AIMI Research Meeting: Enhanced imaging technologies and image-guided surgery for bladder cancer - Joseph Liao, MD & Mark Laurie
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Abstract: Bladder cancer is the sixth most common cancer in the U.S., has one of the highest recurrence rates of all cancers, and is the most expensive cancer to treat from diagnosis to death. Current standard for bladder cancer diagnosis relies on clinic-based white light cystoscopy for initial screening, followed by transurethral resection of bladder tumor in the operating room for pathologic diagnosis and local staging. White light cystoscopy has several well recognized shortcomings, particularly incomplete detection, thereby leading to suboptimal resection and contributing to cancer recurrence and progression. Our goal is to improve outcomes for bladder cancer patients through integration of a deep learning algorithm to improve cystoscopic detection and enhance surgical resection. Artificial intelligence (AI)-based on deep neural networks have demonstrated remarkable capacity to learn complex relationships and incorporate existing knowledge into the inference model. We hypothesize that AI-augmented detection of bladder tumor will improve diagnostic cystoscopy in the clinic setting to identify suspicious lesions and improve the quality of transurethral resection in the operating room, thereby reducing overall cancer recurrence and outcome. Towards the goal of establishing a paradigm of AI-based framework for augmented detection of bladder cancer, we aim to curate a high-quality annotated cystoscopy imaging dataset to optimize deep neural network CystoNet; 2) To design and optimize CystoNet for real-time cystoscopic navigation and cancer detection; and 3) To conduct a prospective multicenter validation of CystoNet during bladder cancer surgery.
About: Joseph Liao, MD is the Kathryn Simmons Stamey Professor at Stanford University. Dr. Liao is a urologic surgeon and a physician scientist with research interests in development of precision diagnostics for major urological diseases including bladder cancer, urinary tract infections, and kidney stone disease. He is interested in the development and application of enhanced optical imaging technologies including fluorescence, endomicroscopy, molecular, and most recently artificial intelligence to improve bladder cancer detection and resection. Dr. Liao earned his medical degree from Stanford and completed his urology residency and fellowship training at UCLA. He has been a faculty at Stanford since 2006 and currently serves as the vice chair for academic affairs in the Department of Urology.
About: Mark Laurie is a graduate student at Stanford studying Computational and Mathematical Engineering (CME) under the co-mentorship of Professor Liao and Professor Lei Xing from radiation oncology. He received his B.S. in Biomedical Computation at Stanford. Mark’s current work encompasses the use of state-of-art video processing models to detect bladder cancer, the creation of a benchmark cystoscopy dataset to streamline predictive model assessment, and the development of new multimodal algorithms to guide decision support system for bladder cancer screening and surveillance.
Attendance is open to the Stanford community. If you would like to attend on Zoom, please contact the AIMI Center at aimicenter@stanford.edu.