| Summary: | Yueyue Zhang,1,2,* Guiqi Zhu,3,* Wenlu Zhao,1 Chaogang Wei,1 Tong Chen,1 Qi Ma,4 Yongsheng Zhang,5 Boxin Xue,6 Junkang Shen1,2 1Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, People’s Republic of China; 2Department of Radiotherapy Institute, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, People’s Republic of China; 3Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, People’s Republic of China; 4Department of Ultrasound, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, People’s Republic of China; 5Department of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, People’s Republic of China; 6Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Junkang Shen Tel +86-512-67783842Email sdfeysjk@126.comPurpose: To develop and validate a PI-RADS-based nomogram for predicting the probability of clinically significant prostate cancer (csPCa) at initial prostate biopsy.Patients and Methods: From February 2015 to October 2018, 573 consecutive patients made up the development cohort (DC), and another 253 patients were included as an independent validation cohort (VC). Univariate and multivariate analysis were used for determining the dependent clinical risk factors for csPCa. Prediction model1 was constructed by integrating independent clinical risk factors. Then added the PI-RADS score to model1 to develop the prediction model2 and present it in the form of a nomogram. The performance of the nomogram was assessed by receiver operating characteristic curve, net reclassification improvement analysis, calibration curve, and decision curve.Results: All clinical candidate factors were significantly different between csPCa and non-csPCa in both the DC and VC. Age, PSA density (PSAD), and free-to-total PSA ratio (f/t) were ultimately determined as dependent clinical risk factors for csPCa and integrated into prediction model1. Then, prediction model2 was developed and presented in a nomogram. In the DC, the nomogram (AUC=0.894) was superior to model1, PI-RADS score, or other clinical factors alone in detecting csPCa. Similar result (AUC=0.891) was obtained in the VC. NRI analysis showed that the nomogram improved the classification of patients significantly compared with model1. Furthermore, the nomogram showed favorable calibration and great clinical usefulness.Conclusion: This study developed and validated a nomogram that integrates PI-RADS score with other independent clinical risk factors to facilitate prebiopsy individualized prediction in high-risk patients with csPCa.Keywords: prostate imaging reporting and data system, nomogram, clinically significant prostate cancer, cohort study
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