Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review

Zine-Eddine Khene; Solène-Florence Kammerer-Jacquet; Pierre Bigot; Noémie Rabilloud; Laurence Albiges; Vitaly Margulis; Renaud De Crevoisier; Oscar Acosta; Nathalie Rioux-Leclercq; Yair Lotan; Morgan Rouprêt; Karim Bensalah

doi:10.1016/j.euo.2023.10.018

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review

Eur Urol Oncol. 2023 Nov 2:S2588-9311(23)00234-1. doi: 10.1016/j.euo.2023.10.018. Online ahead of print.

Affiliations

¹ Department of Urology, University of Rennes, Rennes, France; Laboratoire Traitement du Signal et de l'Image, Inserm U1099, Université de Rennes 1, Rennes, France; Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA. Electronic address: zineddine.khene@gmail.com.
² Laboratoire Traitement du Signal et de l'Image, Inserm U1099, Université de Rennes 1, Rennes, France; Department of Pathology, University of Rennes, Rennes, France.
³ Department of Urology, University of Angers, Rennes, France.
⁴ Laboratoire Traitement du Signal et de l'Image, Inserm U1099, Université de Rennes 1, Rennes, France.
⁵ Department of Medical Oncology, Gustave Roussy, Villejuif, France.
⁶ Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA.
⁷ Department of Pathology, University of Rennes, Rennes, France.
⁸ Department of Urology, La Pitie Salpétrière Hospital, Paris, France.
⁹ Department of Urology, University of Rennes, Rennes, France.

PMID: 37925349
DOI: 10.1016/j.euo.2023.10.018

Abstract

Context: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases.

Objective: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).

Evidence acquisition: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.

Evidence synthesis: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported.

Conclusions: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice.

Patient summary: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Keywords: Artificial intelligence; Computational pathology; Convolutional neural network; Kidney cancer; Machine learning; Pathomics; Renal cell carcinoma; Whole-slide images.

Publication types

Review