摘要
目的探讨基于ADC图的影像组学模型在急性缺血性脑卒中(AIS)患者缺血半暗带(IP)判断中的价值。方法回顾性分析南通市第一人民医院2014年1月至2019年10月发病在24 h内的大脑前循环AIS患者241例。所有患者均接受常规T_(1)WI、T_(2)WI、DWI及动态磁敏感对比增强磁共振灌注成像(DSC-PWI)。以PWI-DWI错配模型作为判断IP是否存在的金标准,将患者分为存在IP(即存在PWI-DWI错配)患者(84例),不存在IP(即不存在PWI-DWI错配)患者(157例)。分别由两名医师在AIS患者ADC图像上病灶最大层面对ADC低信号区域及周围区域进行ROI的勾画,将图像导入AK分析软件,进行影像组学特征提取。先采用组间相关系数筛选出一致性较高的特征,再采用最大相关最小冗余(mRMR)及最小绝对收缩与选择算子算法(Lasso)回归分析对特征进行筛选,然后用所选特征构建各自的影像组学评分模型。采用ROC曲线对模型的性能进行评估,并采用Delong检验对两组模型的ROC曲线下面积(AUC)进行比较。结果经过筛选,12个特征(LongRunLowGreyLevelEmphasis_angle135_offset7、LongRunLowGreyLevelEmphasis_AllDirection_offset7、GLCMEntropy_AllDirection_offset4_SD、GLCMEnergy_angle45_offset1、ColGE_W11B25_16、ColGE_W11B25_24、HaraEntropy、SurfaceVolumeRatio、Sphericity、Quantile0.025、uniformity、Percentile75)用于构建基于ADC图低信号区域的影像组学模型,训练集中AUC为0.900,灵敏度、特异度、准确度分别为84.5%、81.4%、83.4%;验证集中AUC为0.870,灵敏度、特异度、准确度分别为80.9%、84.0%、81.9%。11个特征(RunLengthNonuniformity_AllDirection_offset1_SD、ShortRunLowGreyLevelEmphasis_angle45_offset1、HighGreyLevelRunEmphasis_AllDirection_offset1_SD、ShortRunLowGreyLevelEmphasis_AllDirection_offset7、HaralickCorrelation_AllDirection_offset4_SD、ClusterShade_angle45_offset7、InverseDifferenceMoment_AllDirection_offset7_SD、ColGE_W3B20_0、sumAverage、SurfaceVolumeRatio、VolumeMM)�
Objective To investigate the value of ADC map-based radiomics model for identifying the ischemic penumbra(IP)in acute ischemic stroke(AIS).Methods From January 2014 to October 2019,data of 241 patients with AIS involving the anterior cerebral circulation within 24 h after stroke onset in the First People′s Hospital of Nantong City was analyzed retrospectively.All patients received routine T_(1)WI,T_(2)WI,DWI and dynamic susceptibility contrast-perfusion weighted imaging(DSC-PWI).Considering the PWI-DWI mismatch model as the gold standard for determining IP,patients were divided into the PWI-DWI mismatch(84 cases)and PWI-DWI non-mismatch(157 cases)groups.The ROI of the low signal area and the surrounding area was drawn by two doctors at the maximum level of the lesions on the ADC maps.Then the images were imported into AK analysis software to extract the features.Firstly,the inter-class correlation coefficient was used to screen out the features with high consistency,then the maximum relevance and minimum redundancy(mRMR)and least absolute shrinkage and selection operator(Lasso)regression analysis were used to screen the features.The selected features were used to construct their own radiomics model.ROC curve was used to evaluate the performance of the models,and Delong test was used to compare the area under the curve(AUC)of the two models.Results After screening,12 features(LongRunLowGreyLevelEmphasis_angle135_offset7,LongRunLowGreyLevelEmphasis_AllDirection_offset7,GLCMEntropy_AllDirection_offset4_SD,GLCMEnergy_angle45_offset1,ColGE_W11B25_16,ColGE_W11B25_24,HaraEntropy,SurfaceVolumeRatio,Sphericity,Quantile0.025,uniformity and Percentile75)were used to construct the radiomics model based on the low signal area of the ADC map.The area under the ROC curve in the training set was 0.900,and the sensitivity,specificity and accuracy were 84.5%,81.4%and 83.4%,respectively.The area under the ROC curve in the validation set was 0.870,and the sensitivity,specificity and accuracy were 80.9%,84.0%and 81.9%,respectively.
作者
张茹
朱政锜
朱丽
段绍峰
葛亚琼
王天乐
Zhang Ru;Zhu Zhengqi;Zhu Li;Duan Shaofeng;Ge Yaqiong;Wang Tianle(Department of Imaging,First People′s Hospital of Nantong City,Nantong 226001,China;General Electric Healthcare China,Shanghai 210000,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2021年第4期383-389,共7页
Chinese Journal of Radiology
基金
南通市民生科技项目(MS12018087,MS12018042)
江苏省卫健委科学基金项目(H2019057)。
关键词
卒中
磁共振成像
缺血半暗带
影像组学
Stroke
Magnetic resonance imaging
Ischemic penumbra
Radiomics
