基于全脑白质MR影像组学预测脑白质高信号进展及其相关危险因素分析

Prediction of white matter hyperintensities progression based on radiomics of whole-brain MRI:a study of risk factors

基于全脑白质的MR影像组学预测脑白质高信号(WMH)进展及其相关危险因素分析.方法回顾性分析2014年3月至2018年10月在浙江省人民医院行两次头颅MRI检查的152例患者资料,选取每例患者的基线T1WI图像使用统计参数图(SPM)12软件包切割出全脑白质并导入AK分析软件进行纹理特征提取和降维,最终使用最小绝对收缩与选择算子算法(LASSO)计算每例患者的影像组学标签值.依据改进Fazekas量表,将WMH进展患者分为任何区域进展(AWMH)、侧脑室旁高信号进展(PWMH)和深部高信号进展(DWMH)3种状态,使用独立样本t检验或者Mann-Whitney U检验比较各个状态WMH进展亚组和非进展亚组之间各临床因素和影像组学标签值差异,对各状态筛选出的有统计学差异的因素使用单因素逻辑斯回归筛选独立临床危险因素并使用多变量逻辑斯回归结合影像组学标签构建预测模型,使用ROC曲线评估模型诊断效能,同时分析独立危险因素的临床指标与构建影像组学标签纹理值的相关性.结果预测AWMH进展、PWMH进展和DWMH进展的影像组学模型的ROC曲线下面积(AUC)分别为0.818、0.778和0.824.其中年龄是AWMH进展和DWMH进展的独立危险因素[OR(95%CI)为4.776(2.152~10.601)和3.851(1.101~8.245);P<0.05],体质指数是DWMH的独立危险因素[OR(95%CI)为3.004(1.204~7.37);P<0.05],高脂血症是AWMH和PWMH的独立危险因素[OR(95%CI)为4.062(1.834~8.998)和3.549(1.666~7.563);P<0.05].在AWMH亚组中,SurfaceArea与年龄和低密度脂蛋白呈负相关(r=-0.401,-0.312),InverseDifferenceMoment_ALLDirection_offset1_SD与年龄呈负相关(r=-0.412).在PWMH亚组中,Compactness1与低密度脂蛋白呈负相关(r=-0.198),InverseDifferenceMoment_angle0_offset7与低密度脂蛋白呈正相关(r=0.252).在DWMH亚组中,LongRunEmphasis_ALLDirection_offset7与年龄呈负相关(r=-0.322),GLCMEntropy_angle0_offset4与年龄呈负相关(r=-0.278).GLCMEntropy_AllDirection_offset4与体质指数呈负相关(r=-0.514).结论基于全脑白质影像组学可以预测WMH进展并确立高风险人群的危险因素,可以为常规MR预测WMH进展提供早期额外信息.

Objective To explore the risk factors of predicting white matter hyperintensities progression based on radiomics of MRI of whole-brain white matter.Methods The imaging and clinical data of 152 patients with white matter hyperintensities admitted to Zhejiang People′s Hospital from March 2014 to October 2018 were retrospectively analyzed.The whole brain white matter on baseline T1WI images of each patient were segmented by SPM12 software package,and images of white matter were imported into AK software for texture feature extraction and dimensionality reduction.At last,least absolute shrinkage and selection operator(LASSO)was used to calculate the score of radiomics signature of each patient.According to the improved Fazekas scale,patients with WMH progression were divided into three groups:any white matter hyperintensities(AWMH),periventricular white matter hyperintensities(PWMH)and deep white matter hyperintensities(DWMH).Statistical differences of clinical factors and radiomics signature between WMH progression subgroups and non-progression subgroups were compared with independent sample t test or Mann-Whitney U test,and Univariate logistic regression was used to select independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models,which was evaluated by ROC curve.And the correlation between the clinical indicators of independent risk factors and the texture feature of radiomics signature was analyzed.Results The efficacy of the model for the detection for AWMH progression,PWMH progression and DWMH progression was 0.818,0.778 and 0.824,respectively.Age is an independent risk factor for AWMH progression and DWMH progression[OR(95%CI),4.776(2.152-10.601)vs.3.851(1.101-8.245);P<0.05].BMI is an independent risk factor for DWMH[OR(95%CI),3.004(1.204-7.370);P<0.05],and hyperlipidemia is an independent risk factor for AWMH and PWMH[OR(95%CI),4.062(1.834-8.998)vs.3.549(1.666-7.563);P<0.05].In AWMH subgroup,Surface Area was negatively correlated with age and low density lipoprotein(LDL)(r=-0.401,-0.312),and Inverse Difference Moment_ALLDirection_offset 1_SD was negatively correlated with age(r=-0.412).In PWMH subgroup,Compactness 1 was negatively correlated with LDL(r=-0.198),and Inverse Difference Moment_angle 0_offset 7 was positively correlated with LDL(r=0.252).In DWMH subgroup,LongRunEmphasis_ALLDirection_offset 7 was negatively correlated with age(r=-0.322),and GLCMEntropy_angle0_offset 4 was negatively correlated with age(r=-0.278).GLCMEntropy_AllDirection_offset4 was negatively correlated with body mass index(r=-0.514).Conclusion Radiomics based on whole-brain white matter MR imaging can predict WMH progression and identify the risk factors in high-risk populations,thus providing early additional information to conventional magnetic resonance imaging to predict WMH progression.