S.S.) Simple Summary: Glioma represent approximately one-third of all brain tumors. Although they differ clinically, histologically and genetically, they often are not distinguishable by morphological magnetic resonance imaging (MRI) diagnostics. We therefore investigated in this retrospective study whether diffusion weighted imaging (DWI) using a radiomic approach could provide complementary information with respect to tumor differentiation and cell proliferation, as well as the underlying genetic and epigenetic tumor profile. We identified several histogram features that could facilitate Citation: Gihr, G.; Horvath-Rizea, D.; Kohlhof-Meinecke, P.; Ganslandt, O.; presurgical tumor grading and potentially enable one to draw conclusions about tumor characteristics Henkes, H.; Härtig, W.; Donitza, A.; on a cellular and subcellular scale. Skalej, M.; Schob, S. Diffusion Weighted Imaging in Gliomas: A Abstract: (1) Background: Astrocytic gliomas present overlapping appearances in conventional MRI. Histogram-Based Approach for Supplementary techniques are necessary to improve preoperative diagnostics. Quantitative DWI via Tumor Characterization. Cancers the computation of apparent diffusion coefficient (ADC) histograms has proven valuable for tumor 2022, 14, 3393. https://doi.org/ characterization and prognosis in this regard. Thus, this study aimed to investigate (I) the potential of 10.3390/cancers14143393 ADC histogram analysis (HA) for distinguishing low-grade gliomas (LGG) and high-grade gliomas Academic Editor: John Virostko (HGG) and (II) whether those parameters are associated with Ki-67 immunolabelling, the isocitrate- dehydrogenase-1 (IDH1) mutation profile and the methylguanine-DNA-methyl-transferase (MGMT) Received: 21 June 2022 promoter methylation profile; (2) Methods: The ADC-histograms of 82 gliomas were computed. Accepted: 9 July 2022 Statistical analysis was performed to elucidate associations between histogram features and WHO Published: 13 July 2022 grade, Ki-67 immunolabelling, IDH1 and MGMT profile; (3) Results: Minimum, lower percentiles Publisher’s Note: MDPI stays neutral (10th and 25th), median, modus and entropy of the ADC histogram were significantly lower in with regard to jurisdictional claims in HGG. Significant differences between IDH1-mutated and IDH1-wildtype gliomas were revealed for published maps and institutional affil- maximum, lower percentiles, modus, standard deviation (SD), entropy and skewness. No differences iations. were found concerning the MGMT status. Significant correlations with Ki-67 immunolabelling were demonstrated for minimum, maximum, lower percentiles, median, modus, SD and skewness; (4) Conclusions: ADC HA facilitates non-invasive prediction of the WHO grade, tumor-proliferation Copyright: © 2022 by the authors. rate and clinically significant mutations in case of astrocytic gliomas. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: astrocytoma; glioblastoma; LGG; HGG; DWI; histogram; tumor biology; radiomics distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Cancers 2022, 14, 3393. https://doi.org/10.3390/cancers14143393 https://www.mdpi.com/journal/cancers Cancers 2022, 14, 3393 2 of 15 1. Introduction Gliomas, along with rarer tumors arising from neuroepithelial tissue, represent circa 28 percent of all brain tumors [1]. They derive from the enduring glia of the brain and are either grouped as astrocytoma or oligodendroglioma, depending on the glial cell type they originate from. Furthermore, based upon histopathologic tumor characteristics and with increasing importance related to genetic alterations, the World Health Organization (WHO) taxonomy subdivides gliomas from the lowest grade, I, to the highest grade, IV. Whereas WHO grade I and II gliomas, which usually present a rather benign tumor biology, are classified as low-grade gliomas (LGG), entities of WHO categories III and IV, which exhibit a more aggressive tumor behavior, are classified as high-grade gliomas (HGG). With about 14.6 percent of all newly diagnosed brain tumors, WHO grade IV astrocytoma (or glioblastoma, GBM) represents the most frequent entity, followed by WHO grade II astrocytoma (1.8 percent), then WHO grade III astrocytoma (1.7 percent) and WHO grade I astrocytoma (1.3%) [1]. In fact, gliomas are tumors of substantial heterogeneity, both in terms of the underlying histopathology, manifestation age and the associated clinical course. For example, WHO grade IV astrocytoma, being the most aggressive glioma, is associated with survival rates of 15 months on average [2], whereas WHO grade I (pilocytic) astrocytoma, the most frequent pediatric glioma, is associated with a rather uneventful further course [3]. Since the 2016 update of the WHO classification of CNS tumors, the diagnostic paradigm in case of gliomas shifted from a predominantly histological perspective to- wards a genetic- and molecular-based approach [4]. The classification of diffuse gliomas (WHO II-IV), in particular, now includes mutation analysis of the isocitrate dehydrogenase (IDH) 1 and 2 gene, the telomerase (TERT) promoter, the transcriptional regulator (ATRX) gene and the tumor suppressor gene TP53, as well as methylation analysis of the (MGMT) promoter and detection of 1p/19q co-deletions. Besides the prognostic value of this ge- netical tumor profile, it is essential for the final diagnosis and supersedes the histological diagnostics. For example, the diagnosis of oligodendroglioma is made only in the presence of a complete 1p/19q co-deletion, regardless of the underlying histological appearance [5]. Magnetic resonance imaging (MRI) is the most important imaging modality for gliomas. Presurgical tumor localization, treatment planning and the visualization of eloquent brain areas are the main tasks of MRI in this context, usually performed via conventional T2- and T1-weighted MRI sequences with 2D or 3D acquisition. However, through partially overlapping tumor morphology and significant interobserver variability, the diagnostic performance with those morphological MRI sequences remains limited for glioma grading, and hence, preoperative risk stratification. To overcome those limitations, advanced MRI approaches, for example MR spectroscopy, DWI and gadolinium-based perfusion techniques have been established and