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Updated: Mar 26, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the
Au Hoang Dinh1, Christelle Melodelima1, Rémi Souchon1
1From INSERM, U1032, LabTau, Lyon, France (A.H.D., R.S., J.L., F.B., S.C., O.R.); CNRS, UMR 5553, BP 53, Grenoble, France (C.M.); Laboratoire d'Ecologie Alpine, Université Joseph Fourier, Grenoble, France (C.M.); Department of Urinary and Vascular Imaging (F.B., O.R.), Department of Pathology (F.M.L.), and Department of Urology (S.C., M.C.), Hospices Civils de Lyon, Hôpital Edouard Herriot, Pavillon P Radio, 5 place d'Arsonval, Lyon 69003, France; Université Lyon 1, Faculté de Médecine Lyon Est, Lyon, France (F.B., S.C., M.C., O.R.); and Department of Urology, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Pierre Bénite, France (A.R.).
Abstract:
Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.
Insights
A quantitative model using the 10th percentile of apparent diffusion coefficient (ADC) and time to peak (TTP) accurately distinguishes aggressive prostate cancers. This model shows consistent performance across different 3-T multiparametric MRI manufacturers.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Multiparametric MRI (mpMRI) at 3 Tesla is crucial for detecting prostate cancer.
- Distinguishing aggressive cancers (Gleason score ≥ 7) in the peripheral zone (PZ) remains a challenge.
- Intermanufacturer variability in quantitative models can affect diagnostic accuracy.
Purpose of the Study:
- To evaluate the consistency of quantitative models in differentiating prostate cancers with Gleason score ≥ 7.
- To assess intermanufacturer variability using 3-T mpMRI data from two manufacturers.
- To identify the most effective quantitative parameters for cancer detection.
Main Methods:
- Retrospective review of 106 patients with 3-T mpMRI (T2-weighted, diffusion-weighted, dynamic contrast-enhanced).
- Analysis of 11 quantitative parameters in PZ lesions, including apparent diffusion coefficient (ADC) and time to peak (TTP).
- Model performance evaluated using the area under the receiver operating characteristic curve (AUC) to discriminate Gleason score ≥ 7 cancers.
Main Results:
- The combined model of 10th percentile ADC and TTP achieved the highest AUC in both datasets.
- AUC values ranged from 0.84 to 0.90, demonstrating robust performance.
- No additional parameters significantly improved the model's independent predictive information.
Conclusions:
- The quantitative model utilizing 10th percentile ADC and TTP reliably discriminates aggressive prostate cancers (Gleason score ≥ 7) in PZ lesions.
- This model demonstrates low intermanufacturer variability, suggesting its potential for widespread clinical application.
- The findings support the use of this specific quantitative model for improved prostate cancer diagnosis.
