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Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

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AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.

Hyun-Jong Jang¹, Sung Hak Lee²

¹Department of Physiology, CMC Institute for Basic Medical Science, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.

International Journal of Molecular Sciences

|January 10, 2026

Summary

This summary is machine-generated.

Foundation models in digital pathology enhance precision oncology by integrating diverse data for better diagnosis and treatment. These advanced AI models improve accuracy and generalization across various pathology tasks.

Keywords:

artificial intelligence deep learning digital pathology foundation model precision oncology radiology

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

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Area of Science:

Digital pathology and artificial intelligence in oncology.
Advancements in machine learning for medical applications.

Background:

Pathology is crucial for precision oncology, providing insights for personalized medicine.
Deep learning shows promise in digital pathology for diagnosis, prognosis, and biomarker prediction.

Purpose of the Study:

To review the impact of foundation models on pathology-based precision oncology.
To examine how these models integrate multimodal data for improved medical interpretation.

Main Methods:

Review of transformer-based foundation models in digital pathology.
Analysis of multimodal data integration (histopathology, radiology, clinical text, molecular data).

Main Results:

Foundation models offer scalable representation learning and improved cross-cohort robustness.
These models enable few- and zero-shot inference for diverse pathology applications.
Multimodal foundation models facilitate coherent interpretation of heterogeneous medical data.

Conclusions:

Foundation models are significantly advancing pathology-based precision oncology.
They enable more accurate diagnosis, prognostication, and therapeutic decision-making.
The integration of multimodal data by these models promises more generalizable medical AI.