Prostate Cancer Detection With Multi-parametric MRI: Logistic Regression Analysis of Quantitative T2, Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI
Prostate Cancer Detection With Multi-parametric MRI: Logistic Regression Analysis of Quantitative T2, Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI
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DOI：
10.1002/jmri.21824
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发表时间：
2009-08-01
影响因子：
4.4
通讯作者：
Haider, Masoom A.
中科院分区：
文献类型：
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作者：
Langer, Deanna L.;van der Kwast, Theodorus H.;Haider, Masoom A.
目的：建立一个多参数模型，用于磁共振成像（MRI）前瞻性识别前列腺癌周围区（PZ）。（中位年龄：63岁;范围，44-72岁）进行了T2加权，弥散加权成像（DWI），T2标测，以及使用直肠内线圈在1.5特斯拉（T）下进行动态对比增强（DCE）MRI，以产生参数表观扩散系数（ADC）、T2、体积传递常数（K-transs）和血管外细胞外体积分数（v（e））。从手术标本和描绘的PZ肿瘤产生整体组织学。将38个肿瘤轮廓（每个肿瘤一个）和病理正常的PZ区域转移到MR图像中。使用所有识别的正常和肿瘤体素生成受试者工作特征（ROC）曲线。进行逐步逻辑回归建模，检验偏差的显著性变化。采用ROC曲线下面积（A（z））进行评价和比较。（平均A（z）[95%置信区间]：A（z，ADC）：0.689 [0.675，0.702]; A（z.T2）：0.673 [0.659，0.687]; A（z，Ktranss）：0.592 [0.578，0.606]; A（z，ve）：0. 543 [0.528，0.557]）。由ADC、T2和K-transs组合而成的最佳多参数模型LR-3 p的平均值A（z，LR-3 p）为0.706 [0.692，0.719]，显著高于A（z，T2）、A（z，Ktranss）和A（z，ve）（P < 0.002）。A（z，LR-3 p）有大于A（z，ADC）的趋势，但无统计学意义（P = 0.090）。结论：应用Logistic回归，可以建立一个具有合理性能的PZ肿瘤客观模型。
Purpose: To develop a multi-parametric model suitable for prospectively identifying prostate cancer in peripheral zone (PZ) using magnetic resonance imaging (MRI).Materials and Methods: Twenty-five radical prostatectomy patients (median age, 63 years; range, 44-72 years) had T2-weighted, diffusion-weighted imaging (DWI), T2-mapping, and dynamic contrast-enhanced (DCE) MRI at 1.5 Tesla (T) with endorectal coil to yield parameters apparent diffusion coefficient (ADC), T2, volume transfer constant (K-trans) and extravascular extracellular volume fraction (v(e)). Whole-mount histology was generated from surgical specimens and PZ tumors delineated. Thirty-eight tumor outlines, one per tumor, and pathologically normal PZ regions were transferred to MR images. Receiver operating characteristic (ROC) curves were generated using all identified normal and tumor voxels. Step-wise logistic-regression modeling was performed, testing changes in deviance for significance. Areas under the ROC curves (A(z)) were used to evaluate and compare performance.Results: The best-performing single-parameter was ADC (mean A(z) [95% confidence interval]: A(z,ADC): 0.689 [0.675, 0.702]; A(z.T2): 0.673 [0.659, 0.687]; A(z,Ktrans): 0.592 [0.578, 0.606]; A(z,ve): 0. 543 [0.528, 0.557]). The optimal multi-parametric model, LR-3p, consisted of combining ADC, T2 and K-trans Mean A(z,LR-3p) was 0.706 [0.692, 0.719], which was significantly higher than A(z,T2), A(z,Ktrans), and A(z,ve) (P < 0.002). A(z,LR-3p) tended to be greater than A(z,ADC), however, this result was not statistically significant (P = 0.090).Conclusion: Using logistic regression, an objective model capable of mapping PZ tumor with reasonable performance can be constructed.