Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer - Li, Mou; Yang, Ling; Yue, Yufeng; Xu, Jingxu; Huang, Chencui; Song, Bin (Frontiers in Oncology, 2021)
Objective: To investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa). Methods: This was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including apparent diffusion coefficient (ADC) imaging, T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis. Results: A total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) (0.979 [95% CI, 0.940–0.996]) than PI-RADS (0.905 [0.844–0.948], P = 0.002) in the training set. However, the AUC between them was insignificant in the validation set (0.861 [0.749–0.936] vs. 0.845 [0.731–0.924], P = 0.825). When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P value less than 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set). Conclusions: The radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.