integrated in the majority of clinical MRI protocols. Especially DWI, which provides ADC maps that allow one to quantify the spatial extent of diffusion in vivo on a m scale [6], enables evaluation of the microscopic archi- tecture of a biological sample [7], which renders the technique an important oncological tool [8]. For example, DWI has been shown to be useful in assessing the growth potential of gliomas [9,10], in glioma grading [11], in estimating the clinical prognosis of glioma patients [12] or even in the differentiation of GBM from brain abscesses [13]. Most DWI studies conducted in the past have focused predominantly on first-order features of the ADC histogram profile, i.e., the mean, median, extreme values and percentiles. However, advanced HA includes an estimation of second-order features, i.e., skewness, entropy and kurtosis of the ADC. The latter allow an assessment of the shape of the ADC value distri- bution within the lesion and therefore facilitate assessment of tumor heterogeneity [14]. Recent studies have further substantiated the value of advanced HA for tumor grading and evaluation of individual tumor biology based on DWI [10,15–21] and even signal intensities of conventional T1- and T2-sequences [22–25]. Nevertheless, despite the recent developments in the field of advanced MRI techniques, there still are no reliable in vivo imaging biomarkers that could lead to an effective reduction Cancers 2022, 14, 3393 3 of 15 in tumor biopsies or even replace them, for example, in the case of suspected low-grade gliomas. Therefore, further imaging studies are necessary to reveal new and to evaluate already known imaging biomarkers. Considering the available data and the need for improved, non-invasive character- ization of gliomas, the aim of this study was to evaluate whether whole tumor HA of ADC profiles has the ability to (i) differentiate LGG from HGG, (ii) predict the proliferative activity of the glioma represented by Ki-67 immunolabelling and (iii) predict the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) MGMT methylation profile, as well as the IDH (isocitrate dehydrogenase) mutation profile. 2. Materials and Methods 2.1. Patients Our radiological database was reviewed for patients suffering from primary brain tumors/gliomas. A total of 114 patients who were treated in our center between January 2012 and February 2017 were included. Each diagnosis was confirmed histologically either by biopsy or open tumor surgery. The samples were further processed neuropathologically as outlined below. The patients included had sufficient pre-treatment MRI including DWI and did not exhibit signs of lesional hemorrhage or calcification. Eventually, 82 patients met those criteria. In total, 26 patients had LGG (WHO I: n = 7, WHO II: n = 19; 12 females, 14 males; average age: 34a) and 56 had HGG (WHO III: n = 11, WHO IV: n = 45; 22 females, 34 males; average age: 62a). Twenty-three percent (19/82) of the patients revealed an IDH-1 mutation and 71 percent (58/82) an IDH-1 wildtype genotype (for 5 patients no IDH-1 mutation status was available). The methylation of the MGMT promoter was positive in 39 percent (32/82); 43 percent (35/82) had tumors with an unmethylated MGMT promoter. In the remaining 15 patients, no MGMT promoter profile was available. In 9.8 percent (8/82) Ki-67 immunolabelling was not available. 2.2. MRI Protocol MRI was performed in all cases using a 1.5 T MAGNETOM scanner (Aera or Sym- phony, using a Tx/Rx CP head coil, Siemens, Erlangen, Germany). The scanning protocol consisted of axial T1-weighted (T1w) spin echo (SE) sequences (TR/TE: 453/17, flip angle: 90 , slice thickness: 5 mm, acquisition matrix: 320  179, field of view: 230  187 mm) be- fore and after the application of a gadolinium-based contrast agent (Gadobutrol, Gadovist, Bayer Schering Pharma, Leverkusen, Germany), an axial T2-weighted (T2w) turbo spin echo (TSE) sequence (TR/TE: 5390/99, flip angle: 150 , slice thickness: 5 mm, acquisition matrix: 512  291, field of view: 230  187 mm) and an axial DWI sequence (readout- segmented, multi-shot EPI sequence; TR/TE: 5500/103, flip angle 90 , slice thickness: 5 mm, acquisition matrix: 152  144, field of view: 230  230 mm). All images were digitalized and reviewed by two experienced readers (DHR, SS) who were blinded regarding the histopathological report. Image analysis was performed using a commercially available PACS workstation (Impax EE R20 XII). 2.3. Histogram Analysis of ADC Volumes T1w images, T2w images and ADC maps were anonymized and extracted from the institutional PACS as DICOM files. Subsequently, histogram analysis was performed via a custom-made, MATLAB-based analysis tool (programmed by N.G. using MATLAB, The Mathworks, Natick, MA, USA) as follows. In the case of contrast-enhancing tumors, T1w (post contrast) images, and in the case of non-enhancing tumors, T2w images, were uploaded to the visual interface displaying the anatomical image in order to correlate and tag the tumor of each patient entirely. Regions of interest (ROIs), which were manually drawn in T1w (post contrast) or T2w images along the corresponding border of the whole visible signal alteration (contrast-enhancing region or T2w hyperintense region) in every slice of detectable tumor, were propagated and co-registered to the corresponding ADC maps. Using the MATLAB library, the following features of the ADC histogram of the Cancers 2022, 14, 3393 4 of 15 whole tumor volume were computed: mean, minimum (min), maximum (max), 10th percentile (p10), 25th percentile (p25), 75th percentile (p75), 90th percentile (p90), modus, median, standard deviation (SD), skewness, kurtosis and entropy. 