Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Nov;30(6):823-831.
doi: 10.1097/MOU.0000000000000813.

Pathomics in urology

Victor M Schuettfort  1   2 , Benjamin Pradere  1   3 , Michael Rink  2 , Eva Comperat  4 , Shahrokh F Shariat  1   5   6   7   8   9   10
Affiliations
Review

Pathomics in urology

Victor M Schuettfort et al. Curr Opin Urol. 2020 Nov.

Abstract

Purpose of review: Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology.

Recent findings: There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application.

Summary: In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.

PubMed Disclaimer

References

    1. Gupta R, Kurc T, Sharma A, et al. The emergence of Pathomics. Curr Pathobiol Rep 2019; 10.1007/s40139-019-00200-x. - DOI
    1. Louis DN, Feldman M, Carter AB, et al. Computational pathology: a path ahead. Arch Pathol Lab Med 2016; 140:41–50.
    1. Liu Y, Chen PC, Krause J, Peng L. How to read articles that use machine learning: users’ guides to the medical literature. JAMA 2019; 322:1806–1816.
    1. Faes L, Liu X, Wagner S, et al. A clinician's guide to artificial intelligence: how to critically appraise machine learning studies. Transl Vis Sci Technol 2020; 9:7.
    1. Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst 2018; 42:226.

Publication types

LinkOut - more resources