Deep learning in cancer pathology: a new generation of clinical biomarkers

By A. Echle, Niklas Rindtorff, T. Brinker, Tom Luedde, A. Pearson, Jakob Nikolas Kather. British Journal of Cancer, 2020

619 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cancer Research, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d9a34cd562e936a9722c75c34cb2f13078c91cd4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cancer Research

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning (2022, methodology): “For survival outcome prediction, we validated on the IDC, CCRCC, PRCC, and LUAD cancer types which have relatively large sample sizes in the TCGA, in addition to Colon & Rectal (CRC) and Stomach Adenocarcinoma (STAD) which have been frequently evaluated in real-world clinical studies due to their su”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, methodology): “However, the use of computer-based image-analysis methods for the assessment of routine pathology slides has, until recently, mostly stayed at the level of research rather than clinical routine application 13 .”
Artificial intelligence for digital and computational pathology (2023, background): “5) and translational considerations for clinical deployment (Box 3).”