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Abstract Background To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate nodules from benign ones, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. Methods A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. The clinical model was constructed using logistic regression analysis. Radiomic models were created by comparing seven machine learning classifiers. The useful clinical variables and radiomic signature were integrated to develop the combined model. Model performance was assessed by receiver operating characteristic curve, calibration curve, decision curve, and clinical impact curve. Results The ratio of free PSA to total PSA, PSA density, peripheral zone volume, and PI-RADS score were independent determinants of malignancy. The clinical model based on these factors achieved an AUC of 0.814 (95%CI: 0.763–0.865) and 0.791 (95%CI: 0.742-840) in the internal and external validation cohorts, respectively. The clinical-radiomic nomogram yielded the highest accuracy, with an AUC of 0.925 (95% CI: 0.894–0.956) and 0.872 (95%CI: 0.837–0.907) in the internal and external validation cohorts, respectively. DCA and CIC further confirmed the clinical usefulness of the nomogram. Conclusion Biparametric MRI-based radiomics has the potential to noninvasively discriminate between benign and malignant prostate nodules, which outperforms screening strategies based on PSA and PI-RADS.
Lu et al. (Mon,) studied this question.