Prognostic Value of Artificial Neural Network in Predicting Bladder Cancer Recurrence After BCG Immunotherapy

Background: Artificial neural network (ANN) has been used in medicine to predict either the treatment or the investigative outcomes. The aim of this study was to validate the use of ANN models for predicting recurrence in non muscle invasive bladder cancer (NMIBC) treated by Bacillus Calmette Guerin (BCG) immunotherapy. Materials and methods: In this study, we developed a Multilayer Percepteron (MLP) based ANN to detect recurrence in NMIBC through the analysis of histopathologic data. The study includes 308 patients (mean age, 63.92 years; range, 31?92 years) who were treated with transurethral resection followed by BCG-immunotherapy. Time follow-up was 30 months. Results: In the test group, 39 out of 40 cases were correctly classified by the MLP base neural network with an optimum Mse error (0.02634). Only one case was classified as false positive, with no false negative results. The correlation between the ANN output and histopathologic results are summarized in Table 2. The sensitivity, specificity, positive predictive and negative predictive values calculated from the output data were 96.66%, 100%, 100%, and 90.9%, respectively (Table 2). Network can predict the outcome of 79% (34*100/35) of patients in the testing data set correctly. Conclusion: The proposed algorithm produced high sensitivity and specificity in predicting the recurrence in NMIBC after BCG immunotherapy compared to conventional statistical analysis. Therefore the use of ANNs will increasingly become the method of choice to calibrate complex medical models.