Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging.
To 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.
In all, 199 patients with PSA levels of 4-10 ng/mL.
3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI.
Lesion 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.
For 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.
The 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%.
The 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.
3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019.
Journal of magnetic resonance imaging : JMRI. 2019 Dec 06 [Epub ahead of print]
Yafei Qi, Shuaitong Zhang, Jingwei Wei, Gumuyang Zhang, Jing Lei, Weigang Yan, Yu Xiao, Shuang Yan, Huadan Xue, Feng Feng, Hao Sun, Jie Tian, Zhengyu Jin
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China., Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Science, Beijing, China., Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China., Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.