Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features.

ABSTRACT: Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI model to predict breast cancer tumor subtypes and patient survival. EXPAND focuses on a core set of 12 nuclear pathologist-interpretable features (NPIFs), composing the Nottingham grading criteria used by the pathologists. It is a fully automated, end-to-end diagnostic workflow, which automatically extracts NPIFs given a patient tumor slide and uses them to predict tumor subtype and survival. EXPAND's performance is comparable to that of existing deep learning non-interpretable black box AI models. It achieves areas unde

SUBMITTER: Barman RK 

PROVIDER: S-EPMC12466788 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature