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Abstract #2280

Prostate cancer risk assessment models for bi-parametric MRI in a large patient population

Lavanya Umapathy1,2, Patricia Johnson1,2, Tarun Dutt1, Angela Tong1, Sumit Chopra1,2,3, Hersh Chandarana1,2, and Daniel K Sodickson1,2,4
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 3Courant Institute of Mathematical Sciences, New York University, New York, NY, United States, 4Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Prostate, Diagnosis/Prediction, Automated risk assessment, active surveillance

Motivation: Although the rate of prostate cancer (PCa) growth varies across individuals, the current active surveillance (AS) strategy for PCa follows a one-size-fits-all approach.

Goal(s): To develop an AI-based risk assessment framework to provide physicians with tools to make personalized and objective decisions for AS in prostate.

Approach: We train models with representational learning approaches to predict patient-specific risk of prostate cancer (current and future) using biparametric MR images of the prostate acquired on a large patient population (n=28,342).

Results: The risk assessment framework demonstrated AUCs of 0.88, 0.84, and 0.82 for predicting current, 2-year, and 5-year risk of prostate cancer.

Impact: Automated AI-based risk-assessment frameworks can aid personalized and objective decisions for AS. Patients with higher risks can be managed more aggressively with imaging and biopsy compared to those with lower risks, potentially avoiding overtreatment and overdiagnosis of prostate cancer.

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