BackgroundBladder cancer is the most common cancer of the urinary system among the American population and it is the fourth most common cause of cancer morbidity and the eight most common cause of cancer mortality among men. Using machine learning algorithms, we predict the five-year survival among bladder cancer patients and deploy the best performing algorithm as a web application for survival prediction.MethodsMicroscopically confirmed adult bladder cancer patients were included from the Surveillance Epidemiology and End Results (SEER) database (2000-2017) and randomly split into training and test datasets (70/30 ratio). Five machine learning algorithms (logistic regression, support vector machine, gradient boosting, random forest, and K nearest neighbor) were trained on features to predict five-year survival. The algorithms were compared with performance metrics and the best performing algorithm was deployed as a web application.ResultsA total of 52,529 patients were included in our study. The gradient boosting algorithm was the best performer in terms of predictive ability and discrimination. It was deployed as the survival prediction web application named BlaCaSurv (https://blacasurv.herokuapp.com/).ConclusionsWe tested several machine learning algorithms and developed a web application for predicting five-year survival for bladder cancer patients. This application can be used as a supplementary prognostic tool to clinical decision making.

Background: The average age of diagnosis for bladder cancer is 73 and about 75 percent of all bladder cancers are non-muscle invasive at initial diagnosis. It is recommended that non-muscle invasive bladder cancers (NMIBC) should be treated with transurethral resection of the bladder tumor (TURBT) followed by chemotherapy. However, there is no large-scale study from real-world databases to show the effectiveness of chemotherapy on the survival of older adults with NMIBC that have undergone TURBT. This study aimed to investigate the effects of chemotherapy on survival among older NMIBC patients with TURBT. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015), we performed analyses of cancer-specific mortality and overall mortality comparing chemotherapy versus no chemotherapy after TURBT. Coarsened exact matching was performed to balance the baseline patient characteristics. Cox proportional hazards and Kaplan-Meir analyses were used to evaluate survival outcomes. Results: A total of 3,222 matched patients with 1,611 in each arm (chemotherapy and no chemotherapy) were included in our study. After adjusting for covariates, multivariable Cox regression analyses show chemotherapy was associated with lower cancer-specific mortality (HR 0.63; 95% CI 0.42-0.94; p value 0.024). However, chemotherapy did not have any effect on overall mortality (HR 0.84; 95% CI 0.65-1.07; p value 0.159). The Kaplan-Meier curves show the protective effects of chemotherapy on cancer specific survival (p=0.032), but not on overall survival (p=0.34). Conclusion: Chemotherapy improved cancer specific survival among older patients with non-muscle invasive bladder cancer undergoing TURBT surgery, but it had no effect on overall survival. There is a need for more granular level real-world data on chemotherapy regimens and dosage to effectively investigate the effects of chemotherapy on survival of older patients with NMIBC that have undergone TURBT.

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