计算病理学是一个新的跨学科领域，它将传统病理学与数字成像和机器学习等现代技术相结合，以更好地了解许多疾病的诊断、预后和自然史。概述数字和计算病理学及其当前和潜力肾细胞癌（RCC）中的应用。根据系统评价和荟萃分析指南的首选报告项目（PROSPERO），于 2022 年 12 月使用 PubMed、Web of Science 和 Scopus 数据库对英语文献进行了系统评价ID：CRD42023389282）。根据预测模型研究偏倚风险评估工具评估偏倚风险。总共纳入20篇文章。所有研究均采用回顾性设计，所有数字病理技术均回顾性实施。这些研究根据其主要目标进行分类：检测、肿瘤特征和患者结果。关于向临床实践的过渡，多项研究显示出巨大的潜力。然而，没有人对临床效用和实施进行全面评估。值得注意的是，用于模型构建的策略和报告的性能指标都存在很大的异质性。这篇综述强调了数字和计算病理学在 RCC 肿瘤结果的检测、分类和评估方面的巨大潜力。该领域的初步工作已取得了可喜的成果。然而，这些模型尚未达到可以融入常规临床实践的阶段。计算病理学将传统病理学与数字成像和人工智能等技术相结合，以改善疾病的诊断并识别预后因素和新的生物标志物。探索其治疗肾癌潜力的研究数量正在迅速增加。然而，尽管研究活动激增，计算病理学尚未准备好广泛常规使用。版权所有 © 2023 欧洲泌尿外科协会。由 Elsevier B.V. 出版。保留所有权利。

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.To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).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.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.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.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.Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.