Prognostic Value of Artificial Neural Network in Predicting Bladder Cancer Recurrence After BCG Immunotherapy SciDoc Publishers | Open Access | Science Journals | Media Partners

Non-muscle-invasive bladder cancer (NMIBC) is a slow-growing tumor. These tumors recur in 48% to 70% of patients after transurethral bladder tumor resections, and 10% to 48% of these recurrent tumors carry the potential to become muscle invasive and metastatic [1-3]. Until now, intravesical bacillus Calmette- Guerin (BCG) instillations have proven to be the most successful adjuvant treatment for patients with intermediate- and high-risk non–muscle-invasive bladder cancer; however, no markers are available to predict BCG immunotherapy response. For these reasons, many studies have been conducted to develop scales to predict the recurrence and progression of these tumors [4-9]. Predictive models are used in a variety of medical domains for diagnostic and prognostic tasks. These models are built from experience, which constitutes data acquired from actual cases. The data can be preprocessed and expressed in a set of rules, such as training data for statistical and medical learning models. Among the options in the latter category, the most popular models in medicine are logistic regression (LR) and artificial neural networks (ANN). This latter model is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from clinical data. The use of ANNs has gained increasing popularity for applications where description of the dependency between dependent and independent variables is either unknown or very complex. This learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from clinical data. The application of ANNs to complex relationships makes them highly attractive for the study of complexed medical decision making. In medicine, ANN has been used to predict either the treatment or the investigative outcomes. In this study, we looked at the use of artificial neural network to predict the early recurrence of non muscle invasive bladder cancer. This will then serve as an aid in determining the most appropriate adjuvant treatment after transurethral resection (TUR) and to prevent recurrence and progression after surgical resection