Artificial Intelligence-Based Classification of Renal Oncocytic Neoplasms: Advancing From a 2-Class Model of Renal Oncocytoma and Low-Grade Oncocytic Tumor to a 3-Class Model Including Chromophobe Renal Cell Carcinoma.

Abstract

Distinguishing between renal oncocytic tumors, such as renal oncocytoma (RO), and a subset of tumors with overlapping characteristics, including the recently identified low-grade oncocytic tumor (LOT), can present a diagnostic challenge for pathologists owing to shared histopathologic features. To develop an automatic computational classifier for stratifying whole slide images of biopsy and resection specimens into 2 distinct groups: RO and LOT. A total of 269 whole slide images from 125 cases across 6 institutions were collected. A weakly supervised attention-based multiple-instance-learning deep learning (DL) model was trained and initially evaluated through 5-fold cross validation with case-level stratification, followed by validation using an independent holdout data set. Quantitative performance evaluation was based on accuracy and the area under the receiver operating characteristic curve (AUC). The developed model data set yielded generalizable performance, with a 5-fold average testing accuracy of 84% (AUC = 0.78), and a closely aligning accuracy of 83% (AUC = 0.92) on the independent holdout data set. The proposed artificial intelligence approach contributes toward a comprehensive solution for addressing commonly encountered renal oncocytic neoplasms, encompassing well-established entities like RO along with the challenging "gray zone" LOT, thereby proving applicable in clinical practice.