TY - JOUR
AU1 - Hu, TingDan
AU2 - Wang, ShengPing
AU3 - Huang, Lv
AU4 - Wang, JiaZhou
AU5 - Shi, DeBing
AU6 - Li, Yuan
AU7 - Tong, Tong
AU8 - Peng, Weijun
AB - Objectives To develop and validate a clinical-radiomics nomogram for preoperative prediction of lung metastasis for colorectal cancer (CRC) patients with indeterminate pulmonary nodules (IPN). Methods 194 CRC patients with lung nodules were enrolled in this study (136 in the training cohort and 58 in the validation cohort). To evaluate the probability of lung metastasis, we developed three models, the clinical model with significant clinical risk factors, the radiomics model with radiomics features constructed by the least absolute shrinkage and selection operator algorithm, and the clinical-radiomics model with significant variables selected by the stepwise logistic regression. The Akaike information criterion (AIC) was used to compare the relative strength of different models, and the area under the curve (AUC) was used to quantify the predictive accuracy. The nomogram was developed based on the most appropriate model. Decision-curve analysis was applied to assess the clinical usefulness. Results The clinical-radiomics model (AIC = 98.893) with the lowest AIC value compared with that of the clinical-only model (AIC = 138.502) or the radiomics-only model (AIC = 116.146) was identified as the best model. The clinical-radiomics nomogram was also successfully developed with favourable discrimination in both training cohort (AUC = 0.929, 95% CI: 0.885–0.974) and 
TI - A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules
JF - European Radiology
DO - 10.1007/s00330-018-5539-3
DA - 2018-06-12
UR - https://www.deepdyve.com/lp/springer-journals/a-clinical-radiomics-nomogram-for-the-preoperative-prediction-of-lung-irkPxLzSCp
SP - 439
EP - 449
VL - 29
IS - 1
DP - DeepDyve
ER -