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BackTable / Urology / Podcast / Episode #177
Latest Approaches to Treat High-Risk NMIBC
with Dr. Ashish Kamat
Dr. Ashish Kamat discusses contemporary management of high-risk, non-muscle-invasive bladder cancer (NMIBC) and his thoughts into the future of this arena.
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BackTable, LLC (Producer). (2024, July 9). Ep. 177 – Latest Approaches to Treat High-Risk NMIBC [Audio podcast]. Retrieved from https://www.backtable.com
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Podcast Contributors
Dr. Ashish Kamat
Dr. Ashish M. Kamat is a professor of Urologic Oncology and Cancer Research at M.D. Anderson Cancer Center in Houston, Texas.
Dr. Aditya Bagrodia
Dr. Aditya Bagrodia is an associate professor of urology and genitourinary oncology team leader at UC San Diego Health in California and adjunct professor of urology at UT Southwestern.
Synopsis
Dr. Kamat explores the evolving role of BCG and potential alternative therapies such as gemcitabine and docetaxel. He also covers effective clinical management, emerging clinical trials, and nuanced decision-making principles for radical cystectomy. Finally, the conversation touches on Dr. Kamat’s expert insights regarding the future of NMIBC management, including predictive biomarkers and personalized medicine.
Timestamps
00:00 - Introduction
03:56 - Initial Diagnosis and Workup
12:22 - High-Grade Bladder Cancer
22:37 - BCG and Alternative Treatments
31:30 - BCG Unresponsive Disease
36:56 - Novel Intravesical and Systemic Therapies
46:45 - Future Directions
Resources
Related BackTable episodes:
https://www.backtable.com/shows/urology/podcasts/64/management-of-bcg-refractory-nmibc
https://www.backtable.com/shows/urology/podcasts/157/the-bladder-cancer-matters-podcast
https://www.backtable.com/shows/urology/podcasts/103/adjuvant-therapy-for-high-risk-bladder-cancer
Transcript Preview
[Dr. Ashish Kamat]
It’s an exciting time to study bladder cancer. Predictive and prognostic diagnostic markers are where we need to focus our energies. There’s urinary ctDNA; we've used FISH for many years and now we are trying to couple that with response to newer agents. I think the ability to figure out which drug will work for what patient should be the next goal, because there's no clear winner. The personalized response rate is key when you offer the patient many different treatments. We have machine learning tools, AI-guided pathology, and genome sequencing. The bottom line is analyzing the data to figure out a marker that predicts the best drug for each patient.
Disclaimer: The Materials available on BackTable.com are for informational and educational purposes only and are not a substitute for the professional judgment of a healthcare professional in diagnosing and treating patients. The opinions expressed by participants of the BackTable Podcast belong solely to the participants, and do not necessarily reflect the views of BackTable.
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