Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

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Abstract
Background: Multicompartmental 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.Purpose: To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study Type: Retrospective.Subjects: Forty‐six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field Strength/Sequence: 3T multishell diffusion‐weighted sequence.Assessment: Multicompartmental 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 Tests: Analysis 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.Results: The 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 Conclusion: The 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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Funding Information
  • National Institute of Biomedical Imaging and Bioengineering (K08 EB026503)
  • Prostate Cancer Foundation

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