Gondim, Dibson D.Al-Obaidy, Khaleel I.Idrees, Muhammad T.Eble, John N.Cheng, Liang2023-11-022023-11-022023-02-16Gondim DD, Al-Obaidy KI, Idrees MT, Eble JN, Cheng L. Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms. J Pathol Inform. 2023;14:100299. Published 2023 Feb 16. doi:10.1016/j.jpi.2023.100299https://hdl.handle.net/1805/36885Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalRenal cell carcinomaRenal oncocytomaMetanephric adenomaHistopathologyArtificial intelligenceDigital pathologyArticle