Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

SIMPLE SUMMARY: Computer-aided diagnosis systems to improve significant prostate cancer (PCa) diagnoses are being reported in the literature. These methods are based on either deep learning or radiomics. However, there is a lack of scientific evidence comparing these methods on the same external validation sets. The aim of our study was to compare the performance of a deep-learning model with the performance of a radiomics model for significant-PCa diagnosis on various cohorts. We collected multiparametric magnetic resonance images and pathology data from four patient cohorts (644 patients in total). One of the cohorts was used to develop a deep-learning model and a radiomics model. Both models were tested on the three remaining cohorts. The comparison shows that whereas the performance of the deep-learning model was higher on the training cohort, the radiomics model outperformed the deep-learning model in all the testing cohorts, making it a more accurate tool with which to detect clinically significant prostate cancer. ABSTRACT: The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.