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作 者:高希法 彭明洋 任军[2] 马跃虎[2] 陈国中 王同兴[2] GAO Xifa;PENG Mingyang;REN Jun(Department of Radiology,Affiliated Hospitial of Nanjing University of Chinese Medicine,Nanjing 210000,China)
机构地区:[1]南京中医药大学附属医院(江苏省中医院)放射科,210000 [2]南京医科大学附属南京医院(南京市第一医院)医学影像科
出 处:《临床神经病学杂志》2024年第3期161-165,共5页Journal of Clinical Neurology
基 金:国家自然科学基金项目(82001811);南京市卫生发展项目(YKK22111)。
摘 要:目的基于DWI图像开发一种急性脑卒中病变自动分割模型,并基于该模型和机器学习技术构建急性脑卒中1年复发预测模型。方法回顾性纳入2019年1月至2021年9月在南京市第一医院接受血管内治疗的急性缺血性卒中患者,根据1年内临床及影像学资料将患者分为无复发组和复发组。应用开发的EfficientNet-B0网络分割DWI图像上急性脑卒中病变并评估其分割效能。基于自动分割和人工勾画标签分别提取影像组学特征并应用支持向量机分类器构建急性脑卒中复发预测模型。采用Delong检验比较两个模型的差异。结果共268例患者纳入研究,无复发组161例,复发组107例。DWI病灶自动分割模型的敏感度为0.791、特异度为0.999、准确度为0.817、Dice相似系数为0.803。基于自动分割提取影像组学特征构建的急性脑卒中复发预测模型的曲线下面积(AUC)为0.878(95%CI:0.834~0.923)(敏感度:0.879、特异度:0.851);基于人工勾画标签提取影像组学特征构建的急性脑卒中复发预测模型的AUC为0.865(95%CI:0.819~0.911)(敏感度:0.860、特异度:0.832)。两个模型间预测效能无明显统计学差异(Z=0.526,P=0.599)。结论本研究提出的网络可很好的分割DWI急性脑卒中病灶,基于该模型提取影像组学特征构建的预测模型可很好的预测急性脑卒中复发。Objective To develop an automatic segmentation model of acute stroke lesions based on DWI images,and build a 1-year recurrence prediction model of acute stroke based on this model and machine learning technology.Methods The patients with acute ischemic stroke who received intravascular therapy in Nanjing First Hospital from January 2019 to September 2021 were retrospectively included.The patients were divided into recurrence group and non-recurrence group according to clinical and imaging data within 1 year.The developed EfficientNet-B0 network was applied to segment acute stroke lesions on DWI images and its segmentation efficiency was evaluated.Based on automatic segmentation and manual delineation of tags respectively,the radiomics were extracted and the support vector machine classifier was used to construct the prediction model of acute stroke recurrence.Delong test was used to compare the differences between the two models.Results A total of 268 patients were included in the study,161 in the non-recurrence group and 107 in the recurrence group.The sensitivity,specificity,accuracy and Dice similarity coefficient of DWI automatic lesion segmentation model was 0.791,0.999,0.817 and 0.803,respectively.The area under the curve(AUC)of the prediction model of acute stroke recurrence based on the radiomics of the automatic segmentation lesions was 0.878(95%CI:0.834-0.923)(sensitivity:0.879,specificity:0.851).The AUC of the prediction model of acute stroke recurrence based on the radiomics of of manually outlined tags was 0.865(95%CI:0.819-0.911)(sensitivity:0.860,specificity:0.832).There was no significant statistical difference between the two models(Z=0.526,P=0.599).Conclusion The network proposed in this study can well segment acute stroke lesions on DWI,and the prediction model based on the radiomics of this model can predict the recurrence of acute stroke very well.
分 类 号:R743.3[医药卫生—神经病学与精神病学]
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