Predicting Nottingham grade in breast cancer digital pathology using a foundation model.

ABSTRACT:

Background

The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to inter-observer variability.

Methods

To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. From TCGA database, we trained and evaluated using 521 H&E breast cancer slide images with available Nottingham scores through internal split validation, and further validated its clinical utility using an additional set of 597 cases without Not

SUBMITTER: Kim JS 

PROVIDER: S-EPMC12008962 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature