• Yafei Qi, Shuaitong Zhang, Jingwei Wei, Gumuyang Zhang, Jing Lei, Weigang Yan, Yu Xiao, Shuang Yan, Huadan Xue, Feng Feng, Hao Sun, Jie Tian, and Zhengyu Jin.
• Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
• J Magn Reson Imaging. 2020 Jun 1; 51 (6): 1890-1899.

BackgroundWhether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging.PurposeTo develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies.Study TypeRetrospective.SubjectsIn all, 199 patients with PSA levels of 4-10 ng/mL.Field Strength/Sequence3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI.AssessmentLesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors.Statistical TestsFor continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant.ResultsThe combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%.Data ConclusionThe combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies.Level Of Evidence3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.© 2019 International Society for Magnetic Resonance in Medicine.

16 keywords...

hide…