Keywords: Diagnosis/Prediction, Cancer
Motivation: Accurate selection of prostate cancer patients to undergo active surveillance (AS) is crucial to ensure suitable treatment.
Goal(s): To develop an automated framework for mpMRI analysis to assist clinical decision making about whether a patient should remain on AS.
Approach: We developed a progression risk stratification model using mpMRI data from an AS trial, and incorporating clinical biomarkers and radiomic features from lesions identified by a deep neural network.
Results: The lesion segmentation network achieved a median DSC of 60.7%, and the progression prediction model an AUC of 81.1% in determining likelihood of progression within 12 months.
Impact: We present a fully automated methodology to assess prostate cancer progression risk for AS patients within the timeframe between their follow-up visits, thereby providing essential data for clinicians that can prospectively improve AS patient selection.
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