Chao-Gang Wei1, Tong Chen2, Yue-Yue Zhang3, Peng Pan4, Guang-Cheng Dai5, Hong-Chang Yu6, Shuo Yang7, Zhen Jiang8, Jian Tu9, Zhi-Hua Lu10, Jun-Kang Shen11, Wen-Lu Zhao12. 1. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: weichaogang1122@163.com. 2. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China; Institute of Radiation Oncology; Therapeutics of Soochow University, Suzhou China. Electronic address: chentong1120@163.com. 3. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: yxyxzyy@126.com. 4. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: suzhoupanp@163.com. 5. Department of Urology Surgery, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: daiguangcheng@163.com. 6. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: 835702258@qq.com. 7. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: 957439953@qq.com. 8. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: j102@163.com. 9. Department of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: tujian654321@126.com. 10. Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Changshu 215500, China. Electronic address: luzhihua79@163.com. 11. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China; Institute of Radiation Oncology; Therapeutics of Soochow University, Suzhou China. Electronic address: shenjunkang@suda.edu.cn. 12. Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China. Electronic address: wenluzhao81@163.com.
Abstract
PURPOSE: To predict clinically significant prostate cancer (cs-PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators in men with prostate-specific antigen (PSA) levels in the gray zone of 4-10 ng/mL. METHOD: We retrospectively analyzed 364 patients with elevated PSA levels in the gray zone who had pathologically confirmed disease and had undergone MRI examinations from January 2015 to October 2019; a training group (n = 255) and validation group (n = 109) were randomly established. Multivariate logistic regression analysis of the training group was performed to identify the independent predictors for cs-PCa, thereby establishing a predictive model that was evaluated in the training and validation groups by analyzing the receiver operating characteristic (ROC) curve. RESULTS: In the training group, the PI-RADS v2 score and prostate volume (PV) were independent predictors of cs-PCa (P < 0.05). The prediction model comprising the PI-RADS v2 score and PV had a larger AUC than the other predictors alone in the training group. The diagnostic sensitivity and specificity of the prediction model were 84.1 % and 83.4 %, respectively. The prediction model was indicated to have better predictive performance in the validation group. CONCLUSIONS: The prediction model exhibits a satisfactory predictive value for cs-PCa in men with PSA levels in the gray zone. PI-RADS v2 is the strongest univariate predictor for the detection of cs-PCa in men with PSA in the gray zone, but combining this with the PV can provide superior predictive ability.
PURPOSE: To predict clinically significant prostate cancer (cs-PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators in men with prostate-specific antigen (PSA) levels in the gray zone of 4-10 ng/mL. METHOD: We retrospectively analyzed 364 patients with elevated PSA levels in the gray zone who had pathologically confirmed disease and had undergone MRI examinations from January 2015 to October 2019; a training group (n = 255) and validation group (n = 109) were randomly established. Multivariate logistic regression analysis of the training group was performed to identify the independent predictors for cs-PCa, thereby establishing a predictive model that was evaluated in the training and validation groups by analyzing the receiver operating characteristic (ROC) curve. RESULTS: In the training group, the PI-RADS v2 score and prostate volume (PV) were independent predictors of cs-PCa (P < 0.05). The prediction model comprising the PI-RADS v2 score and PV had a larger AUC than the other predictors alone in the training group. The diagnostic sensitivity and specificity of the prediction model were 84.1 % and 83.4 %, respectively. The prediction model was indicated to have better predictive performance in the validation group. CONCLUSIONS: The prediction model exhibits a satisfactory predictive value for cs-PCa in men with PSA levels in the gray zone. PI-RADS v2 is the strongest univariate predictor for the detection of cs-PCa in men with PSA in the gray zone, but combining this with the PV can provide superior predictive ability.