Estimation of bladder muscle invasion in transitional cell carcinoma by using artificial neural networks: A study based on prebiopsy imaging findings

PurposeTo identify the capability of ANNs in estimation of muscle invasive disease in transitional cell carcinomas (TCC).Materials and methodsIn this study, we developed a MLP based ANN to detect muscle invasive disease in transitional cell carcinoma of bladder through the analysis of prebiopsy imaging data. The study includes 172 patients (116 males and 56 females; mean age, 63.92 years; range, 31–92 years) who had had the definitive diagnosis of Transitional cell carcinomas (TCC) based on biopsy results.ResultsIn the test group, 34 out of 35 cases were correctly classified by he MLP based Neural Network with only one false negative case. The sensitivity, specificity, positive predictive and negative predictive values calculated from the output data were 100%, 96.1%, 90%, and 100%, respectively.ConclusionThe proposed algorithm produced high sensitivity and specifity in predicting the histopathologic results, which shows that this method has a promising value in estimation of bladder muscle invasion in TCC.