Deep Learning Identified Extra-Prostatic Extension and Seminal Vesicle Invasion as an MRI Biomarker for Prostate Cancer Outcomes
Hossain, S.; Hossain, S.; Sritharan, D.; Fu, D.; Nene, A.; Hossain, J.; Chadha, S.; Kim, I.; Lin, M.; Aboian, M.; Aneja, S.
Show abstract
Current risk stratification methods for localized prostate cancer (PCa) are reliant on clinical and pathological variables that do not easily account for location of cancer spread. Prostate MRI is a helpful tool to identify anatomic extra-prostatic cancer spread (EPE) and seminal vesicle invasion (SVI) but is subject to radiologist expertise and inter-observer variation. We report deep learning models which provide objective end-to-end evaluation of EPE and SVI on prostate MRI. Both EPE and SVI models demonstrate high discriminatory ability on three held-out test sets spanning different clinical settings, equipment manufacturers, and MRI magnet strengths. Interpretability studies suggest both EPE and SVI models identify clinically-relevant anatomic regions. Lastly, we show that classification of EPE and SVI by our models is independently associated with increased risk of biochemical recurrence (BCR) following localized treatment. Furthermore, we demonstrate that our models can be easily integrated to well-established risk stratification methods (NCCN and UCSF-CAPRA) for improved ability to identify high risk PCa phenotypes.
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