Distante Alfredo, Marandino Laura, Bertolo Riccardo, Ingels Alexandre, Pavan Nicola, Pecoraro Angela, Marchioni Michele, Carbonara Umberto, Erdem Selcuk, Amparore Daniele, Campi Riccardo, Roussel Eduard, Caliò Anna, Wu Zhenjie, Palumbo Carlotta, Borregales Leonardo D, Mulders Peter, Muselaers Constantijn H J
Department of Urology, Catholic University of the Sacred Heart, 00168 Roma, Italy.
Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands.
Diagnostics (Basel). 2023 Jul 6;13(13):2294. doi: 10.3390/diagnostics13132294.
Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.
肾细胞癌（RCC）具有多样的组织病理学特征，这给准确诊断和预后带来了可能的挑战。我们进行了一项全面的文献综述，以探索人工智能（AI）在RCC病理学领域的最新进展。本文的目的是评估这些进展是否有望提高RCC组织病理学分析的准确性、效率和客观性，同时降低成本和观察者间的变异性，并有可能减轻病理学家的劳动和时间负担。综述的人工智能驱动方法在识别和分类与RCC相关的几种组织病理学特征方面表现出有效的能力，有助于准确诊断、分级和预后预测，并实现精确可靠的评估。然而，在肾细胞癌中应用人工智能会在标准化、通用性、基准性能以及将数据整合到临床工作流程等方面产生挑战。开发使病理学家能够准确解释人工智能决策的方法势在必行。此外，建立更强大、标准化的验证工作流程对于增强对人工智能驱动系统结果的信心至关重要。这些努力对于推动当前的先进实践和未来改善患者护理至关重要。