2.4. Immunolabelling and Polymerase Chain Reaction (PCR)—Molecular Neuropathology Testing was performed as described previously [10]: Tumor specimens were fixed in formaldehyde and embedded in paraffin for histology, immunolabelling and PCR. The embedded tissue was cut with 3m thickness and stained with hematoxylin and eosin (H&E). Immunolabelling was done employing antibodies raised against IDH1-R132H (1:20 diluted, cat. no. DIA-H09; Dianova, Hamburg, Germany) and MIB/Ki67 (1:800 diluted; cat. no. M7240; Dako Denmark A/S, Glostrup, Denmark). Histology was digitalized using a Leica microscope equipped with a DFC290 HD digital camera; LAS V4.4 was used for image processing (Leica Microsystems, Wetzlar, Germany). Necrosis and hemorrhage were absent in the investigated samples; the presence of viable tumor cells was also confirmed via microscopy. IDH1 immunolabelling resulting in strong cytoplasmic staining was interpreted as IDH1 positive. The proliferation index was calcu- lated by dividing the Ki67-immunolabelled (stained) cellular nuclei by all nuclei. The area exhibiting the greatest number of Ki67-reactive nuclei was chosen in each sample. To determine the methylation profile of the MGMT promoter, DNA from each glioma was isolated using 10 m-thick sections (derived from the paraffin-embedded samples). Extraction was performed with the Maxwell RSC FFPE Plus DNA Kit AS1720 (Promega, Madison, WI, USA). Unmethylated cytosine residues were converted to uracil by bisul- fite treatment using the EpiTect Bisulfite Kit (QIAGEN, Hilden, Germany). All steps were performed in accordance with the manufacturer ’s instructions. PCR with bisulfite- converted DNA was performed for amplification and the methylation profile was evaluated via pyrosequencing using the Therascreen MGMT Pyro Kit (QIAGEN, in accordance with the manufacturers protocol). A methylation of 10 percent or more was considered methylation positive. 2.5. Statistical Analysis Descriptive statistics, group comparison with significance testing and computation of correlations was performed with GraphPad Prism 8 (GraphPad Software, CA, USA). The Shapiro–Wilk test was performed to test for Gaussian vs. non-Gaussian distribu- tion. Group comparisons of normally distributed values was performed with Student’s T test; non-normally distributed data were tested employing the Mann–Whitney U Test. Correlations between parameters with normal distribution were computed using Pear- son’s Correlation Coefficient. For parameters exhibiting non-Gaussian distribution, the Spearman–Rho coefficient was computed. In all instances, p-values < 0.05 were interpreted as statistically significant. As a last step, aiming to assess the accuracy of the investigated histogram features, the receiver operating characteristics (ROC) curve analysis was calculated, including the area under the curve (AUC). For the estimation of suitable cutoff values, Youden’s index was calculated. 3. Results Figure 1 demonstrates the commonly used contrast-enhanced T1-weighted MRI scans of different representative glioma entities (WHO grade II and WHO grade IV), the calcu- lated ADC histogram of the respective tumor, as well as the corresponding histopathological images, consisting of HE staining and Ki-67 immunolabeling. For better comprehensibility, Table 1 summarizes the results of the descriptive statisti- cal analysis of all included cases. In brief, statistically significant differences between both entities, LGG vs. HGG, were identified for the following set of ADC features: minimum, lower percentiles (p10 and p25), median, modus and entropy (p < 0.05 in all instances). As expected, Ki-67 positive Cancers 2022, 14, 3393 5 of 15 immunolabelling was significantly stronger in HGG compared to LGG. The results of the Cancers 2022, 14, x FOR PEER REVIEW 5 of 16 comparative statistics considering LGG and HGG are listed in Table 2 and graphically demonstrated in Figure 2. Figure Figure 1.1. T1w T1wMR MRimag images es pos post t gadoli gadolinium, nium,c c orr orrespondi esponding ng ADC ADC histograms, histogram H&E s, H& images E imag and es and Ki67 Ki67 immunolabelling sections from patients with WHO grade II—(a–d) and WHO grade IV glioma (e– immunolabelling sections from patients with WHO grade II—(a–d) and WHO grade IV glioma (e–h). The upper left image shows a T1w image after intravenous application of a gadolinium-based contrast h). The upper left image shows a T1w image after intravenous application of a gadolin- agent revealing a WHO grade II glioma in the left frontal lobe, extending into the precentral gyrus. ium-based contrast agent revealing a WHO grade II glioma in the left frontal lobe, extend- The atypical morphologic appearance of this LGG with contrast medium enhancement and cystic ing into the precentral gyrus. The atypical morphologic appearance of this LGG with con- areas renders differentiation of this lesion difficult from high-grade neoplasms, based on standard trast medium enhancement and cystic areas renders differentiation of this lesion difficult MRI sequences alone (a). The lower left image depicts T1w imaging of a WHO grade IV glioma from high-grade neoplasms, based on standard MRI sequences alone (a). The lower left arising from the parafalcine region of the right frontal lobe (e). The set of images in the second image depicts T1w imaging of a WHO grade IV glioma arising from the parafalcine region column show histograms of the respective entire tumor ADC volume (b,f) The set of images in the of the right frontal lobe (e). The set of images in the second column show histograms of third column demonstrate representative H&E stainings (c,g). The images in the last column (d,h) the respective entire tumor ADC volume (b,f) The set of images in the third column demonstrate Ki67 immunolabeling of the lesions. The LGG (WHO grade II) showed a proliferation demonstrate representative H&E stainings (c,g). The images in the last column (d,h) index of 5%, the HGG had a proliferation index of 50%. demonstrate Ki67 immunolabeling of the lesions. The LGG (WHO grade II) showed a pro- liferation index of 5%, the HGG had a proliferation index of 50%. Table 1. ADC histogram features of all included gliomas. ADC Histogram Features Mean  Standard Deviation Minimum Maximum For better comprehensibility, Table 1 summarizes the results of the descriptive sta- 5 2 1 tistica ADC l analy ,s is 10 of all mm incl s uded cases. 141.11  31.97 66.56 230.91 mean 5 2 1 ADC , 10 mm s 44.00  28.36 0.1 101.30 min Table 1. ADC histogram features of all included gliomas. 