Wu Zhuo, Matsui Osamu, Kitao Azusa, Kozaka Kazuto, Koda Wataru, Kobayashi Satoshi, Ryu Yasuji, Minami Tetsuya, Sanada Junichiro, Gabata Toshifumi
Department of Radiology, Kanazawa University Graduate School of Medical Science, 13-1 Takaramachi, Kanazawa 920-8640, Japan; Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang Xi Road, Guangzhou 510120, Guangdong, China.
Department of Radiology, Kanazawa University Graduate School of Medical Science, 13-1 Takaramachi, Kanazawa 920-8640, Japan.
PLoS One. 2015 Mar 5;10(3):e0118297. doi: 10.1371/journal.pone.0118297. eCollection 2015.
To assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging.
From April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.
Based on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.
Texture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.
评估在T2加权（T2W）、T1加权（T1W）及钆塞酸二钠增强肝细胞期（EOB-HP）成像上，纹理分析对慢性丙型肝炎患者纤维化分期及坏死性炎症活动度分级进行分类的可行性。
回顾性分析2008年4月至2012年6月间123例经病理证实为慢性丙型肝炎患者的磁共振图像。利用成像软件估算从直方图、梯度、游程矩阵、共生矩阵、自回归模型及小波变换方法得出的纹理参数。采用Fisher、分类错误概率及平均相关性以及互信息系数来提取优化纹理特征子集。将线性判别分析与1-最近邻分类器（LDA/1-NN）相结合用于病变分类。根据软件要求，分别基于所有患者数据集、坏死性炎症活动度为1级的患者组以及纤维化分期为4期的患者组进行分类。
基于所有患者数据集，在T2W、T1W及EOB-HP图像上，LDA/1-NN对纤维化分期的误分类率分别为28.46%、35.77%和20.33%，对坏死性炎症活动度分级的误分类率分别为34.15%、25.20%和28.46%。在坏死性炎症活动度为1级的患者组中，LDA/1-NN在T2W、T1W及EOB-HP图像上对纤维化分期的误分类率分别为5.00%、0%和12.50%。在纤维化分期为4期的患者组中，LDA/1-NN在T2W、T1W及EOB-HP图像上对坏死性炎症活动度分级的误分类率分别为5.88%、12.94%和11.76%。
磁共振图像的纹理定量参数有助于慢性丙型肝炎纤维化分期及坏死性炎症活动度分级的分类，尤其是在根据活动度或纤维化程度对输入数据集进行分类以消除纤维化分期与坏死性炎症活动度分级对纹理特征的干扰之后。
