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

Dibson D Gondim; Khaleel I Al-Obaidy; Muhammad T Idrees; John N Eble; Liang Cheng

doi:10.1016/j.jpi.2023.100299

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

J Pathol Inform. 2023 Feb 16:14:100299. doi: 10.1016/j.jpi.2023.100299. eCollection 2023.

Authors

Dibson D Gondim¹, Khaleel I Al-Obaidy², Muhammad T Idrees³, John N Eble³, Liang Cheng⁴

Affiliations

¹ Department of Pathology, University of Louisville School of Medicine, Louisville, KY 40202, USA.
² Department of Pathology and Laboratory Medicine, Henry Ford Health, 2799 West Grand Blvd, Detroit, MI 48202, USA.
³ Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
⁴ 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.

Abstract

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.

Keywords: Artificial intelligence; Digital pathology; Histopathology; Metanephric adenoma; Renal cell carcinoma; Renal oncocytoma.