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Updated: Apr 30, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Published on: October 28, 2018
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.
Abstract:
Pathology is undergoing a shift from morpho-molecular interpretation toward the computational integration of molecular mechanisms encoded in tissue architecture. Here, we argue that such morphology-driven molecular inference may enable biomarker prediction and potentially generate therapeutic insights directly from routine histology. This paradigm has important clinical implications for quantitative biomarker testing, patient stratification, and the design of digital biomarker-based clinical trials. At the same time, we emphasize that most current artificial intelligence (AI) models remain correlative, with clinical impact still dependent on rigorous validation, integration into workflows, and ethical governance. Addressing these open challenges will be essential for computational pathology to mature into a clinically meaningful discipline.
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