Deep learning in cancer pathology: a new generation of clinical biomarkersEchle, Amelie; Rindtorff, Niklas Timon; Brinker, Titus Josef; Luedde, Tom; Pearson, Alexander Thomas; Kather, Jakob Nikolas
doi: 10.1038/s41416-020-01122-xpmid: 33204028
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
Tumour treating fields therapy for glioblastoma: current advances and future directionsRominiyi, Ola; Vanderlinden, Aurelie; Clenton, Susan Jane; Bridgewater, Caroline; Al-Tamimi, Yahia; Collis, Spencer James
doi: 10.1038/s41416-020-01136-5pmid: 33144698
Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults and continues to portend poor survival, despite multimodal treatment using surgery and chemoradiotherapy. The addition of tumour-treating fields (TTFields)—an approach in which alternating electrical fields exert biophysical force on charged and polarisable molecules known as dipoles—to standard therapy, has been shown to extend survival for patients with newly diagnosed GBM, recurrent GBM and mesothelioma, leading to the clinical approval of this approach by the FDA. TTFields represent a non-invasive anticancer modality consisting of low-intensity (1–3 V/cm), intermediate-frequency (100–300 kHz), alternating electric fields delivered via cutaneous transducer arrays configured to provide optimal tumour-site coverage. Although TTFields were initially demonstrated to inhibit cancer cell proliferation by interfering with mitotic apparatus, it is becoming increasingly clear that TTFields show a broad mechanism of action by disrupting a multitude of biological processes, including DNA repair, cell permeability and immunological responses, to elicit therapeutic effects. This review describes advances in our current understanding of the mechanisms by which TTFields mediate anticancer effects. Additionally, we summarise the landscape of TTFields clinical trials across various cancers and consider how emerging preclinical data might inform future clinical applications for TTFields.
A case-control study to evaluate the impact of the breast screening programme on mortality in EnglandMaroni, Roberta; Massat, Nathalie J.; Parmar, Dharmishta; Dibden, Amanda; Cuzick, Jack; Sasieni, Peter D.; Duffy, Stephen W.
doi: 10.1038/s41416-020-01163-2pmid: 33223536
BackgroundOver the past 30 years since the implementation of the National Health Service Breast Screening Programme, improvements in diagnostic techniques and treatments have led to the need for an up-to-date evaluation of its benefit on risk of death from breast cancer. An initial pilot case-control study in London indicated that attending mammography screening led to a mortality reduction of 39%.MethodsBased on the same study protocol, an England-wide study was set up. Women aged 47–89 years who died of primary breast cancer in 2010 or 2011 were selected as cases (8288 cases). When possible, two controls were selected per case (15,202 controls) and were matched by date of birth and screening area.ResultsConditional logistic regressions showed a 38% reduction in breast cancer mortality after correcting for self-selection bias (OR 0.62, 95% CI 0.56–0.69) for women being screened at least once. Secondary analyses by age group, and time between last screen and breast cancer diagnosis were also performed.ConclusionsAccording to this England-wide case-control study, mammography screening still plays an important role in lowering the risk of dying from breast cancer. Women aged 65 or over see a stronger and longer lasting benefit of screening compared to younger women.