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Updated: Jul 12, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Detecting clinically significant prostate cancer with a distributed parameter model based on quantitative dynamic
Hongjiang Zhang1,2, Ji Du2, Jiannan Lei3
1Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China.
A novel distributed parameter (DP) model for dynamic contrast-enhanced MRI (DCE-MRI) effectively differentiates clinically significant prostate cancer (csPCa) from clinically insignificant PCa (ciPCa). The permeability surface area product (PS) from the DP model shows strong potential as a quantitative imaging biomarker for PCa risk stratification.
Area of Science:
- Radiology and Imaging Science
- Oncology and Cancer Research
- Biomedical Engineering and Quantitative Modeling
Background:
- Accurate differentiation between clinically significant prostate cancer (csPCa) and clinically insignificant PCa (ciPCa) is crucial for treatment decisions.
- Multiparametric MRI (mp-MRI) is essential for PCa detection, but dynamic contrast-enhanced MRI (DCE-MRI) has limited use due to its qualitative nature.
- Quantitative analysis of DCE-MRI using pharmacokinetic models may enhance diagnostic value for PCa.
Purpose of the Study:
- To evaluate the feasibility of a distributed parameter (DP) model using quantitative DCE-MRI for differentiating csPCa from ciPCa.
- To compare the diagnostic performance of the DP model with existing models like the extended Tofts model (ETM) and adiabatic tissue homogeneity (ATH) model.
- To assess the incremental value of quantitative DCE parameters in improving mp-MRI diagnostic performance.
Main Methods:
- Prospective enrollment of patients with suspected PCa undergoing 3.0-T DCE-MRI.
- Voxel-wise kinetic parameter estimation using DP, ETM, and ATH models.
- Manual delineation of regions of interest (ROIs) on parameter maps (PS for DP/ATH, Ktrans for ETM) and comparison with Gleason score as the reference standard.
Main Results:
- DP model-derived parameters, particularly PS, were significantly higher in csPCa compared to ciPCa (P<0.05), with PS showing the strongest discriminative ability (P<0.001).
- DP-derived PS achieved the highest diagnostic performance (AUC=0.87, sensitivity=0.74, specificity=0.83, accuracy=0.78), outperforming ETM (AUC ≤0.71) and ATH (AUC ≤0.82) parameters.
- Incorporating DCE parameters into mp-MRI improved diagnostic performance compared to biparametric MRI (AUC: 0.92 vs. 0.88).
Conclusions:
- Quantitative DCE-MRI based on the DP model demonstrates superior performance in distinguishing csPCa from ciPCa compared to ETM and ATH models.
- DP-derived PS is a robust quantitative imaging biomarker with strong diagnostic ability for PCa risk stratification.
- The DP model holds significant potential for improving PCa detection and management through quantitative imaging.
