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[AI in Cancer Pathology-Present Developments and Future Directions for Treatment Optimization]
Maki Takao1, Daisuke Komura, Shumpei Ishikawa
1Dept. of Preventive Medicine, Graduate School of Medicine, The University of Tokyo.
Gan to Kagaku Ryoho. Cancer & Chemotherapy
|August 4, 2026
Summary
Artificial intelligence (AI) enhances pathological image analysis for predicting cancer treatment response and prognosis. Future AI models aim for multi-biomarker prediction from single images, personalizing cancer therapy.
Area of Science:
- Digital pathology
- Computational pathology
- Artificial intelligence in medicine
Background:
- Artificial intelligence (AI) is transforming pathological image analysis, moving beyond diagnostics to integrate diverse data.
- AI enables prediction of treatment response, prognosis, and workload reduction for medical professionals.
Purpose of the Study:
- To review analytical methods for pathological images.
- To summarize approaches for predicting biomarkers and therapeutic responsiveness from histological images.
Main Methods:
- Review of key studies in major cancer types and pan-cancer investigations.
- Summary of AI applications including cellular component analysis, tumor microenvironment evaluation, and integration with omics data.
Main Results:
- AI successfully predicts biomarkers and treatment responsiveness from histological images.
- Emerging frameworks include pathology foundation models and spatial transcriptomics integration.
Conclusions:
- AI in pathology offers potential for simultaneous multi-biomarker estimation, streamlining drug selection and personalizing cancer treatment.
- Challenges include data limitations, model interpretability, bias, and clinical implementation barriers.
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