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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.

Chiara Frascarelli^1,2, Konstantinos Venetis¹, Antonio Marra^2,3

¹Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy.

NPJ Breast Cancer

|December 16, 2025

Summary

Small, task-oriented artificial intelligence (AI) models offer a promising alternative to large foundation models for breast cancer pathology. These models aim to predict molecular features directly from whole slide images, overcoming limitations of current AI in clinical settings.

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Area of Science:

Pathology
Artificial Intelligence
Oncology

Background:

Foundation models in AI for breast cancer pathology analyze whole slide images (WSIs) for diagnostic and prognostic insights.
Clinical adoption of these large AI models is hindered by poor workflow integration, limited explainability, and lack of generalizability.

Purpose of the Study:

To examine the potential of small, task-oriented AI models for predicting key molecular features in breast cancer directly from WSIs.
To address the limitations of foundation models by exploring techniques like model distillation and weak supervision.

Main Methods:

Focus on task-specific AI models for predicting hormone receptors (HRs), HER2, Ki-67, BRCA-related status, and somatic mutations from WSIs.
Critical evaluation of methods such as model distillation, weak supervision, and modular training to enhance AI model performance and applicability.

Main Results:

Small, task-oriented AI models show promise in predicting clinically relevant molecular features directly from breast cancer WSIs.
Techniques like model distillation and weak supervision can help overcome the constraints of large foundation models.

Conclusions:

Task-oriented AI models offer a viable path toward integrating AI into breast cancer pathology, providing explainable and clinically actionable insights.
Advancement requires high-quality datasets, multi-institutional validation, and interdisciplinary collaboration among computational scientists, clinicians, and regulators.