X-K Niu1, W-F He2, Y Zhang3, S K Das4, J Li5, Y Xiong1, Y-H Wang6. 1. Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China. 2. Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Sichuan 637000, China. 3. Department of Radiology, Deyang City People's Hospital, 618000, China. 4. Department of Interventional Radiology, Tenth People's Hospital of Tongji University, Shanghai 200072, China. Electronic address: niu051228@163.com. 5. Department of General Surgery, Affiliated Hospital of Chengdu University, Chengdu 610081, China. 6. Department of Urology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China.
Abstract
AIM: To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters. MATERIAL AND METHODS: Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy. RESULTS: The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83). CONCLUSION: The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.
AIM: To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters. MATERIAL AND METHODS: Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy. RESULTS: The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83). CONCLUSION: The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.
Authors: Anwar R Padhani; Jelle Barentsz; Geert Villeirs; Andrew B Rosenkrantz; Daniel J Margolis; Baris Turkbey; Harriet C Thoeny; François Cornud; Masoom A Haider; Katarzyna J Macura; Clare M Tempany; Sadhna Verma; Jeffrey C Weinreb Journal: Radiology Date: 2019-06-11 Impact factor: 11.105
Authors: Matthew D Greer; Joanna H Shih; Nathan Lay; Tristan Barrett; Leonardo Bittencourt; Samuel Borofsky; Ismail Kabakus; Yan Mee Law; Jamie Marko; Haytham Shebel; Maria J Merino; Bradford J Wood; Peter A Pinto; Ronald M Summers; Peter L Choyke; Baris Turkbey Journal: AJR Am J Roentgenol Date: 2019-03-27 Impact factor: 3.959