Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment

Bladder cancer is the fifth most common malignant disease in the United States with an annual incidence of around 63,210 new cases and 13,180 deaths. The cost for providing care for patients with bladder cancer disease is high. Bladder cancer treatment options such as immunotherapy, chemotherapy, radiation therapy, transurethral resection, and cystectomy, are used with varying success rates. In this research, data from a nationwide bacillus Calmette-Gue rin (BCG) plus interferon-alpha (IFN-alpha) immunotherapy clinical trial was considered. Data mining algorithms were used to analyze the effectiveness of immunotherapy treatment and to understand the prominent parameters and their interactions. The extracted knowledge was used to build a patient recognition model for prediction of treatment outcomes. The data was analyzed to understand the impact of various parameters on the treatment outcome. A list of significant parameters such as cumulative tumor size, presence of residual disease, stages of prior bladder cancer, current state of bladder cancer, and the presence of current bladder cancer (T1) is provided. The decision-making approach outlined in the paper supplemented with additional knowledge bases will lead to a comprehensive analytical road map of the BCG/IFN-alpha immunotherapy treatment. It will provide individualized guidelines for each stage of the treatment as well as measure the success of the treatment.