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Related Experiment Video

Updated: Jan 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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

Ranjan Kumar Barman¹, Saugato Rahman Dhruba¹, Danh-Tai Hoang¹

¹Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Biorxiv : the Preprint Server for Biology

|September 29, 2025

Summary

EXPAND is a new AI tool for digital pathology that predicts breast cancer subtypes and survival using pathologist-interpretable features. This explainable AI model achieves performance comparable to black box models, enhancing diagnostic transparency.

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Published on: June 10, 2025

Area of Science:

Digital Pathology
Artificial Intelligence
Computational Biology

Background:

Artificial intelligence (AI) offers advancements in digital pathology but often lacks human interpretability.
Existing AI models in pathology can be 'black boxes', hindering clinical trust and adoption.
Pathologist interpretability is crucial for integrating AI into routine cancer diagnostics.

Purpose of the Study:

To introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI model for breast cancer diagnostics.
To develop an automated workflow for predicting tumor subtypes and patient survival using interpretable features.
To validate the performance and prognostic value of interpretable AI in breast cancer.

Main Methods:

Development of EXPAND, an AI model focusing on 12 nuclear pathologist-interpretable features (NPIFs) from the Nottingham grading criteria.
Implementation of a fully automated, end-to-end diagnostic workflow for NPIF extraction and prediction.
Evaluation of EXPAND's performance in predicting HER2+, HR+, and TNBC tumor subtypes and patient survival.

Main Results:

EXPAND achieved AUC values of 0.73 (HER2+), 0.79 (HR+), and 0.75 (TNBC), comparable to non-interpretable AI models.
The 12 NPIFs demonstrated significant and independent prognostic value for patient survival.
EXPAND's performance matched proprietary models using more complex feature sets.

Conclusions:

EXPAND provides a pathologist-interpretable AI solution for breast cancer diagnostics, enhancing transparency.
The 12 NPIFs serve as biologically grounded, interpretable biomarkers for survival stratification.
This work paves the way for developing interpretable AI diagnostic models in other cancer types.