BackgroundMulticompartmental modeling outperforms conventional diffusion‐weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study TypeRetrospective.SubjectsForty‐six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field Strength/Sequence3T multishell diffusion‐weighted sequence.AssessmentMulticompartmental models with 2–5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model‐fitting residuals were calculated to quantify model complexity and goodness‐of‐fit. Tumor contrast‐to‐noise ratio (CNR) and tumor‐to‐background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal‐contribution maps.Statistical TestsAnalysis of variance (ANOVA) and two‐sample t‐tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T‐tests (α = 0.05) were also used to assess differences in compartmental signal‐fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4‐compartment model, with optimal ADCs of 5.2e‐4, 1.9e‐3, 3.0e‐3, and >3.0e‐2 mm2/sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment‐1 signal maps than conventional DWI (P < 0.05) and increased with model order.Data ConclusionThe 4‐compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer.Level of Evidence 3Technical Efficacy Stage 3J. MAGN. RESON. IMAGING 2021;53:628–639.

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Statin use is independently associated with significant improvements in PFS for the overall group and PFS and OS in the hyperlipidemic group.

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

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