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REVIEW article

Front. Oncol.
Sec. Genitourinary Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1516264

Digital pathology and artificial intelligence in Renal Cell Carcinoma focusing on feature extraction: a literature review

Provisionally accepted
Ming-Yue Li Ming-Yue Li 1Yu Pan Yu Pan 2,3Yang Lv Yang Lv 4He Ma He Ma 5PingLi Sun PingLi Sun 1*Hongwen Gao Hongwen Gao 1*
  • 1 Department of Pathology, The Second Hospital of Jilin University, Changchun, Hebei Province, China
  • 2 Department of Urology, Second Affiliated Hospital of Jilin University, Changchun, China
  • 3 the Second Hospital of Jilin University, Changchun, China
  • 4 Department of Orthopedics, The Second Hospital of Jilin University, Chanagchun, China
  • 5 Department of Anesthesiology, The Second Hospital of Jilin University, Chanagchun, China

The final, formatted version of the article will be published soon.

    The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.

    Keywords: digital pathology, artificial intelligence, deep learning, WSI, rcc, prediction, diagnosis, prognosis

    Received: 24 Oct 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Li, Pan, Lv, Ma, Sun and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    PingLi Sun, Department of Pathology, The Second Hospital of Jilin University, Changchun, Hebei Province, China
    Hongwen Gao, Department of Pathology, The Second Hospital of Jilin University, Changchun, Hebei Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.