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

Multicompartmental modeling outperforms conventional DWI in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. Retrospective. 46 patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. 3T multi-shell diffusion-weighted sequence. Multicompartmental models with 2–5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental 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. 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. 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/s. 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. The 4-compartment signal model best described diffusion in the prostate. Compartmental signal-contributions revealed by this model may improve assessment of prostate cancer.

多室模型在前列腺癌评估中优于传统弥散加权成像（DWI）。优化的多室模型可进一步提高前列腺癌的检测和定性能力。 目的：优化多室信号模型并将其应用于体内正常和癌性前列腺组织的弥散研究。 研究类型：回顾性研究。 研究对象：46例因疑似前列腺癌接受磁共振成像（MRI）检查的患者；其中23例患有前列腺癌，23例未检测到癌症。 检查方法：3T多壳弥散加权序列。 将具有2 - 5个组织室的多室模型与前列腺的DWI数据进行拟合，以确定最佳的室表观弥散系数（ADC）。利用这些ADC计算不同室的信号贡献。计算贝叶斯信息准则（BIC）和模型拟合残差，以量化模型复杂性和拟合优度。计算传统DWI和多室信号贡献图的肿瘤对比噪声比（CNR）和肿瘤与背景信号强度比（SIR）。 采用方差分析和两样本t检验（α = 0.05）比较前列腺不同区域以及多室模型之间的拟合残差。t检验（α = 0.05）还用于评估不同组织类型之间室信号分数的差异以及传统DWI和多室模型之间的CNR/SIR差异。 4室模型的BIC最低，其最佳ADC分别为5.2×10⁻⁴、1.9×10⁻³、3.0×10⁻³和>3.0×10⁻² mm²/s。多室模型的拟合残差显著低于传统ADC成像（P < 0.05）。外周带的残差最低，肿瘤中的残差最高。通过增加模型阶数，肿瘤组织的拟合残差降幅最大。肿瘤中来自第1室的信号比例高于正常组织（P < 0.05）。在第1室信号图上，肿瘤的CNR和SIR大于传统DWI（P < 0.05），且随模型阶数增加而增大。 4室信号模型最能描述前列腺中的弥散情况。该模型揭示的室信号贡献可能会改善前列腺癌的评估。