Augmented Bladder Tumor Detection Using Deep Learning

Publication date: Available online 17 September 2019Source: European UrologyAuthor(s): Eugene Shkolyar, Xiao Jia, Timothy C. Chang, Dharati Trivedi, Kathleen E. Mach, Max Q.-H. Meng, Lei Xing, Joseph C. LiaoAbstractAdequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy. Deep learning augmented cystoscopy may improve tumor localization, intraoperative navigation, and surgical resection of bladder cancer. We aimed to develop a deep learning algorithm for augmented cystoscopic detection of bladder cancer. Patients undergoing cystoscopy/TURBT were recruited and white light videos were recorded. Video frames containing histologically confirmed papillary urothelial carcinoma were selected and manually annotated. We constructed CystoNet, an image analysis platform based on convolutional neural networks, for automated bladder tumor detection using a development dataset of 95 patients for algorithm training and five patients for testing. Diagnostic performance of CystoNet was validated prospectively in an additional 54 patients. In the validation dataset, per-frame sensitivity and specificity were 90.9% (95% confidence interval [CI], 90.3–91.6%) and 98.6% (95% CI, 98.5–98.8%), respectively. Per-tumor sensitivity was 90.9% (95% CI, 90.3–91.6%). CystoNet detected 39 of 41 papillary and three of three flat bladder cancers. With high s...
Source: European Urology - Category: Urology & Nephrology Source Type: research