Context.—: 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.
Objective.—: To develop an automatic computational classifier for stratifying whole slide images of biopsy and resection specimens into 2 distinct groups: RO and LOT.
Design.—: 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).
Results.—: 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.
Conclusions.—: 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.
© 2025 College of American Pathologists.