5 2 1 ADC , 10 mm s 278.35  60.11 107.30 397.80 max 5 2 1 ADC Histogram Features Mean ± Standard Deviation Minimum Maximum ADC , 10 mm s 100.16  20.25 44.50 160.70 p10 −5 2 −1 ADCmean, ×10 mm s 141.11 ± 31.97 66.56 230.91 5 2 1 ADC , 10 mm s 115.86  26.49 49.80 203.60 p25 −5 2 −1 ADCmin, ×10 mm s 44.00 ± 28.36 0.1 101.30 5 2 1 163.91  44.76 75.30 274.00 ADC , 10 mm s p75 −5 2 −1 ADCmax, ×10 mm s 278.35 ± 60.11 107.30 397.80 5 2 1 −5 2 −1 ADC , 10 mm s 186.27  48.66 82.79 283.70 ADCp10, ×10 mm s 100.16 ± 20.25 44.50 160.70 p90 −5 2 −1 5 2 1 ADCp25, ×10 mm s 115.86 ± 26.49 49.80 203.60 ADC , 10 mm s 137.65  35.92 59.85 263.30 median −5 2 −1 ADCp75, ×10 mm s 163.91 ± 44.76 75.30 274.00 5 2 1 ADC , 10 mm s 137.66  52.57 46.30 277.00 modus −5 2 −1 ADCp90, ×10 mm s 186.27 ± 48.66 82.79 283.70 5 2 1 ADC SD, 10 mm s 34.87  15.64 8.77 77.46 −5 2 −1 ADCmedian, ×10 mm s 137.65 ± 35.92 59.85 263.30 Kurtosis 4.72  3.65 1.35 23.34 −5 2 −1 ADCmodus, ×10 mm s 137.66 ± 52.57 46.30 277.00 −5 2 −1 Skewness 0.60  0.95 2.07 3.80 ADC SD, 10 mm s 34.87 ± 15.64 8.77 77.46 Kurtosis 4.72 ± 3.65 1.35 23.34 Entropy 4.87  0.61 3.25 6.19 Skewness 0.60 ± 0.95 −2.07 3.80 Entropy 4.87 ± 0.61 3.25 6.19 In brief, statistically significant differences between both entities, LGG vs. HGG, were identified for the following set of ADC features: minimum, lower percentiles (p10 and p25), median, modus and entropy (p < 0.05 in all instances). As expected, Ki-67 positive immunolabelling was significantly stronger in HGG compared to LGG. The results of the Cancers 2022, 14, 3393 6 of 15 Cancers 2022, 14, x FOR PEER REVIEW 6 of 16 Table 2. ADC histogram features and Ki67 immunolabelling in LGG vs. HGG. Significant results are comparative statistics considering LGG and HGG are listed in Table 2 and graphically written in bold letters. demonstrated in Figure 2. Low-Grade Glioma High-Grade Glioma t-Test ADC Histogram Features Mean  SD Mean  SD p-Values Table 2. ADC histogram features and Ki67 immunolabelling in LGG vs. HGG. Significant results 5 2 1 are writ ADC ten i,n b 10 old lett mm ers s. 148.70 32.04 137.60 31.88 0.1446 mean 5 2 1 53.75 25.47 39.48 28.96 0.0433 ADC , 10 mm s min Low-Grade Glioma High-Grade Glioma t-Test ADC Histogram Features 5 2 1 Mean ± SD Mean ± SD p-Values ADC , 10 mm s 260.50 59.07 286.60 59.85 0.0604 max −5 2 −1 ADCmean, ×10 5 mm s 2 1 148.70 32.04 137.60 31.88 0.1446 ADC , 10 mm s 110.00 15.45 95.59 20.88 0.0024 p10 −5 2 −1 ADCmin, ×10 mm s 53.75 25.47 39.48 28.96 0.0433 5 2 1 129.30 26.59 109.60 24.47 0.0014 ADC , 10 mm s p25 −5 2 −1 ADCmax, ×10 mm s 260.50 59.07 286.60 59.85 0.0604 5 2 1 ADC , 10 −5 mm 2 −1s 167.80 42.13 162.10 46.58 0.4528 p75 ADCp10, ×10 mm s 110.00 15.45 95.59 20.88 0.0024 −5 2 −1 5 2 1 ADCp25, ×10 mm s 129.30 26.59 109.60 24.47 0.0014 ADC , 10 mm s 185.30 47.07 186.70 50.22 0.8996 p90 −5 2 −1 ADCp75, ×10 mm s 167.80 42.13 162.10 46.58 0.4528 5 2 1 ADC , 10 mm s 148.90 36.22 132.40 35.21 0.0450 median −5 2 −1 ADCp90, ×10 mm s 185.30 47.07 186.70 50.22 0.8996 5 2 1 153.20 46.25 130.40 54.60 0.0083 ADC , 10 mm s modus −5 2 −1 ADCmedian, ×10 mm s 148.90 36.22 132.40 35.21 0.0450 5 2 1 −5 2 −1 ADC SD, 10 mm s 30.11 13.84 37.07 16.19 0.0600 ADCmodus, ×10 mm s 153.20 46.25 130.40 54.60 0.0083 −5 2 −1 ADC SD, 10 mm s 30.11 13.84 37.07 16.19 0.0600 Kurtosis 4.23 2.76 4.94 4.03 0.6381 Kurtosis 4.23 2.76 4.94 4.03 0.6381 Skewness 0.32 0.89 0.72 0.96 0.0740 Skewness 0.32 0.89 0.72 0.96 0.0740 Entropy 5.19 0.70 4.73 0.51 0.0011 Entropy 5.19 0.70 4.73 0.51 0.0011 Ki-67 4.71 2.58 25.74 17.82 <0.0001 Ki-67 4.71 2.58 25.74 17.82 <0.0001 Figure 2. (a–f) demonstrates significant ADC HA differences between LGG and HGG features (p < 0.05). Figure 2. (a–f) demonstrates significant ADC HA differences between LGG and HGG features (p < 0.05). Cancers 2022, 14, 3393 7 of 15 Comparing IDH1-mutated with IDH1-wildtype gliomas, the following features of the ADC histogram achieved statistical significance: lower percentiles (p10 and p25), modus, standard deviation, maximum, skewness and entropy. The complete results are listed in Table 3 and those with significant differences are shown in Figure 3a–g. Cancers 2022, 14, x FOR PEER REVIEW 7 of 16 Table 3. ADC histogram features in gliomas with and without IDH-1 mutation. Significant results are written in bold letters. Comparing IDH1-mutated with IDH1-wildtype gliomas, the following features of IDH-1 Mutation IDH-1 Wildtype the ADC histogram achieved statistical significance: lower percentiles (p10 and p25), mo- ADC Histogram Features p-Values Mean  SD Mean  SD dus, standard deviation, maximum, skewness and entropy. The complete results are listed 5 2 1 in Ta ADC ble 3 an,d tho 10se w mm ith si sgnificant di 144.60 fferences are 22.65 shown in Fig 138.50 ure 3a–g. 33.03 0.4523 mean 5 2 1 ADC , 10 mm s 54.88 23.27 40.41 29.25 0.0571 min Table 3. ADC histogram features in gliomas with and without IDH-1 mutation. Significant results 5 2 1 are written in bold letters. ADC , 10 mm s 244.10 51.79 288.40 60.15 0.0022 max 5 2 1 IDH-1 Mutation IDH-1 Wildtype ADC , 10 mm s 112.50 18.52 95.64 19.89 0.0017 p10 ADC Histogram Features p-Values Mean ± SD Mean ± SD 5 2 1 ADC , 10 mm s 127.80 22.62 110.00 24.00 0.0058 −5 2 −1 p25 ADCmean, ×10 mm s 144.60 22.65 138.50 33.03 0.4523 −5 2 −1 5 2 1 ADCmin, ×10 mm s 54.88 23.27 40.41 29.25 0.0571 160.80 26.43 163.40 48.61 0.7899 ADC , 10 mm s p75 −5 2 −1 ADCmax, ×10 mm s 244.10 51.79 288.40 60.15 0.0022 5 2 1 ADC , 10 mm s 175.60 28.29 188.50 52.87 0.5170 −5 2 −1 p90 ADCp10, ×10 mm s 112.50 18.52 95.64 19.89 0.0017 −5 2 −1 5 2 1 ADCp25, ×10 mm s 127.80 22.62 110.00 24.00 0.0058 ADC , 10 mm s 144.60 25.65 133.20 35.53 0.1245 median −5 2 −1 ADCp75, ×10 mm s 160.80 26.43 163.40 48.61 0.7899 5 2 1 ADC , 10 mm s 146.60 31.42 132.80 56.84 0.0320 −5 2 −1 modus ADCp90, ×10 mm s 175.60 28.29 188.50 52.87 0.5170 −5 2 −1 5 2 1 ADCmedian, ×10 mm s 144.60 25.65 133.20 35.53 0.1245 ADC SD, 10 mm s 25.78 9.16 37.70 16.19 0.0030 −5 2 −1 ADCmodus, ×10 mm s 146.60 31.42 132.80 56.84 0.0320 Kurtosis 3.82 1.61 5.02 4.14 0.7296 −5 2 −1 ADC SD, ×10 mm s 25.78 9.16 37.70 16.19 0.0030 Kur Skewness tosis 3.82 0.07 1.61 0.82 5.02 0.80 4.14 0.93 0.7296 0.0028 Skewness 0.07 0.82 0.80 0.93 0.0028 Entropy 5.42 0.58 4.70 0.54 <0.0001 Entropy 5.42 0.58 4.70 0.54 <0.0001 Figure Figure 3 3. . Bo Boxplots xplots (a(– a g– ) g de ) demonstrating monstrating statistatistically stically signifi significant cant difference diffs er of ences ADC of hist ADC ogram histogram fea- features tures between IDH-1 mutated and IDH-1 wildtype gliomas (p < 0.05). between IDH-1 mutated and IDH-1 wildtype gliomas (p < 0.05). Cancers 2022, 14, 3393 8 of 15 When comparing gliomas with a methylated