Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning

Abstract

Objectives To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi‐automated fashion for tumour‐to‐cortex peak early‐phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging. Methods The CNN was trained and validated to identify the kidney + tumour areas in images from 192 patients. The tumour type was differentiated through automated measurement of PEER after manual segmentation of tumours. The performance of this diagnostic model was compared with that of manual expert identification and tumour pathology with regard to accuracy, sensitivity and specificity, along with the root‐mean‐square error (RMSE), for the remaining 20 patients with CD117(+) oncocytoma or ChRCC. Results The mean ± sd Dice similarity score for segmentation was 0.66 ± 0.14 for the CNN model to identify the kidney + tumour areas. PEER evaluation achieved accuracy of 95% in tumour type classification (100% sensitivity and 89% specificity) compared with the final pathology results (RMSE of 0.15 for PEER ratio). Conclusions We have shown that deep learning could help to produce reliable discrimination of CD117(+) benign oncocytoma and malignant ChRCC through PEER measurements obtained by computer vision.

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“…Other models have incorporated MRI radiomics data with Gleason scores to train a ML model to predict clinically significant prostate cancer on MRI images with some success [50]. In renal cancer, ML models have been developed to accurately differentiate benign oncocytomas from RCC [51, 52]. Similarly, AI has been used to help in the planning of surgery by constructing three‐dimensional models of renal anatomy from CT scans.…”

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“…They found that all five classifiers performed well at differentiating between the two with AUC values over 0.85 and concluded that their approach provides valuable preoperative diagnostic accuracy [29]. Other studies used DL approaches such as convolutional neural networks (CNN) to accurately classify chRCC and oncocytoma while also achieving 100% sensitivity in comparison with final pathology results [30]. Coy et al investigated the diagnostic value and feasibility of a DL-based renal lesion classifier to differentiate ccRCC from oncocytoma in 179 patients with pathologically confirmed renal masses on routine four-phasic CECT [28].…”

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“…Based on a CNN model and CT images, Baghdadi et al. ( 25 ) identified benign renal oncocytoma and ChRCC on images with an accuracy rate of 95%. Zhou et al.…”