Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.

Autor: Gondim DD; Department of Pathology, University of Louisville School of Medicine, Louisville, KY 40202, USA., Al-Obaidy KI; Department of Pathology and Laboratory Medicine, Henry Ford Health, 2799 West Grand Blvd, Detroit, MI 48202, USA., Idrees MT; Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA., Eble JN; Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA., Cheng L; Department of Pathology and Laboratory Medicine, Brown University Warren Alpert Medical School, Lifespan Academic Medical Center, and the Legorreta Cancer Center at Brown University, Providence, RI, USA.
Jazyk: angličtina
Zdroj: Journal of pathology informatics [J Pathol Inform] 2023 Feb 16; Vol. 14, pp. 100299. Date of Electronic Publication: 2023 Feb 16 (Print Publication: 2023).
DOI: 10.1016/j.jpi.2023.100299
Abstrakt: Artificial 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.
(© 2023 The Authors.)
Databáze: MEDLINE
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