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- Title
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects.
- Authors
Verghese, Gregory; Lennerz, Jochen K; Ruta, Danny; Ng, Wen; Thavaraj, Selvam; Siziopikou, Kalliopi P; Naidoo, Threnesan; Rane, Swapnil; Salgado, Roberto; Pinder, Sarah E; Grigoriadis, Anita
- Abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
- Subjects
JOHN Wiley &; Sons Inc.; MACHINE learning; CANCER diagnosis; PATHOLOGY; DEEP learning; ARTIFICIAL intelligence
- Publication
Journal of Pathology, 2023, Vol 260, Issue 5, p551
- ISSN
0022-3417
- Publication type
Article
- DOI
10.1002/path.6163