Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects
- Author(s)
- Verghese, G; Lennerz, JK; Ruta, D; Ng, W; Thavaraj, S; Siziopikou, KP; Naidoo, T; Rane, S; Salgado, R; Pinder, SE; Grigoriadis, A;
- Details
- Publication Year 2023-08,Volume 260,Issue #5,Page 551-563
- Journal Title
- Journal of Pathology
- Publication Type
- Review
- 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. (c) 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
- Publisher
- Wiley
- Keywords
- Humans; *Artificial Intelligence; Algorithms; Prognosis; Pathologists; *Neoplasms/diagnosis/pathology; biomarkers; computational pathology; deep learning; digital pathology; histopathology
- Department(s)
- Laboratory Research
- PubMed ID
- 37580849
- Publisher's Version
- https://doi.org/10.1002/path.6163
- Open Access at Publisher's Site
https://doi.org/10.1002/path.6163- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2023-11-14 12:42:02
Last Modified: 2023-11-14 12:43:26