Related Experiment Video
Updated: Aug 14, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps
Matthew Gibbons1, Jeffry P Simko2, Peter R Carroll3
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.
Abstract:
Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.
Insights
This study developed prostate cancer (PCa) risk maps using multi-parametric MRI (mpMRI) to precisely locate and quantify PCa volume. These maps accurately detect PCa lesions, aiding treatment decisions and biopsy targeting.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Multi-parametric MRI (mpMRI) is crucial for prostate cancer (PCa) assessment.
- Accurate quantification and localization of PCa are vital for treatment planning, biopsy targeting, and focal therapy.
- Existing methods may lack precision in determining the exact volume and location of PCa.
Purpose of the Study:
- To generate PCa risk maps from pre-prostatectomy mpMRI data.
- To quantify the volume and location of both all PCa and high-grade PCa (Gleason grade group ≥ 3).
- To aid physicians and patients in making informed treatment decisions.
Main Methods:
- A cohort of men with biopsy-proven PCa and pre-prostatectomy mpMRI scans were studied.
- Prostate cancer (PCa) and benign regions of interest (ROIs) were identified and registered between mpMRI and histopathology.
- Logistic regression models using apparent diffusion coefficient (ADC), T2 weighted (T2W), and dynamic contrast-enhanced MRI (DCE MRI) parameters were developed to create cancer probability maps.
Main Results:
- The developed PCa risk maps accurately quantified PCa volume for lesions >0.1 cc.
- The models achieved an area under the curve (AUC) of 0.91 for all PCa and 0.73 for high-grade PCa.
- The PCa maps detected 90% of histopathological lesions >0.1 cc at the optimum threshold.
Conclusions:
- Pre-prostatectomy mpMRI-derived cancer risk maps are feasible for detecting and quantifying PCa lesions.
- This method offers improved non-invasive capabilities for PCa detection, localization, volume estimation, and characterization.
- Further improvements can refine the accuracy of cancer volume estimation by addressing over- and underestimation root causes.
