Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects

Gregory Verghese, Jochen K. Lennerz, Danny Ruta, Wen Ng, Selvam Thavaraj, Kalliopi P. Siziopikou, Threnesan Naidoo, Swapnil Rane, Roberto Salgado, Sarah E. Pinder, Anita Grigoriadis*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)551-563
Number of pages13
JournalJournal of pathology
Volume260
Issue number5
DOIs
Publication statusPublished - Aug 2023

Keywords

  • biomarkers
  • computational pathology
  • deep learning
  • digital pathology
  • histopathology

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