Related Experiment Video
Updated: Nov 11, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using
Christine H Feng1, Christopher C Conlin2, Kanha Batra3
1Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California, USA.
Background:
Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity.
Purpose:
To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.
Study Type:
Retrospective.
Subjects:
Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.
Field Strength/Sequence:
A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.
Assessment:
High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated.
Statistical Tests:
Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.
Results:
RSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).
Data Conclusion:
RSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.
Evidence Level:
3 TECHNICAL EFFICACY: Stage 2.
Insights
A new diffusion MRI model, RSI4-C1, significantly improves prostate cancer detection compared to conventional ADC. This advanced technique offers better accuracy for identifying tumors, aiding in diagnosis and treatment planning.
Area of Science:
- Radiology and Imaging Science
- Oncology Research
- Biomedical Engineering
Background:
- Conventional diffusion MRI (dMRI) for prostate cancer (PCa) detection faces limitations with apparent diffusion coefficient (ADC) due to tissue complexity and image noise.
- The four-compartment restriction spectrum imaging (RSI4) model shows promise in characterizing pelvic diffusion signals.
- The slowest diffusion compartment coefficient (RSI4-C1) from this model demonstrated superior tumor conspicuity.
Purpose of the Study:
- To assess the efficacy of the RSI4-C1 model's slowest diffusion compartment as a quantitative, voxel-level classifier for PCa.
- To compare the diagnostic performance of RSI4-C1 against conventional ADC in differentiating PCa from benign prostate tissue.
Main Methods:
- A retrospective study involving 46 men with suspected PCa (23 with PCa, 23 with benign prostates) using 3T MRI.
- Calculation of RSI4-C1 and conventional ADC values from diffusion images.
- Generation of classifier images and assessment of voxel-level discrimination using ROC curves and comparison of AUC and FPR90 at 90% sensitivity.
Main Results:
- RSI4-C1 significantly outperformed conventional ADC in PCa detection.
- RSI4-C1 achieved a higher Area Under the ROC Curve (AUC) (0.977 vs. 0.922).
- RSI4-C1 demonstrated a lower false-positive rate at 90% sensitivity (FPR90) (0.032 vs. 0.201), with statistically significant differences (P < 0.05).
Conclusions:
- The RSI4-C1 model provides a superior quantitative, voxel-level classifier for PCa compared to conventional ADC.
- RSI classifier images with low false-positive rates hold potential for enhancing PCa detection.
- This advanced imaging approach may facilitate clinical applications such as targeted biopsy and treatment planning for prostate cancer.
