| Title | Prebiopsy mp-MRI Can Help to Improve the Predictive Performance in Prostate Cancer: A Prospective Study in 1,478 Consecutive Patients |
| Authors | Wang, Rui Wang, Jing Gao, Ge Hu, Juan Jiang, Yuanyuan Zhao, Zhenlong Zhang, Xiaodong Zhang, Yu-Dong Wang, Xiaoying |
| Affiliation | Peking Univ, Hosp 1, Dept Radiol, 8 Xishenku St, Beijing 100032, Peoples R China. CFDA, Ctr Med Device Evaluat, Beijing, Peoples R China. Nanjing Med Univ, Affiliated Hosp 1, Dept Radiol, Nanjing, Jiangsu, Peoples R China. Peking Univ, Hosp 1, Dept Radiol, 8 Xishenku St, Beijing 100032, Peoples R China. Zhang, YD (reprint author), Nanjing Med Univ, Affiliated Hosp 1, 300 Guangzhou Rd, Nanjing 210009, Jiangsu, Peoples R China. |
| Keywords | DIGITAL RECTAL EXAMINATION VERSION 2 BIOPSY AGGRESSIVENESS STATISTICS GUIDELINES MORTALITY DIAGNOSIS ACCURACY MEN |
| Issue Date | 2017 |
| Publisher | CLINICAL CANCER RESEARCH |
| Citation | CLINICAL CANCER RESEARCH.2017,23(14),3692-3699. |
| Abstract | Purpose: To investigate whether prebiopsy multi-parametric (mp) MRI can help to improve predictive performance in prostate cancer. Experimental Design: Based on a support vector machine (SVM) analysis, we prospectively modeled clinical data (age, PSA, digital rectal examination, transrectal ultrasound, PSA density, and prostate volume) and mp-MRI findings [Prostate Imaging and Reporting and Data System (PI-RADS) score and tumor-node-metastasis stage] in 985 men to predict the risk of prostate cancer. The new nomogram was validated in 493 patients treated at the same institution. Multivariable Cox regression analyses assessed the association between input variables and risk of prostate cancer, and area under the receiver operating characteristic curve (Az) analyzed the predictive ability. Results: At 5-year follow-up period, 34.3% of patients had systemic progression of prostate cancer. Nomogram (SVM-MRI) predicting 5-year prostate cancer rate trained with clinical and mp-MRI data was accurate and discriminating with an externally validated Az of 0.938, positive predictive value (PPV) of 77.4%, and negative predictive value of 91.5%. The improvement was significant (P < 0.001) compared with the nomogram trained with clinical data. When stratified by PSA, SVM-MRI nomogram had high PPV (93.6%) in patients with PSA > 20 ng/mL, with intermediate to low PPV in PSA 10 to 20 ng/mL (64%), PSA 4 to 10 ng/mL (55.8%), and PSA 0 to 4 ng/mL (29%). PI-RADS score (Cox HR, 2.112; P < 0.001), PSA level (HR, 1.435; P < 0.001), and age (HR, 1.012; P = 0.043) were independent predictors of prostate cancer. Conclusions: Featured with low false positive rate, mp-MRI could be the first investigation of a man with a raised PSA before prostate biopsy. (C) 2017 AACR. |
| URI | http://hdl.handle.net/20.500.11897/472123 |
| ISSN | 1078-0432 |
| DOI | 10.1158/1078-0432.CCR-16-2884 |
| Indexed | SCI(E) |
| Appears in Collections: | 第一医院 |
