Abstract
ObjectivesThis study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.MethodsTraining and validation datasets were established based on radiological, clinical, and pathological data exported from the radiology, urology, and pathology departments. As the gold standard, reports were reviewed to determine the pathological subtype. Six CNN-based models were trained and validated to differentiate the two subtypes. A special test dataset generated with six new cases and four cases from The Cancer Imaging Archive (TCIA) was applied to validate the efficiency of the best model and of the manual processing by abdominal radiologists. Objective evaluation indexes [accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC)] were calculated to assess model performance.ResultsThe CT image sequences of 70 patients were segmented and validated by two experienced abdominal radiologists. The best model achieved 96.8640% accuracy (99.3794% sensitivity and 94.0271% specificity) in the validation set and 100% (case accuracy) and 93.3333% (image accuracy) in the test set. The manual classification achieved 85% accuracy (100% sensitivity and 70% specificity) in the test set.ConclusionsThis framework demonstrates that DL models could help reliably predict the subtypes of PRCC and ChRCC.
Highlights
With the continuous advancement of imaging technology and increasing awareness of the public for early cancer screening, the detection rate of renal masses is increasing [1]
This study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices
This framework demonstrates that DL models could help reliably predict the subtypes of Papillary renal cell carcinoma (PRCC) and ChRCC
Summary
With the continuous advancement of imaging technology and increasing awareness of the public for early cancer screening, the detection rate of renal masses is increasing [1]. In China, most renal masses are kidney cancer. The differentiation between subtypes of non-clear carcinoma may be difficult because of the lack of a quantitative evaluation of images, especially from the early-stage cancers, which usually present atypically [3]. According to previous reports [10], the accuracy and sensitivity of the manual classification of PRCC/ChRCC are 61.8% and 84.5%, respectively, which cannot meet this need. In the clinic, it is difficult to provide a highly accurate manual subtype differentiation between PRCC and ChRCC, and this remains to be a challenge
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