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Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging
Allison Y Zhong1, Leonardino A Digma1, Troy Hussain1
1Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Background:
Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs).
Objective:
To evaluate RSIrs for automated patient-level detection of csPCa.
Design Setting And Participants:
We retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.
Intervention:
We calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.
Outcome Measurements And Statistical Analysis:
We compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.
Results And Limitations:
AUC values for ADC, RSIrs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSIrs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p < 0.0001). RSIrs alone was comparable with PI-RADS (p = 0.8). The combination of PI-RADS and RSIrs had an AUC of 0.85 (0.78, 0.91) and was superior to either PI-RADS or RSIrs alone (p < 0.05). Similar patterns were seen in the peripheral and transition zones.
Conclusions:
RSIrs is a promising quantitative marker for patient-level csPCa detection, warranting a prospective study.
Patient Summary:
We evaluated a rapid, advanced prostate magnetic resonance imaging technique called restriction spectrum imaging to see whether it could give an automated score that predicted the presence of clinically significant prostate cancer. The automated score worked about as well as expert radiologists' interpretation. The combination of the radiologists' scores and automated score might be better than either alone.
Insights
Restriction spectrum imaging (RSI) offers a promising quantitative score for detecting clinically significant prostate cancer (csPCa), performing comparably to radiologist interpretation. Combining RSI scores with PI-RADS may improve csPCa detection accuracy.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Multiparametric MRI (mpMRI) aids in detecting clinically significant prostate cancer (csPCa).
- Current methods like PI-RADS and ADC show inconsistencies in csPCa detection.
- Restriction spectrum imaging (RSI) is an advanced MRI technique providing a quantitative biomarker, RSI restriction score (RSIrs).
Purpose of the Study:
- To evaluate the efficacy of RSIrs for automated, patient-level detection of csPCa.
- To compare the diagnostic performance of RSIrs against PI-RADS and ADC.
- To assess if combining PI-RADS and RSIrs enhances csPCa detection.
Main Methods:
- Retrospective analysis of 151 patients undergoing 3T mpMRI with RSI for suspected prostate cancer.
- Calculation of maximum RSIrs and minimum ADC, alongside PI-RADS v2.1 scores.
- Comparison of diagnostic performance using Area Under the Curve (AUC) for csPCa detection (grade group ≥2).
Main Results:
- RSIrs (AUC 0.78) and PI-RADS (AUC 0.77) significantly outperformed ADC (AUC 0.48) in detecting csPCa.
- RSIrs performance was comparable to PI-RADS (p=0.8).
- The combination of PI-RADS and RSIrs yielded the highest AUC (0.85), superior to either alone (p<0.05).
Conclusions:
- RSIrs demonstrates potential as a quantitative biomarker for automated csPCa detection.
- The combination of PI-RADS and RSIrs may offer superior diagnostic accuracy for csPCa.
- Further prospective studies are warranted to validate these findings.