vs. unmethylated MGMT promoter, no statistically significant differences were detectable, as demonstrated in Table 4. Table 4. ADC histogram features in gliomas with and without MGMT promoter methylation. MGMT Promoter MGMT Promoter ADC Histogram Features Methylation Positive Methylation Negative p-Values Mean  SD Mean  SD 5 2 1 ADC , 10 mm s 138.30 31.98 139.60 30.61 0.8605 mean 5 2 1 ADC , 10 mm s 41.96 27.30 40.21 29.64 0.8835 min 5 2 1 ADC , 10 mm s 271.80 59.61 280.70 66.78 0.4633 max 5 2 1 ADC , 10 mm s 99.01 21.24 97.10 20.17 0.3837 p10 5 2 1 ADC , 10 mm s 112.60 25.37 112.50 24.19 0.9848 p25 5 2 1 161.70 45.81 163.00 44.18 0.6467 ADC , 10 mm s p75 5 2 1 ADC , 10 mm s 184.00 49.19 187.10 48.99 0.7434 p90 5 2 1 ADC , 10 mm s 133.70 34.32 135.90 34.58 0.7767 median 5 2 1 ADC , 10 mm s 131.00 50.25 135.50 53.61 0.9329 modus 5 2 1 ADC SD, 10 mm s 34.25 15.51 36.38 16.85 0.5618 Kurtosis 3.92 2.09 4.75 3.60 0.8176 Skewness 0.59 0.71 0.55 0.70 0.8430 Entropy 4.92 0.71 4.87 0.51 0.7397 The computation of correlations between ADC histogram features and Ki67 immuno- labelling revealed significant associations (p < 0.05) for the following ADC parameters: minimum, lower percentiles (p10 and p25), median, modus, maximum and skewness. Table 5 provides all results of the correlative analysis; the strongest correlation was detected for ADCp10. All significant correlations are graphically demonstrated in Figure 4. Table 5. Correlations between ADC histogram features and Ki67 immunolabelling in all included gliomas. Significant results are in bold. ADC Histogram Features Correlation r = 0.2044 5 2 1 ADC , 10 mm s mean p = 0.0807 r = 0.3107 5 2 1 ADC , 10 mm s min p = 0.0071 r = 0.2772 5 2 1 ADC , 10 mm s max p = 0.0168 r = 0.4506 5 2 1 ADC , 10 mm s p10 p < 0.0001 r = 0.4026 5 2 1 ADC , 10 mm s p25 p = 0.0004 r = 0.1103 5 2 1 ADC , 10 mm s p75 p = 0.3493 r = 0.0207 5 2 1 ADC , 10 mm s p90 p = 0.8613 r = 0.2835 5 2 1 ADC , 10 mm s median p = 0.0144 Cancers 2022, 14, 3393 9 of 15 Table 5. Cont. ADC Histogram Features Correlation r = 0.2782 5 2 1 ADC , 10 mm s modus p = 0.0164 r = 0.2957 5 2 1 ADC SD, 10 mm s p = 0.0105 r = 0.0953 Kurtosis p = 0.4192 r = 0.3118 Skewness p = 0.0068 Cancers 2022, 14, x FOR PEER REVIEW r = 0.0974 9 of 16 Entropy p = 0.4090 Figure Figure 4. 4. (a (a –– hh ) ) di displays splays the the correlati correlations ons between between ADC ADC histogram histogram feature featur s (X-axis) es (Xand -axis) the and corr the e- corre- sponding proliferation index Ki-67 (Y-axis) as well as the respective simple linear regression (red sponding proliferation index Ki-67 (Y-axis) as well as the respective simple linear regression (red line) line) of all significant correlations. of all significant correlations. To evaluate the test performance of the different histogram parameters as possible classifiers in terms of differentiating LGG from HGG and IDH1-mutated gliomas from IDH1-wildtype gliomas, receiver operating characteristic (ROC) analysis and calculation of the corresponding AUC values was performed using those ADC HA features that achieved significance in the comparative statistics. The greatest accuracy concerning the distinction between LGG and HGG was detected for the lowest percentile ADCp10 (AUC = 0.7332, (CI: 0.6214–0.8450), p = 0.0007) and concerning the distinction between IDH1- mutated and IDH1-wildtype gliomas for entropy (AUC = 0.8040, (CI: 0.6849–0.9231), p < 0.0001). The complete results of the ROC analysis are listed in Table 6; the corresponding ROC curves are displayed in Figure 5. Cancers 2022, 14, 3393 10 of 15 To evaluate the test performance of the different histogram parameters as possible classifiers in terms of differentiating LGG from HGG and IDH1-mutated gliomas from IDH1-wildtype gliomas, receiver operating characteristic (ROC) analysis and calculation of the corresponding AUC values was performed using those ADC HA features that achieved significance in the comparative statistics. The greatest accuracy concerning the distinction between LGG and HGG was detected for the lowest percentile ADCp10 (AUC = 0.7332, (CI: 0.6214–0.8450), p = 0.0007) and concerning the distinction between IDH1-mutated and IDH1-wildtype gliomas for entropy (AUC = 0.8040, (CI: 0.6849–0.9231), p < 0.0001). The Cancers 2022, 14, x FOR PEER REVIEW 11 of 16 complete results of the ROC analysis are listed in Table 6; the corresponding ROC curves are displayed in Figure 5. Figure 5. (a–m) provides the receiver operating characteristics (ROC) curves and the corresponding Figure 5. (a–m) provides the receiver operating characteristics (ROC) curves and the corresponding AUC values of all ADC features that achieved statistical significance comparing LGG to HGG and AUC values of all ADC features that achieved statistical significance comparing LGG to HGG and IDH1-mutated to IDH1-wildtype gliomas. IDH1-mutated to IDH1-wildtype gliomas. Cancers 2022, 14, 3393 11 of 15 Table 6. ROC analysis of different ADC histogram features as possible classifiers between LGG and HGG as well as between IDH1-mutated and IDH1-wildtype gliomas. ROC Analysis LGG versus HGG ADC Histogram Features AUC Confidence Interval p-Value 5 2 1 ADC , 10 mm s 0.6391 0.5107–0.7674 0.0436 min 5 2 1 ADC , 10 mm s 0.7332 0.6214–0.8450 0.0007 p10 5 2 1 ADC , 10 mm s 0.7050 0.5868–0.8233 0.0029 p25 5 2 1 0.6380 0.5099–0.7662 0.0452 ADC , 10 mm s median 5 2 1 ADC , 10 mm s 0.6806 0.5647–0.7966 0.0088 modus Entropy 0.7026 0.5671–0.8381 0.0033 ROC Analysis IDH1-mutated versus IDH1-wildtype gliomas 5 2 1 0.7314 0.6054–0.8573 0.0026 ADC , 10 mm s max 5 2 1 ADC , 10 mm s 0.7296 0.6116–0.8476 0.0028 p10 5 2 1 ADC , 10 mm s 0.6924 0.5653–0.8195 0.0122 p25 5 2 1 ADC , 10 mm s 0.6642 0.5413–0.7872 0.0325 modus 5 2 1 ADC SD, 10 mm s 0.7241 0.6017–0.8466 0.0035 Skewness 0.7486 0.6235–0.8737 0.0012 Entropy 0.8040 0.6849–0.9231 p < 0.0001 Finally, Youden’s index was calculated to define the optimal cutoff value for differen- tiation between LGG and HGG: ADCp10 values  0.0009805 indicate HGG (sensitivity: 0.81, specificity: 0.63). Furthermore, the calculation of Youden’s index was performed to define the optimal cutoff value for differentiation between IDH wildtype and IDH1 mutant: ADC entropy values  5.488 indicate an IDH1-wildtype profile (sensitivity: 0.73, specificity: 0.97). 