Prediction of survival and prognostic factors in patients with bladder cancer after surgery using artificial intelligence recommendation algorithm: a preliminary study

Objective To discover the variables that affect bladder cancer (BC) patients' survival and prognosis after surgical treatment, and to use this knowledge to build an artificial intelligence (AI)-based recommendation algorithm. Methods This study comprised 832 BC patients who underwent surgery at The Second Affiliated Hospital of Dalian Medical University (2nd HDMU) and Nanfang Hospital of Southern Medical University (NHSMU) between January 2007 and January 2019. Their clinical and follow-up data were obtained. The 2nd HDMU patients were the training group, whereas NHSMU patients were the test group for external validation. An AI algorithm model was created using the deep neural network (DNN). The parameters influencing patient survival were analyzed and ranked with the assistance of AI algorithm. Results Out of the 832 bladder cancer patients included in this study, 438 (52.64%) were treated in the 2nd HDMU, while 394 (47.36%) were in the NHSMU. Among the BC cases, 579 (69.6%) were diagnostic of non-muscle invasive bladder cancer, while only 253 (30%) were muscle-invasive bladder cancer. In terms of surgical intervention, 539 (64.8%) patients underwent transurethral resection of bladder tumor, 66 (7.9%) received partial cystectomy, and 227 (27.3%) received total cystectomy. We concluded that the factors affecting the survival and prognosis of patients, in descending order, were T stage, pathological grade, hypertension or cardiovascular and cerebrovascular diseases, hemoglobin concentration, serum calcium, smoking, serum albumin level, lymphocyte count, age, serum albumin/globulin ratio, surgical method, N stage, and creatinine clearance rate. The testing group evaluated and confirmed this model to predict BC patients' survival before surgery. Conclusion Utilizing DNN modeling and external validation, the influencing factors of postoperative survival can be predicted for patients with BC. It can be employed to forecast BC patients' surgical outcomes before surgery. Additionally, this model can provide algorithmic assistance in selecting surgical and postoperative follow-up strategies for such patients.