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Establishment of Pancreatic Cancer-Derived Tumor Organoids and Fibroblasts From Fresh Tissue

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Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.

Bo Zhang¹, Victor Yu Cui², Tong Wu³

¹Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and the School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China.

Research (Washington, D.C.)

|June 29, 2026

Summary

This summary is machine-generated.

Computational pathology foundation models (CPathFMs) offer reusable representations for cancer analysis, moving beyond task-specific AI. Further research is needed to establish their clinical value and ensure broad applicability.

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Area of Science:

Digital pathology
Artificial intelligence in medicine
Machine learning for healthcare

Background:

Conventional deep learning in computational pathology requires extensive annotations and is task-specific.
Recent advancements include self-supervised learning, transformers, and large-scale pretraining.
Computational pathology foundation models (CPathFMs) learn transferable representations from diverse pathology data.

Purpose of the Study:

To review the development and applications of CPathFMs.
To identify challenges and future directions for CPathFM deployment in clinical settings.

Main Methods:

Examination of data resources, backbone architectures, representation levels, input modalities, and pretraining strategies for CPathFMs.
Analysis of CPathFM applications in cancer pathology tasks.
Discussion of limitations and ethical considerations.

Main Results:

CPathFMs leverage diverse data and architectures for transferable representation learning.
Applications span tumor detection, grading, biomarker prediction, prognosis, and report generation.
Key challenges include data representation, generalization, standardization, costs, and ethics.

Conclusions:

CPathFMs represent a shift towards reusable AI in pathology.
Clinical utility requires improved performance, generalizability, standardized evaluation, and real-world validation.