By Jiayun Li
Medical and Imaging Informatics Ph.D. Candidate — Dr. Corey Arnold Lab.
Prostate cancer (PCa) is the most common and second deadliest cancer in men in the United States. Active surveillance (AS) is an important option for the management of low- to intermediate-risk clinically localized prostate cancer. Prostate biopsy, which is an invasive procedure that has associated side effects, is performed repeatedly during the course of AS. Progression on biopsies may trigger curative treatment. Yet, no consensus has been reached on the optimal frequency for repeated biopsies. Gleason score is considered as the current best biomarker in predicting long-term prostate cancer outcomes. However, Gleason scores are assigned manually through pathologist review, a process that has been shown to have low inter-observer agreement across pathologists.
The aforementioned problems create a great clinical need for tools that should leverage rich prognostic information embedded in histopathological images to predict the potential change of tumor histology. The main objective of this project is to extract quantitative representations from histopathological images, which can be combined with clinical variables and imaging features in a multi-modal model to better characterize the progression of prostate cancer. Towards this goal, we have been working on a set of semi-supervised image segmentation models, weakly-supervised detection and classification models, and self-supervised models.