AI code could speed cancer diagnosis, treatment decisions

New York University School of Medicine researchers described in Nature Medicine a machine learning-based program that can distinguish lung cancer subtypes and at least six driving mutations using only histological slide images, in less time than could a pathologist and standard profiling techniques. The available online code, which the authors think can be applied across cancers, could help clinicians diagnose cancer and make treatment decisions faster.

The deep-learning program was trained to distinguish between lung adenocarcinoma, lung squamous cell carcinoma and non-cancerous lung tissue using open-source code from Google by analyzing hundreds of pixels within images of patient samples from The Cancer Genome Atlas (TCGA)...