mpMRI Radiomic Features Predict the Likelihood for Progression to Treatment of Prostate Cancer Patients on Active Surveillance

Active surveillance (AS) for prostate cancer has emerged as a safe and attractive alternative to immediate treatment. Here we present an integrated method for baseline mpMRI analysis enabling early detection of patients harboring lesions with a high potential for progression. The approach consists of three steps: (i) Training a deep learning network for automatic segmentation of prostate and lesions, suspicious for cancer; (ii) Application of the network to identify lesions on mpMRI images for patients, enrolled in an AS trial; and (iii) Development of a progression risk stratification model by incorporating radiomic and clinical variables.