Predicting Outcomes in Intermediate and High-risk Non-muscle Invasive Bladder Cancer Using Automated Analysis of Digital Pathology Data

Summary

This is a multi-center study and the aim is to develop and validate an Artificial Intelligence (AI) -based histologic analysis tool to predict responsiveness to intravesical Bacillus Calmette-Guérin (BCG) and intravesical chemotherapy in intermediate and high-risk non-muscle invasive bladder cancer patients.

Detailed description

Analysis will be performed on the most recent Transurethral resection of bladder tumor (TURBT) histologic specimen obtained prior to BCG induction and on histologic specimens at time of recurrence after BCG induction. This stratification is of potential utility to clinicians for patient counseling purposes, for the identification of patients likely to benefit from induction or re-induction with BCG, and for consideration of alternative treatment strategies including clinical trials, chemotherapy, or cystectomy. Additionally, there is currently no reliable tool for identifying which NMIBC patients are most likely to benefit from adjuvant BCG versus intravesical chemotherapy. This is of current relevance in the management of Intermediate-risk (IR) NMIBC since both BCG and chemotherapy are first-line treatment options and will likely become of increasing relevance in High-risk (HR) NMIBC as efficacious first-line alternatives to intravesical BCG are introduced into clinical practice. In the proposed prospective study, the study team also aims to develop and then validate an AI-based histologic analysis tool for clinicians that is intended to predict recurrence following intravesical chemotherapy in IR and HR NMIBC patients.

Arms & interventions

Outcome measures

Eligibility criteria