Radiomic approaches for prostate cancer risk stratification largely depend on radiologist delineation of prostate cancer regions of interest (ROI) on MRI. In this study, we acquired multi-reader delineations of ROIs, derived radiomic features within the ROIs trained and evaluated machine learning classifiers. We observed that variation in delineations did not affect the classification performance within a cohort but it did affect when evaluated on an independent validation set. We observed that a more conservative approach in delineations may ensure better generalizability and classification performance of machine learning models.
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