4. Discussion Depending on the underlying tumor grade, gliomas comprise a more or less heteroge- neous tissue microarchitecture containing areas of different mitotic activity and cellularity. This tumor heterogeneity is not represented adequately by conventional T1w and T2w im- ages. In the case of HGG, for example, it has been reported that the peritumoral edema—a region that is not characterized by leaky neoplastic vessels and thus parenchymal contrast enhancement—contains aggressive and highly proliferating tumor cell islets [26]. Espe- cially those highly proliferative areas that remain unremarkable in conventional contrast- enhanced MRI sequences are often the origin of local tumor recurrence. Therefore, presurgical identification of hot spots with increased proliferation—within and outside the morphologically identifiable tumor region—as potential targets for biopsy and open surgery planning is pivotal. The ADC histogram of a tumor reflects the characteristically altered diffusion profile of proteins inside the extracellular matrix of a neoplasm, which is strongly associated with tumor-cell proliferation and higher tumor grades in a variety of neoplasms, with lower ADC values resulting from higher cellularity and tumor grade [27–30]. In line with these studies, our results showed significantly decreased first-order ADC histogram parameters; i.e., the minimum values, the lower percentiles as well as the median and mode for HGG, underlining a shift of the ADC continuum towards lower values in association with a higher tumor grade and greater proliferative activity of the respective tumor tissues. Entropy values of the ADC, representing a second-order feature, also exhibited significant differences between LGG and HGG with decreased values in the HGG group. This is a rather unexpected result, since previously published ADC histogram studies suggest ADC Cancers 2022, 14, 3393 12 of 15 entropy as potential imaging marker for tumor heterogeneity with higher values in case of higher tumor grade [10,31,32]. Entropy as a quantity in HA describes the randomness of values, possibly reflecting the degree of ‘chaos’ in the neoplastic microarchitecture. For example, ADC entropy values were reported to be significantly greater in WHO grade II gliomas in comparison to WHO grade I gliomas [10] and exhibited significant changes after treatment of cancer cells with cytoreductive agents [33]. We hypothesize that our finding substantiates an association of ADC entropy with a set of phenotypic features being characteristic of altered tumor behavior, and not an exclusive link between ADC entropy and architectural heterogeneity. In particular context of gliomas, advanced tumor biology has been related to a phenotypic shift from a proneural to mesenchymal subtype [34], which may potentially be reflected by corresponding changes in ADC entropy. However, this hypothesis is of purely theoretical nature and requires further investigations. In line with the generally accepted concept of increased proliferation in HGG, our study revealed a significantly greater extent of Ki67 immunoreactivity in HGG than in the low-grade neoplasms as shown in Table 2 and exemplarily demonstrated in Figure 1 by the Ki67 immunolabelling sections. IDH1 and -2 mutations appear early in gliomagenesis in the majority of LGG and sec- ondary HGG [35]. As important prognostic factor, IDH mutation status is a well-established part of the basic histopathological diagnosis in case of gliomas. Tumor entities with mutated IDH genes show better prognosis and favorable individual outcomes in comparison to IDH-wildtype gliomas [36–38]. Beside this prognostic relevance, IDH mutation could fur- thermore play a role as potential therapeutical target in the future and therefore influence the glioma therapy regime [39]. In the present study, we discovered a strong, significant dif- ference (p < 0.0001) concerning the entropy values of IDH1-wildtype versus IDH1-mutated tumors, with higher values being present in the latter. These findings are in accordance with a recent imaging study on the ADC histogram analysis of LGG [10]. The entropy of the ADC histogram appears to be a resilient imaging marker reflecting IDH gene mutation status across the different glioma grades, and thus represents a promising biomarker for future studies. Additionally, the present histogram study revealed significantly higher val- ues of ADCmax, ADC SD and skewness as well as lower values of ADCp10, ADCp25 and ADCmodus in the IDH1-wildtype group, which is best explained by the fact that most of the IDH1-wildtype tumors are WHO grade IV entities, together with the abovementioned asymmetrical shift of the ADC histogram profile towards lower values and a possibly broader scattering of ADC values in general. MGMT represents an important genomic repair mechanism, which has gained im- portance for the risk stratification of HGG. Silencing its gene expression due to promoter methylation during tumor development enhances the cyto-reductive efficacy of alkylating anti-cancer drugs and has been shown to increase survival rates in the case of GBM [40]. However, the prediction of the MGMT promoter profile via ADC histograms in gliomas showed partly conflicting and ambiguous results [41–45]. Consistent with the negative results of those works, our actual study did not reveal associations of ADC histogram features with the MGMT promoter profile. Finally, a number of histogram features showed significant, inverse correlations with Ki67 immunolabelling. It is a well-established fact that tumors with a higher tumor grade are associated with increased proliferative activity, accompanied by the higher expression of Ki67 and consecutively increased cellularity, with a reduction in intercellular space and thus extracellular diffusion restriction. Our correlative statistic results suggest a direct and robust correlation between tumor proliferation and tumor ADC histogram profile with a complete shifting of the ADC curve towards lower ADC values, expressed by lower percentiles, extreme values, median and modus, as well as higher skewness. These results are consistent with previous studies investigating different neoplastic intracranial lesions like lymphoma, meningioma and low-grade glioma, which also demonstrated comparable associations