An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Anil V Parwani; Ankush Patel; Ming Zhou; John C Cheville; Hamid Tizhoosh; Peter Humphrey; Victor E Reuter; Lawrence D True

doi:10.1016/j.jpi.2022.100177

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

J Pathol Inform. 2022 Dec 30:14:100177. doi: 10.1016/j.jpi.2022.100177. eCollection 2023.

Authors

Anil V Parwani¹, Ankush Patel², Ming Zhou³, John C Cheville⁴, Hamid Tizhoosh⁵, Peter Humphrey⁶, Victor E Reuter⁷, Lawrence D True⁸

Affiliations

¹ The Ohio State University, Columbus, Ohio, USA.
² The Ohio State University, 2441 60 Ave SE, Mercer Island, Washington 98040, USA.
³ Tufts University, Medford, Massachusetts, USA.
⁴ Mayo Clinic, Rochester, Minnesota, USA.
⁵ University of Waterloo, Waterloo, Ontario, Canada.
⁶ Yale University, New Haven, Connecticut, USA.
⁷ Memorial Sloan Kettering, New York, NY, USA.
⁸ University of Washington, Seattle, Washington, USA.

Abstract

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Keywords: Artificial intelligence; Computational pathology; Digital pathology; Genitourinary pathology.

Publication types

Review

Grants and funding

P30 CA008748/CA/NCI NIH HHS/United States