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Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data
Nicola Fusco1,2, Konstantinos Venetis1
1Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Frontiers in Bioscience (Landmark Edition)
|April 29, 2026
Summary
Computational pathology integrates tissue architecture for molecular insights, enabling biomarker prediction and therapeutic discovery from histology. Rigorous validation and ethical governance are crucial for clinical translation of artificial intelligence models.
Area of Science:
- Computational pathology
- Digital pathology
- Biomarker discovery
Background:
- Pathology is shifting from morpho-molecular interpretation to computational integration of molecular mechanisms within tissue architecture.
- Morphology-driven molecular inference offers potential for biomarker prediction and therapeutic insights from routine histology.
Purpose of the Study:
- To explore the paradigm of computational pathology for biomarker prediction and therapeutic insights.
- To highlight the clinical implications for quantitative biomarker testing, patient stratification, and digital biomarker-based clinical trials.
Main Methods:
- Computational integration of molecular mechanisms encoded in tissue architecture.
- Analysis of routine histology for morphology-driven molecular inference.
Main Results:
- Morphology-driven molecular inference can enable biomarker prediction and therapeutic insights.
- This approach has significant clinical implications for biomarker testing and patient stratification.
Conclusions:
- Computational pathology holds promise for biomarker prediction and therapeutic discovery directly from histology.
- Clinical translation requires rigorous validation, workflow integration, and ethical governance of artificial intelligence models.
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