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Updated: May 26, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?
Olivier Rouvière1, Matthieu Papillard, Nicolas Girouin
1Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Hôpital Edouard Herriot, Lyon, France. olivier.rouviere@netcourrier.com
Objective:
To evaluate whether focal abnormalities (FAs) depicted by prostate MRI could be characterised using simple semiological features.
Methods:
134 patients who underwent T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T before prostate biopsy were prospectively included. FAs visible at MRI were characterised by their shape, the degree of signal abnormality (0 = normal to 3 = markedly abnormal) on individual MR sequences, and a subjective score (SS(1) = probably benign to SS(3) = probably malignant). FAs were then biopsied under US guidance.
Results:
56/233 FAs were positive at biopsy. The subjective score significantly predicted biopsy results (P < 0.01). As compared to SS(1) FAs, the odds ratios (OR) of malignancy of SS(2) and SS(3) FAs were 9.9 (1.8-55.9) and 163.8 (11.5-2331). Unlike FAs' shape, a simple combination of MR signal abnormalities (into "low-risk", "intermediate" and "high-risk" groups) significantly predicted biopsy results (P < 0.008). As compared to "low risk" FAs, the OR of malignancy of "intermediate" and "high-risk" FAs were 4.5 (1.1-18.4) and 52.7 (6.8-407) in the overall population and 5.4 (1.1-27.2) and 118.2 (6.1-2301) in PZ.
Conclusions:
A simple combination of signal abnormalities of individual MR sequences can significantly stratify the risk of malignancy of FAs, holding promise of a more standardised interpretation of MRI by readers with varying experience.
Key Points:
• Using multiparameter(mp)-MRI, experienced uroradiologists can stratify the malignancy risk of prostatic lesions • The shape of prostatic focal abnormalities in the peripheral zone does not help predicting malignancy. • A simple combination of findings at mp-MRI can help less-experienced radiologists.
Insights
Simple prostate MRI signal analysis can effectively stratify cancer risk. Combining signal abnormalities from MR sequences helps predict malignancy, aiding less experienced readers in interpreting findings.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Prostate cancer diagnosis relies on accurate interpretation of magnetic resonance imaging (MRI) findings.
- Focal abnormalities (FAs) on prostate MRI require reliable characterization to guide biopsy decisions.
Purpose of the Study:
- To determine if simple semiological features of focal abnormalities (FAs) on prostate MRI can be used for characterization.
- To evaluate the predictive value of MRI signal abnormalities and lesion shape for malignancy.
Main Methods:
- 134 patients underwent multiparametric MRI (mp-MRI) including T2-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences.
- Focal abnormalities were assessed for shape, signal intensity (0-3 scale), and a subjective score (SS1-SS3).
- Biopsy results were correlated with MRI features to assess predictive accuracy.
Main Results:
- The subjective score (SS) significantly predicted biopsy results (P < 0.01).
- A combination of MR signal abnormalities into risk groups ('low', 'intermediate', 'high') significantly predicted biopsy results (P < 0.008).
- Odds ratios for malignancy increased significantly with higher risk groups, while lesion shape did not predict malignancy.
Conclusions:
- A simple combination of signal abnormalities from individual MR sequences can significantly stratify the risk of malignancy in prostate FAs.
- This approach offers a standardized method for MRI interpretation, potentially benefiting readers with varying experience levels.
- Multiparametric MRI (mp-MRI) findings, particularly signal characteristics, are crucial for risk stratification of prostatic lesions.
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