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

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 int...

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Published in:Breast cancer research : BCR Vol. 27; no. 1; pp. 58 - 14
Main Authors: Kim, Jun Seo, Lee, Jeong Hoon, Yeon, Yousung, An, Doyeon, Kim, Seok Jun, Noh, Myung-Giun, Lee, Suehyun
Format: Journal Article
Language:English
Published: London BioMed Central 19.04.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Artificial intelligence
Artificial intelligence in breast imaging
Automation
Biological processes
Biomedical and Life Sciences
Biomedicine
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Breast Neoplasms - mortality
Breast Neoplasms - pathology
Cancer
Cancer Research
Cell division
Female
Gene expression
Gene expression data
Gene ontology
Humans
Multiple instance learning
Neoplasm Grading - methods
Nottingham grade
Oncology
Pathology
Prognosis
Statistical analysis
Surgical Oncology
United Kingdom
Gene ontology
Biological processes
Nottingham grade
Breast cancer
Gene expression data
Multiple instance learning
TCGA
ISSN:1465-542X, 1465-5411, 1465-542X
Online Access:Get full text
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Summary: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 Nottingham scores. The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms. Results The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. The top 300 genes correlated with model predictions were significantly enriched in pathways related to cell division and chromosome segregation, supporting the model’s biological relevance. The predicted grades demonstrated statistically significant association with 5-year overall survival ( p  < 0.05). Conclusion Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice.
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ISSN:1465-542X
1465-5411
1465-542X
DOI:10.1186/s13058-025-02019-4