利用3.0T联合对比增强磁共振成像的纹理分析评估肝纤维化
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T.
作者信息
Yokoo Takeshi, Wolfson Tanya, Iwaisako Keiko, Peterson Michael R, Mani Haresh, Goodman Zachary, Changchien Christopher, Middleton Michael S, Gamst Anthony C, Mazhar Sameer M, Kono Yuko, Ho Samuel B, Sirlin Claude B
机构信息
Departments of Radiology, University of California, San Diego, CA 92103, USA ; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, 2201 Inwood Road, NE2.210B, Dallas, TX 75390-9085, USA.
Computational and Applied Statistics Laboratory, San Diego Supercomputer Center, University of California, San Diego, CA 92093, USA.
出版信息
Biomed Res Int. 2015;2015:387653. doi: 10.1155/2015/387653. Epub 2015 Sep 1.
PURPOSE
To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis.
MATERIALS AND METHODS
In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L 1 regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses.
RESULTS
The texture-based predicted fibrosis score significantly correlated with qualitative (r = 0.698, P < 0.001) and quantitative (r = 0.757, P < 0.001) histology. The prediction model for qualitative histology had 0.814-0.976 areas under the curve (AUC), 0.659-1.000 sensitivity, 0.778-0.930 specificity, and 0.674-0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742-0.950 AUC, 0.688-1.000 sensitivity, 0.679-0.857 specificity, and 0.696-0.848 accuracy, depending on the binary classification threshold.
CONCLUSION
CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis.
目的
使用联合对比增强（CCE）磁共振成像（MRI）和纹理分析对肝纤维化进行无创评估。
材料与方法
在这项经机构审查委员会（IRB）批准且符合健康保险流通与责任法案（HIPAA）的前瞻性研究中，46名新诊断为丙型肝炎病毒（HCV）感染且近期进行过肝脏活检的成年人在静脉注射超顺磁性氧化铁（菲立磁）和钆喷酸葡胺（钆双胺）后接受了CCE肝脏MRI检查。通过计算165个纹理特征，在感兴趣区域对肝脏的图像纹理进行量化。肝脏活检标本用Masson三色染色法染色，并进行定性（METAVIR纤维化评分）和定量（胶原染色面积百分比）评估。使用L1正则化路径算法构建了两个基于纹理的多变量线性模型，一个用于定量预测，另一个用于定量组织学预测。使用受试者操作特征（ROC）和相关性分析评估每个模型的预测性能。
结果
基于纹理的预测纤维化评分与定性（r = 0.698，P < 0.001）和定量（r = 0.757，P < 0.001）组织学显著相关。定性组织学的预测模型根据二元分类阈值，曲线下面积（AUC）为0.814 - 0.976，灵敏度为0.659 - 1.000，特异性为0.778 - 0.930，准确率为0.674 - 0.935。定量组织学的预测模型根据二元分类阈值，AUC为0.742 - 0.950，灵敏度为0.688 - 1.000，特异性为0.679 - 0.857，准确率为0.696 - 0.848。
结论
CCE MRI和纹理分析可能允许对肝纤维化进行无创评估。
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