Pathology Research is an international, peer-reviewed journal dedicated to advancing the understanding of disease mechanisms through fundamental, translational, and clinical pathology research. The journal aims to provide a comprehensive platform for the dissemination of high-quality scientific work that bridges pathological science with clinical diagnosis, prognosis, and therapeutic decision-making.

The journal covers a broad spectrum of pathology-related disciplines, including surgical pathology, molecular pathology, cellular pathology, experimental pathology, and diagnostic pathology. Particular emphasis is placed on emerging and forward-looking areas such as digital pathology, computational pathology, artificial intelligence (AI)–assisted diagnostics, and data-driven pathology, which are transforming modern pathological practice and research.

Pathology Research welcomes original research articles, reviews, case-based studies, guidelines and consensus statements, and image-based contributions that elucidate the morphological, molecular, genetic, and functional alterations underlying human diseases. Studies integrating AI algorithms, machine learning, deep learning, whole-slide imaging, multi-omics analysis, spatial biology, and quantitative image analysis are especially encouraged, provided that methodological rigor and clinical or biological relevance are clearly demonstrated.

The scope of the journal encompasses both neoplastic and non-neoplastic diseases across all organ systems, including inflammatory, infectious, metabolic, genetic, degenerative, and immune-mediated disorders. Submissions that highlight innovations in disease classification, biomarker discovery, precision diagnostics, prognostic modeling, and personalized medicine are highly valued.

By fostering interdisciplinary collaboration among pathologists, clinicians, computer scientists, and biomedical researchers, Pathology Research aims to promote scientific rigor, technological innovation, and the responsible translation of advanced pathological methodologies into improved patient care and health outcomes.