Keywords: Diagnosis/Prediction, AI/ML Software, Active Surveillance, Cancer Progression
Motivation: Patients with low-risk prostate cancer are often put under active surveillance (AS) to delay treatment until cancer progression. Selecting correctly is crucial to disease management.
Goal(s): The aim is to determine if the embeddings of U-Found can be utilized to assist in the clinical decision making whether a patient is at high risk for progression.
Approach: Using multiparametric (mp)MRI data from contemporary clinical trial, we obtained embeddings from Apparent Diffusion Coefficient (ADC) maps. We developed a model using features related to progression.
Results: The prediction model reached AUC of 97%, indicating strong cancer signal available in the embeddings.
Impact: We present an application of novel MRI-based foundation model of the prostate to assess prostate cancer progression risk for AS patients, thereby providing essential data for clinicians that can prospectively improve AS patient selection.
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