Deep Learning in Cancer Genomics and Histopathology: Applications, Biases, and Future Workflows
This Genome Medicine review surveys how deep learning is transforming both histopathology and genomic analysis in oncology. The authors catalog current diagnostic and prognostic applications, from automated tumor grading and molecular subtyping to mutation prediction from H&E slides. They critically examine biases and failure modes inherent in deep learning models and propose that DL could serve as the foundation for entirely new oncology workflows, while urging awareness of pitfalls among healthcare users.
The original study
Deep learning in cancer genomics and histopathology.
- Authors
- Unger M, Kather JN
- Journal
- Genome medicine
- Type
- Journal Article, Review, Research Support, Non-U.S. Gov't
- PMID
- 38539231
Original abstract
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.