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首页|期刊导航|中国脑血管病杂志|基于非增强CT的影像组学识别动脉致密征阴性的大脑中动脉闭塞的初步研究

基于非增强CT的影像组学识别动脉致密征阴性的大脑中动脉闭塞的初步研究OA北大核心CSTPCD

Preliminary application of non-contrast CT radiomics for identification of middle cerebral artery occlusion with negative hyperdense artery sign

中文摘要英文摘要

目的 探究基于非增强CT(NCCT)的影像组学识别动脉致密征(HAS)阴性的急性单侧大脑中动脉闭塞(MCAO)的价值.方法 回顾性连续纳入2015年1月至2023年6月就诊于扬州大学附属医院卒中中心或急诊科,经血管成像[MR血管成像(MRA)或CT血管成像(CTA)或DSA]检查证实的急性单侧MCAO且NCCT表现为HAS阴性的患者80例;在NCCT图像上以每例患者的患侧MCAO段及正常侧对应节段血管作为感兴趣区,共提取108个影像组学特征.使用最小绝对收缩和选择算子(LASSO)筛选关键特征,构建并计算出影像组学评分,分别建立支持向量机(SVM)、光梯度提升机(LightGBM)、梯度提升(GradientBoosting)及自适应提升(AdaBoost)4种影像组学模型以预测MCAO.通过受试者工作特征(ROC)曲线评价不同模型的预测效能,并使用Delong检验方法进行各模型ROC间的比较,最后通过临床决策曲线分析(DCA)评估影像组学模型的应用价值.结果 基于160根血管的NCCT图像最终筛选出6个关键特征,分别为偏度、能量、灰度大小区域矩阵(GLSZM)-灰度不均匀性、GLSZM-低灰度区域强调、GLSZM-尺寸区非均匀性标准化、GLSZM-区域熵.SVM模型测试集预测急性单侧MCAO的ROC曲线下面积为0.688(95%CI:0.497~0.878),精确度为0.688;LightGBM模型测试集的曲线下面积为0.787(95%CI:0.620~0.955),精确度为 0.781;GradientBoosting 模型测试集的曲线下面积为 0.654(95%CI:0.457~0.852),精确度为0.688;AdaBoost模型测试集的曲线下面积为0.707(95%CI:0.515~0.899),精确度为0.750.Delong检验显示,LightGBM模型测试集与GradientBoosting模型测试集间的曲线下面积差异有统计学意义(P=0.040),余模型测试集间曲线下面积差异均无统计学意义(均P>0.05).DCA显示,LightGBM模型表现较好.结论 基于NCCT的影像组学对于识别HAS阴性的急性单侧MCAO具有较好的诊断效能,该结论尚需多中心、大样本研究进一步验证.

Objective To investigate the value of non-contrast CT(NCCT)-based radiomics for identifying acute unilateral middle cerebral artery occlusion(MCAO)with negative hyperdense artery sign(HAS).Methods All 80 patients with acute unilateral MCAO confirmed by angiography(MR angiography[MRA]or CT angiography[CTA]or DSA)and presenting with negative NCCT presentation for HAS were enrolled from January 2015 to June 2023 in the Emergency Department of Stroke Center of Affiliated Hospital of Yangzhou university.On the NCCT images,the occluded segment of the middle cerebral artery on the affected side of each case and the corresponding segment of the vessel on the normal side were used as the regions of interest,and a total of 108 radiomic features were extracted.The least absolute shrinkage and selection operator(LASSO)was used to screen the key features,construct and calculate the radiomics score,and four imaging histology models,support vector machine(SVM),light gradient boosting machine(LightGBM),GradientBoosting and adaptive boosting(AdaBoost),were built respectively to predict MCAO.Predictive performance was evaluated by the area under the receiver operating characteristic curves,and comparisons between the modeled receiver operating characteristic curves were made using the Delong test.Finally,the value of the application of radiological modeling was assessed by clinical decision curve analysis(DCA).Results The NCCT images based on 160 vessels were finally screened for 6 key features,including skewness,energy,gray level size zone matrix(GLSZM)-gray uneven,GLSZM-low gray area emphasis,GLSZM-size area non-uniform standardization,GLSZM-area entropy.The area under the curve(AUC)of the SVM-test was 0.688(95%CI 0.497-0.878)with an accuracy of 0.688;the AUC of the LightGBM-test was 0.787(95%CI 0.620-0.955)with an accuracy of 0.781;the AUC of the GradientBoosting-test was 0.654(95%CI 0.457-0.852)with an accuracy of 0.688;the AUC of the AdaBoost-test was 0.707(95%CI 0.515-0.899)with an accuracy of 0.750.The Delong test showed a statistically significant difference between LightGBM-test and GradientBoosting-test(P=0.040),and no statistically significant difference in performance between the remaining models(all P>0.05).DCA showed that the LightGBM-test performed better.Conclusion NCCT-based radiomics has good diagnostic efficacy for identifying acute unilateral MCAO with negative HAS,and this conclusion needs to be further verified by multi-center and large sample studies.

周怡;瞿航;赵义;王苇;郝慧婷;班淇琦;闫晓辉

225002 扬州大学附属医院影像科

大脑中动脉闭塞非增强CT动脉致密征阴性影像组学

Middle cerebral artery occlusionNon-contrast computed tomographyNegative arterial densification signRadiomics

《中国脑血管病杂志》 2024 (005)

297-305 / 9

江苏省重点研发项目(BE2021604)

10.3969/j.issn.1672-5921.2024.05.002

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