of distinct ADC fractions with Ki67 immunolabelling [10,16,17]. Cancers 2022, 14, 3393 13 of 15 Our study has a number of limitations, including its retrospective design using data exclusively from 1.5T scanners with therefore lower signal-to-noise ratios and subsequently ADC maps with reduced spatial information in comparison to 3T scanners. Additionally, the ADC was derived from only two b-values (0 and 1000 s/mm ). This may result in biased ADC values, as perfusion effects influence the ADC when using b-values below 200 s/mm . This limitation could be overcome by using DWI with a multiple-b-value approach, which not only makes the calculation of the ADC more accurate, but also provides additional parameters like the pseudo-diffusion coefficient that reflects the perfusion properties of the tissue. Furthermore, related to the preliminary nature of our investigation, the familywise alpha inflation due to serial hypothesis tests in the statistical analysis was not controlled. 5. Conclusions Glioma ADC histogram profiling could aid in the differentiation of LGG and HGG, facilitate the estimation of growth kinetics and allow clinicians to draw preliminary conclu- sions about the IDH gene profile of the lesion at hand. Therefore, ADC histogram profiling as very straightforward and easily available postprocessing radiomic approach should be implemented in standard presurgical diagnostics to improve the accuracy of diagnosis as well as to help detecting possible hotspots of increased proliferation within the tumor tissue for targeted biopsy. Author Contributions: G.G. and S.S. conceived and planned the study; G.G., D.H.-R. and S.S. collected and analyzed the data; P.K.-M. was responsible for the histopathological workup; G.G. performed the statistical analysis and wrote the manuscript with support from H.H., M.S., W.H., O.G. and A.D.; S.S. supervised the project. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the medical council of Baden- Württemberg (Ethik-Kommission Landesärztekammer Baden-Württemberg, F-2017-047). Informed Consent Statement: Due to the retrospective nature of the investigation and the largely poor prognosis of the included cases, the patient data were mainly collected post-mortem and therefore the patient consent was waived. Data Availability Statement: The relevant histogram data are contained in the tables of this ar- ticle. The primary histogram data on each patient level can be obtained on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. References 1. Ostrom, Q.T.; Cioffi, G.; Gittleman, H.; Patil, N.; Waite, K.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012–2016. Neuro Oncol. 2019, 21 (Suppl. S5), v1–v100. [CrossRef] [PubMed] 2. Stupp, R.; Mason, W.P.; van den Bent, M.J.; Weller, M.; Fisher, B.; Taphoorn, M.J.; Belanger, K.; Brandes, A.A.; Marosi, C.; Bogdahn, U.; et al. European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 2005, 352, 987–996. [CrossRef] [PubMed] 3. Fernandez, C.; Figarella-Branger, D.; Girard, N.; Bouvier-Labit, C.; Gouvernet, J.; Paz Paredes, A.; Lena, G. Pilocytic astrocytomas in children: Prognostic factors—a retrospective study of 80 cases. Neurosurgery 2003, 53, 544–553. [CrossRef] [PubMed] 4. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [CrossRef] 5. Wesseling, P.; Capper, D. WHO 2016 Classification of gliomas. Neuropathol. Appl. Neurobiol. 2018, 44, 139–150. [CrossRef] 6. Mori, S.; Barker, P.B. Diffusion magnetic resonance imaging: Its principle and applications. Anat Rec. 1999, 257, 102–109. [CrossRef] Cancers 2022, 14, 3393 14 of 15 7. Le Bihan, D. Apparent diffusion coefficient and beyond: What diffusion MR imaging can tell us about tissue structure. Radiology 2013, 268, 318–322. [CrossRef] 8. Charles-Edwards, E.M.; deSouza, N.M. Diffusion-weighted magnetic resonance imaging and its application to cancer. Cancer Imaging 2006, 6, 135–143. [CrossRef] 9. Yin, Y.; Tong, D.; Liu, X.Y.; Yuan, T.T.; Yan, Y.Z.; Ma, Y.; Zhao, R. Correlation of apparent diffusion coefficient with Ki-67 in the diagnosis of gliomas. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2012, 34, 503–508. [CrossRef] 10. Gihr, G.A.; Horvath-Rizea, D.; Hekeler, E.; Ganslandt, O.; Henkes, H.; Hoffmann, K.T.; Scherlach, C.; Schob, S. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: In Vivo Characterization of Tumor Architecture and Corresponding Neuropathology. Front. Oncol. 2020, 10, 206. [CrossRef] 11. Zhang, L.; Min, Z.; Tang, M.; Chen, S.; Lei, X.; Zhang, X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis. J. Neurol. Sci. 2017, 373, 9–15. [CrossRef] [PubMed] 12. Cui, Y.; Ma, L.; Chen, X.; Zhang, Z.; Jiang, H.; Lin, S. Lower apparent diffusion coefficients indicate distinct prognosis in low-grade and high-grade glioma. J. Neurooncol. 2014, 119, 377–385. [CrossRef] [PubMed] 13. Horvath-Rizea, D.; Surov, A.; Hoffmann, K.T.; Garnov, N.; Vörkel, C.; Kohlhof-Meinecke, P.; Ganslandt, O.; Bäzner, H.; Gihr, G.A.; Kalman, M.; et al. The value of whole lesion ADC histogram profiling to differentiate between morphologically indistinguishable ring enhancing lesions-comparison of glioblastomas and brain abscesses. Oncotarget 2018, 9, 18148–18159. [CrossRef] 14. Just, N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer 2014, 111, 2205–2213. [CrossRef] 15. Woo, S.; Cho, J.Y.; Kim, S.Y.; Kim, S.H. Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: A preliminary correlation study with histological grade. Acta Radiol. 2014, 55, 1270–1277. [CrossRef] [PubMed] 16. Surov, A.; Gottschling, S.; Mawrin, C.; Prell, J.; Spielmann, R.P.; Wienke, A.; Fiedler, E. Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters. Transl. Oncol. 2015, 8, 517–523. [CrossRef] 17. Schob, S.; Meyer, J.; Gawlitza, M.; Frydrychowicz, C.; Müller, W.; Preuss, M.; Bure, L.; Quäschling, U.; Hoffmann, K.T.; Surov, A. Diffusion-Weighted MRI Reflects Proliferative Activity in Primary CNS Lymphoma. PLoS ONE 2016, 11, e0161386. [CrossRef] 18. Schob, S.; Meyer, H.J.; Dieckow, J.; Pervinder, B.; Pazaitis, N.; Höhn, A.K.; Garnov, N.; Horvath-Rizea, D.; Hoffmann, K.T.; Surov, A. Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer. Int. J. Mol. Sci. 2017, 18, 821. [CrossRef] 19. Schob, S.; Meyer, H.J.; Pazaitis, N.; Schramm, D.; Bremicker, K.; Exner, M.; Höhn, A.K.; Garnov, N.; Surov, A. ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study. Mol. Imaging Biol. 2017, 19, 953–962. [CrossRef] 20. Gihr, G.A.; Horvath-Rizea, D.; Garnov, N.; Kohlhof-Meinecke, P.; Ganslandt, O.; Henkes, H.; Meyer, H.J.; Hoffmann, K.T.; Surov, A.; Schob, S. Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status. Mol. Imaging Biol. 2018, 20, 632–640. [CrossRef] 21. Gihr, G.; Horvath-Rizea, D.; Hekeler, E.; Ganslandt, O.; Henkes, H.; Hoffmann, K.T.; Scherlach, C.; Schob, S. Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile. PLoS ONE 2021, 16, e0249878. [CrossRef] [PubMed] 22. Meyer, H.J.; Schob, S.; Münch, B.; Frydrychowicz, C.; Garnov, N.; Quäschling, U.; Hoffmann, K.T.; Surov, A. Histogram Analysis of T1-Weighted, T2-Weighted, and Postcontrast T1-Weighted Images in Primary CNS Lymphoma: Correlations with Histopathological Findings-a Preliminary Study. Mol. Imaging Biol. 2018, 20, 318–323. [CrossRef] [PubMed] 23. Meyer, H.J.; Leifels, L.; Hamerla, G.; Höhn, A.K.; Surov, A. Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1 , and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma. Mol. Imaging Biol. 2019, 21, 740–746. [CrossRef] 24. Meyer, H.J.; Hamerla, G.; Höhn, A.K.; Surov, A. Whole Lesion Histogram Analysis Derived From Morphological MRI Sequences Might be Able to Predict EGFR- and Her2-Expression in Cervical Cancer. Acad. Radiol. 2019, 26, e208–e215. [CrossRef] 25. Gihr, G.A.; Horvath-Rizea, D.; Kohlhof-Meinecke, P.; Ganslandt, O.; Henkes, H.; Richter, C.; Hoffmann, K.T.; Surov, A.; Schob, S. Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas. Transl. Oncol. 2018, 11, 957–961. [CrossRef] [PubMed] 26. Lemée, J.M.; Clavreul, A.; Menei, P. Intratumoral heterogeneity in glioblastoma: Don’t forget the peritumoral brain zone. Neuro Oncol. 2015, 17, 1322–1332. [CrossRef] [PubMed] 27. Surov, A.; Meyer, H.J.; Wienke, A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: A meta-analysis. Oncotarget 2017, 8, 59492–59499. [CrossRef] 28. Chen, L.; Liu, M.; Bao, J.; Xia, Y.; Zhang, J.; Zhang, L.; Huang, X.; Wang, J. The correlation between apparent diffusion coefficient and tumor cellularity in patients: A meta-analysis. PLoS ONE 2013, 8, e79008. [CrossRef] 29. Surov, A.; Caysa, H.; Wienke, A.; Spielmann, R.P.; Fiedler, E. Correlation Between Different ADC Fractions, Cell Count, Ki-67, Total Nucleic Areas and Average Nucleic Areas in Meningothelial Meningiomas. Anticancer. Res. 2015, 35, 6841–6846. 30. Surov, A.; Meyer, H.J.; Wienke, A. Correlation between Minimum Apparent Diffusion Coefficient (ADC ) and Tumor Cellularity: min A Meta-analysis. Anticancer Res. 2017, 37, 3807–3810. [CrossRef] Cancers 2022, 14, 3393 15 of 15 31. Rosenkrantz, A.B. Histogram-based apparent diffusion coefficient analysis: An emerging tool for cervical cancer characterization? AJR Am. J. Roentgenol. 2013, 200, 311–313. [CrossRef] 32. Suo, S.; Zhang, K.; Cao, M.; Suo, X.; Hua, J.; Geng, X.; Chen, J.; Zhuang, Z.; Ji, X.; Lu, Q.; et al. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J. Magn. Reson Imaging 2016, 43, 894–902. [CrossRef] [PubMed] 33. Foroutan, P.; Kreahling, J.M.; Morse, D.L.; Grove, O.; Lloyd, M.C.; Reed, D.; Raghavan, M.; Altiok, S.; Martinez, G.V.; Gillies, R.J. Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy. PLoS ONE 2013, 8, e82875. [CrossRef] [PubMed] 34. Fedele, M.; Cerchia, L.; Pegoraro, S.; Sgarra, R.; Manfioletti, G. Proneural-Mesenchymal Transition: Phenotypic Plasticity to Acquire Multitherapy Resistance in Glioblastoma. Int. J. Mol. Sci. 2019, 20, 2746. [CrossRef] [PubMed] 35. Cohen, A.L.; Holmen, S.L.; Colman, H. IDH1 and IDH2 mutations in gliomas. Curr. Neurol. Neurosci. Rep. 2013, 13, 345. [CrossRef] 36. Houillier, C.; Wang, X.; Kaloshi, G.; Mokhtari, K.; Guillevin, R.; Laffaire, J.; Paris, S.; Boisselier, B.; Idbaih, A.; Laigle-Donadey, F.; et al. IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 2010, 75, 1560–1566. [CrossRef] [PubMed] 37. Xia, L.; Wu, B.; Fu, Z.; Feng, F.; Qiao, E.; Li, Q.; Sun, C.; Ge, M. Prognostic role of IDH mutations in gliomas: A meta-analysis of 55 observational studies. Oncotarget 2015, 6, 17354–17365. [CrossRef] 38. Olar, A.; Wani, K.M.; Alfaro-Munoz, K.D.; Heathcock, L.E.; van Thuijl, H.F.; Gilbert, M.R.; Armstrong, T.S.; Sulman, E.P.; Cahill, D.P.; Vera-Bolanos, E.; et al. IDH mutation status and role of WHO grade and mitotic index in overall survival in grade II-III diffuse gliomas. Acta Neuropathol. 2015, 129, 585–596. [CrossRef] 39. Karpel-Massler, G.; Nguyen, T.T.T.; Shang, E.; Siegelin, M.D. Novel IDH1-Targeted Glioma Therapies. CNS Drugs. 2019, 33, 1155–1166. [CrossRef] 40. Zhao, H.; Wang, S.; Song, C.; Zha, Y.; Li, L. The prognostic value of MGMT promoter status by pyrosequencing assay for glioblastoma patients’ survival: A meta-analysis. World J. Surg. Oncol. 2016, 14, 261. [CrossRef] 41. Pope, W.B.; Lai, A.; Mehta, R.; Kim, H.J.; Qiao, J.; Young, J.R.; Xue, X.; Goldin, J.; Brown, M.S.; Nghiemphu, P.L.; et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am. J. Neuroradiol. 2011, 32, 882–889. [CrossRef] [PubMed] 42. Romano, A.; Calabria, L.F.; Tavanti, F.; Minniti, G.; Rossi-Espagnet, M.C.; Coppola, V.; Pugliese, S.; Guida, D.; Francione, G.; Colonnese, C.; et al. Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: Correlation with MGMT promoter methylation status. Eur. Radiol. 2013, 23, 513–520. [CrossRef] [PubMed] 43. Gupta, A.; Prager, A.; Young, R.J.; Shi, W.; Omuro, A.M.; Graber, J.J. Diffusion-weighted MR imaging and MGMT methylation status in glioblastoma: A reappraisal of the role of preoperative quantitative ADC measurements. AJNR Am. J. Neuroradiol. 2013, 34, E10–E11. [CrossRef] [PubMed] 44. Choi, Y.S.; Ahn, S.S.; Kim, D.W.; Chang, J.H.; Kang, S.G.; Kim, E.H.; Kim, S.H.; Rim, T.H.; Lee, S.K. Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma. Radiology 2016, 281, 175–184. [CrossRef] [PubMed] 45. Han, Y.; Yan, L.F.; Wang, X.B.; Sun, Y.Z.; Zhang, X.; Liu, Z.C.; Nan, H.Y.; Hu, Y.C.; Yang, Y.; Zhang, J.; et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: A region of interest based analysis. BMC Cancer 2018, 18, 215. [CrossRef] [PubMed]

Journal

Cancers – Multidisciplinary Digital Publishing Institute

Published: Jul 13, 2022

Keywords: astrocytoma; glioblastoma; LGG; HGG; DWI; histogram; tumor biology; radiomics

There are no references for this article.