Related Experiment Video
Updated: Jan 2, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary
Yafei Qi1, Shuaitong Zhang2,3, Jingwei Wei2,3
1Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background:
Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging.
Purpose:
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.
Study Type:
Retrospective.
Subjects:
In all, 199 patients with PSA levels of 4-10 ng/mL.
Field Strength/Sequence:
3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI.
Assessment:
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.
Statistical Tests:
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.
Results:
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%.
Data Conclusion:
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.
Level Of Evidence:
3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.
