The Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 has been recognized as the standard for prostate magnetic resonance imaging (MRI) reporting in prostate cancer detection and risk stratification. The weight of dynamic contrast-enhanced imaging is limited in this version since its evidence supporting of necessity is controversial [1-3]. For men with clinical suspicion of prostate cancer, undergoing prostate MRI without intravenous contrast agents, termed biparametric MRI (bpMRI), can reduce the MRI scanning time, lower the cost of examination for patients, and avoid the risks associated with adverse events to contrast agents [2, 4].
Prostate-specific antigen (PSA) has been widely used in clinical practice, facilitating the early detection of prostate cancer. However, its low specificity can lead to unnecessary prostate biopsies for patients [5]. To address the limitations of PSA testing and enhance the accuracy, predictive models (or ‘risk calculators’) based on useful predictive factors are used in pre-biopsy risk assessments [6, 7], especially for the detection of clinically significant prostate cancer (CsPCa). The promising value of several PSA related clinical parameters and bpMRI protocols has recently been reported in CsPCa diagnosis [8-10].
The aim of this study is to develop an easily applicable nomogram model for predicting CsPCa to improve the accuracy in risk stratification, based on the PIâ€RADS version 2.1 for bpMRI and PSA related parameters.