Identification of Molecular Biomarkers Through Digital Pathology

Dear Colleagues,

Recent advances in digital pathology, artificial intelligence, and computational image analysis have created unprecedented opportunities to extract molecularly relevant information directly from routine histopathological slides. Beyond visual assessment, quantitative analysis of tissue architecture, cellular morphology, and spatial relationships can now be linked to underlying genomic, transcriptomic, proteomic, and epigenetic alterations. These developments position digital pathology as a powerful platform for molecular biomarker discovery and translational research.

This Special Issue, “Identification of Molecular Biomarkers through Digital Pathology”, aims to present state-of-the-art research that bridges histomorphology with molecular biology. We welcome original research articles and reviews that investigate how image-derived features reflect molecular pathways, cellular states, and tumor–microenvironment interactions across neoplastic and non-neoplastic diseases. Emphasis is placed on studies integrating digital pathology with multi-omics data, spatial biology approaches, and machine learning or deep learning frameworks to uncover biologically meaningful and clinically actionable biomarkers.

The scope includes basic, translational, and clinically oriented studies addressing biomarker discovery, disease stratification, prognosis, therapy response prediction, and treatment monitoring. Relevant topics encompass AI-driven image analysis, spatially resolved molecular profiling, validation of digital biomarkers in large cohorts, and methodological advances that improve reproducibility and interpretability. By aligning current research trends in molecular pathology and computational analysis, this Special Issue seeks to advance mechanistic understanding at the molecular level and to accelerate the clinical translation of digital pathology-based biomarkers.

Dr. Rafael Parra-Medina
Guest Editor

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