XKidneyOnco: An Explainable Framework to Classify Renal Oncocytoma and Chromophobe Renal Cell Carcinoma with a Small Sample Size

Abstract

Renal oncocytoma and chromophobe renal cell carcinoma are two kidney cancer types that present a diagnostic challenge to pathologists and other clinicians due to their microscopic similarities. While RO is a benign renal neoplasm, ChRCC is considered malignant. Therefore, the differentiation between the two is crucial. In this study, we introduce an explainable framework to accurately differentiate ChRCC from RO, histologically. Our approach examined H&E-stained images of 656 ChRCC and 720 RO, and achieved a diagnostic accuracy of 88.2%, the sensitivity of 87%, and 100% specificity for explainable AI, which either outperforms or operate on par with convolutional neural network (CNN) models. Besides, we enrolled 44 pathology experts (including pathologists and pathology trainees) to differentiate the two tumors. The average accuracy of pathologists was 73%, which is 15.2% lower than our framework. These results indicate that the combination of human expert along with explainable AI achieve higher accuracy in differentiating the two tumors, while it reduces the workload of experts and offers the desired explainability for the medical experts.