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Updated: Jan 16, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Investigating the role of multiparametric and biparametric MRI based on PI-RADS v2.1 and machine learning models in
1Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
Purpose:
We compared the diagnostic performance of multiparametric MRI (mpMRI) and biparametric MRI (bpMRI) in detecting clinically significant prostate cancer (csPCa) using the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Additionally, we constructed multiple machine learning (ML) models for detecting csPCa using PI-RADS scores and clinical parameters.
Materials And Methods:
We enrolled 583 patients with 594 lesions who underwent mpMRI before MRI/transrectal ultrasound (MRI-TRUS) fusion-targeted biopsy and systematic biopsy. The diagnostic performance of bpMRI and mpMRI was analyzed by the area under the curve (AUC). We built multiple ML models for detecting csPCa. The input parameters were: PI-RADS scores in bpMRI or mpMRI, age, prostate-specific antigen (PSA), MRI-defined PSA density (PSAD), and prostate volume (PV). Training and test cohorts included 475 and 119 lesions, respectively.
Results:
The AUCs of bpMRI and mpMRI for the diagnosis of csPCa in total lesions were 0.88 and 0.90, respectively (p<0.05). mpMRI had higher sensitivity (93.1%) but lower specificity (77.3%) compared to bpMRI (sensitivity: 79.2%; specificity: 86.2%). All the ML models exhibited the high AUC in detecting csPCa (0.93-0.96 based on mpMRI models and 0.91-0.94 based on bpMRI models). There were no statistically significant differences in the AUC values between the two groups of ML models in test sets.
Conclusions:
Compared to bpMRI, the AUC of mpMRI based on PI-RADS v2.1 to detect csPCa was higher. The diagnostic performance of ML models for detecting csPCa using PI-RADS scores and clinical parameters was excellent and comparable between mpMRI and bpMRI.
