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Abstract：
OBJECTIVE: To develop an objective method based on texture analysis on MRI for diagnosis of congenital muscular torticollis (CMT).
METHODS: The T1- and T2-weighted imaging, Q-dixon, and T1-mapping MRI data of 38 children with CMT were retrospectively analyzed. The region of interest (ROI) was manually drawn at the level of the largest cross-sectional area of the SCM on the affected side. MaZda software was used to obtain the texture features of the T2WI sequences of the ROI in healthy and affected SCM. A radiomics diagnostic model based on muscle texture features was constructed using logistic regression analysis. Fatty infiltration grade was calculated by hematoxylin and eosin staining, and fibrosis ratio by Masson staining. Correlation between the MRI parameters and pathological indicators was analyzed.
RESULTS: There was positive correlation between fatty infiltration grade and mean value, standard deviation, and maximum value of the Q-dixon sequence of the affected SCM (correlation coefficients, 0.65, 0.59, and 0.58, respectively, P < 0.05).Three muscle texture features-S(2,2)SumAverg, S(3,3)SumVarnc, and T2WI extreme difference-were selected to construct the diagnostic model. The model showed significant diagnostic value for CMT (P < 0.05). The area under the curve of the multivariate conditional logistic regression model was 0.828 (95% confidence interval 0.735-0.922); the sensitivity was 0.684 and the specificity 0.868.
CONCLUSIONS: The radiomics diagnostic model constructed using T2WI muscle texture features and MRI signal values appears to have good diagnostic efficiency. Q-dixon sequence can reflect the fatty infiltration grade of CMT.
METHODS: The T1- and T2-weighted imaging, Q-dixon, and T1-mapping MRI data of 38 children with CMT were retrospectively analyzed. The region of interest (ROI) was manually drawn at the level of the largest cross-sectional area of the SCM on the affected side. MaZda software was used to obtain the texture features of the T2WI sequences of the ROI in healthy and affected SCM. A radiomics diagnostic model based on muscle texture features was constructed using logistic regression analysis. Fatty infiltration grade was calculated by hematoxylin and eosin staining, and fibrosis ratio by Masson staining. Correlation between the MRI parameters and pathological indicators was analyzed.
RESULTS: There was positive correlation between fatty infiltration grade and mean value, standard deviation, and maximum value of the Q-dixon sequence of the affected SCM (correlation coefficients, 0.65, 0.59, and 0.58, respectively, P < 0.05).Three muscle texture features-S(2,2)SumAverg, S(3,3)SumVarnc, and T2WI extreme difference-were selected to construct the diagnostic model. The model showed significant diagnostic value for CMT (P < 0.05). The area under the curve of the multivariate conditional logistic regression model was 0.828 (95% confidence interval 0.735-0.922); the sensitivity was 0.684 and the specificity 0.868.
CONCLUSIONS: The radiomics diagnostic model constructed using T2WI muscle texture features and MRI signal values appears to have good diagnostic efficiency. Q-dixon sequence can reflect the fatty infiltration grade of CMT.
摘要：
目的：建立一种基于MRI纹理分析的先天性肌性斜颈（CMT）诊断的客观方法。
方法：T1和T2加权成像，Q-Dixon,回顾性分析38例CMT患儿的T1-mappingMRI资料。在受影响侧的SCM的最大横截面积的水平处手动绘制感兴趣区域(ROI)。使用MaZda软件获得健康和受影响的SCM中ROI的T2WI序列的纹理特征。使用逻辑回归分析构建了基于肌肉纹理特征的影像组学诊断模型。通过苏木精和伊红染色计算脂肪浸润等级，和通过Masson染色的纤维化比率。分析MRI参数与病理指标的相关性。
结果：脂肪浸润等级与平均值呈正相关，标准偏差，和受影响的SCM的Q-dixon序列的最大值(相关系数，分别为0.65、0.59和0.58，P<0.05)。三个肌肉纹理特征-S(2,2)SumAverg，S(3,3)SumVarnc,选择T2WI极差-构建诊断模型。该模型对CMT具有显著的诊断价值(P<0.05)。多因素条件logistic回归模型的曲线下面积为0.828（95％置信区间0.735-0.922）；敏感性为0.684，特异性为0.868。
结论：使用T2WI肌肉纹理特征和MRI信号值构建的影像组学诊断模型似乎具有良好的诊断效率。Q-dixon序列可以反映CMT的脂肪浸润等级。
方法：T1和T2加权成像，Q-Dixon,回顾性分析38例CMT患儿的T1-mappingMRI资料。在受影响侧的SCM的最大横截面积的水平处手动绘制感兴趣区域(ROI)。使用MaZda软件获得健康和受影响的SCM中ROI的T2WI序列的纹理特征。使用逻辑回归分析构建了基于肌肉纹理特征的影像组学诊断模型。通过苏木精和伊红染色计算脂肪浸润等级，和通过Masson染色的纤维化比率。分析MRI参数与病理指标的相关性。
结果：脂肪浸润等级与平均值呈正相关，标准偏差，和受影响的SCM的Q-dixon序列的最大值(相关系数，分别为0.65、0.59和0.58，P<0.05)。三个肌肉纹理特征-S(2,2)SumAverg，S(3,3)SumVarnc,选择T2WI极差-构建诊断模型。该模型对CMT具有显著的诊断价值(P<0.05)。多因素条件logistic回归模型的曲线下面积为0.828（95％置信区间0.735-0.922）；敏感性为0.684，特异性为0.868。
结论：使用T2WI肌肉纹理特征和MRI信号值构建的影像组学诊断模型似乎具有良好的诊断效率。Q-dixon序列可以反映CMT的脂肪浸润等级。
