Mikkel Pedersen1, Michael Brun Andersen2, Henning Christiansen3, Nessn H Azawi4. 1. Department of People and Technology (DPT), Roskilde University Universitetsvej 1, 4000, Roskilde, Denmark. Electronic address: mikkped@ruc.dk. 2. Department of Radiology, Herlev-Gentofte University Hospital, Herlev Ringvej 75, 2700, Herlev, Denmark. Electronic address: michael.brun.andersen@regionh.dk. 3. Department of People and Technology (DPT), Roskilde University Universitetsvej 1, 4000, Roskilde, Denmark. Electronic address: henning@ruc.dk. 4. Department of Urology, Zealand University Hospital, Roskilde, Department of Clinical Medicine, Copenhagen University, Sygehusvej 10, 4000, Roskilde, Denmark. Electronic address: nesa@regionsjaelland.dk.
Abstract
PURPOSE: To investigate the ability of convolutional neural networks (CNNs) to facilitate differentiation of oncocytoma from renal cell carcinoma (RCC) using non-invasive imaging technology. METHODS: Data were collected from 369 patients between January 2015 and September 2018. True labelling of scans as benign or malignant was determined by subsequent histological findings post-surgery or ultrasound-guided percutaneous biopsy. The data included 20,000 2D CT images. Data were randomly divided into sets for training (70 %), validation (10 %) and independent testing (20 %, DataTest_1). A small dataset (DataTest_2) was used for additional validation of the training model. Data were divided into sets at the patient level, rather than by individual image. A modified version of the ResNet50V2 was used. Accuracy of detecting benign or malignant renal mass was evaluated by a 51 % majority vote of individual image classifications to determine the classification for each patient. RESULTS: Test results from DataTest_1 indicate an area under the curve (AUC) of 0.973 with 93.3 % accuracy and 93.5 % specificity. Results from DataTest_2 indicate an AUC of 0.946 with 90.0 % accuracy and 98.0 % specificity when evaluation is performed image by image. There is no case in which multiple false negative images originate from the same patient. When evaluated with 51 % majority of scans for each patient, the accuracy rises to 100 % and the incidence of false negatives falls to zero. CONCLUSION: CNNs and deep learning technology can classify renal tumour masses as oncocytoma with high accuracy. This diagnostic method could prevent overtreatment for patients with renal masses.
PURPOSE: To investigate the ability of convolutional neural networks (CNNs) to facilitate differentiation of oncocytoma from renal cell carcinoma (RCC) using non-invasive imaging technology. METHODS: Data were collected from 369 patients between January 2015 and September 2018. True labelling of scans as benign or malignant was determined by subsequent histological findings post-surgery or ultrasound-guided percutaneous biopsy. The data included 20,000 2D CT images. Data were randomly divided into sets for training (70 %), validation (10 %) and independent testing (20 %, DataTest_1). A small dataset (DataTest_2) was used for additional validation of the training model. Data were divided into sets at the patient level, rather than by individual image. A modified version of the ResNet50V2 was used. Accuracy of detecting benign or malignant renal mass was evaluated by a 51 % majority vote of individual image classifications to determine the classification for each patient. RESULTS: Test results from DataTest_1 indicate an area under the curve (AUC) of 0.973 with 93.3 % accuracy and 93.5 % specificity. Results from DataTest_2 indicate an AUC of 0.946 with 90.0 % accuracy and 98.0 % specificity when evaluation is performed image by image. There is no case in which multiple false negative images originate from the same patient. When evaluated with 51 % majority of scans for each patient, the accuracy rises to 100 % and the incidence of false negatives falls to zero. CONCLUSION: CNNs and deep learning technology can classify renal tumour masses as oncocytoma with high accuracy. This diagnostic method could prevent overtreatment for patients with renal masses.
Authors: Andrew B Chen; Taseen Haque; Sidney Roberts; Sirisha Rambhatla; Giovanni Cacciamani; Prokar Dasgupta; Andrew J Hung Journal: Urol Clin North Am Date: 2021-10-23 Impact factor: 2.766