iProstate, a mathematical predictive model-based, 3D-rendering tool to visualize the location and extent of XXXX .

@entity5 @entity1 @entity263
BACKGROUND: @entity263 is the most common non-cutaneous @entity5 in @entity1 . Radical prostatectomy (RP) is a mainstay of treatment for organ-confined @entity263 . Preoperatively, the process of planning RP is based on local stage and precisely defines the location and size of disease within the prostate. Nowadays, there is no technology that can accurately map @entity263 within the gland. Hence, urologists rely mainly on information provided by histopathological examination of biopsy cores, but this information does not accurately locate or @entity5 . The purpose of this study was to provide the surgeon with a 3D visualization tool capable of showing the location and extent of @entity5 within the prostate. METHODS: To perform this task, an application named iProstate, which makes use of four different mathematical predictive models that use biopsy cores information, was developed. These predictive models were trained with 277 clinical reports from @entity1 who had undergone radical prostatectomy. Two sets of data from the @entity1 reports were used to train the predictive models, the first containing the lengths of the biopsy cores and the @entity5 percentages of the biopsy cores information, and the second containing the lengths of the biopsy cores, @entity5 percentages of the biopsy cores, Gleason score, PSA and gland volume information. RESULTS: Multilayer Perceptron was the predictive model that scored the better results, giving a better approximation in the prediction of location and extent of @entity5 in the prostatic gland. Gleason score, PSA and gland volume proved to be important variables for the training of the predictive model. CONCLUSIONS: The application was able to perform predictions of the location and extent of @entity5 that were very close to the real location and extent of @entity5 observed in the whole mount radical prostatectomy specimens, therefore its implementation in clinical assessment and follow-up should be considered.
