中国血吸虫病防治杂志 ›› 2022, Vol. 34 ›› Issue (5): 500-.

• 论著 • 上一篇    下一篇

基于超声影像组学建立肝棘球蚴病分型模型的 可行性研究

张旭辉1△,索朗拉姆2△,邱甲军3,蒋静文3,殷晋3,王俊人3,王逸非1,李永忠1,蔡迪明1*   

  1. 1四川大学华西医院超声医学科(四川 成都 610041);2西藏自治区疾病预防控制中心;3四川大学华西医院华西生物医学大数据中心
  • 出版日期:2022-11-23 发布日期:2022-11-23
  • 作者简介:张旭辉,男,硕士研究生。研究方向:肝棘球蚴病诊治 索朗拉姆,女,本科,主管医师。研究方向:肝棘球蚴病防治
  • 基金资助:
    国家卫生健康委员会包虫病防治研究重点实验室开放课题(2021WZK1002)

Feasibility of ultrasound radiomics⁃based models for classification of hepatic echinococcosis

ZHANG Xu⁃hui1△, SUOLANG La⁃mu2△, QIU Jia⁃jun3, JIANG Jing⁃wen3, YIN Jin3, WANG Jun⁃ren3, WANG Yi⁃fei1, LI Yong⁃zhong1, CAI Di⁃ming1*   

  1. 1 Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; 2 Tibet Autonomous Region Center for Disease Control and Prevention, China; 3 West China Biomedical Big Data Center, West China Hospital, Sichuan University, China
  • Online:2022-11-23 Published:2022-11-23

摘要: 目的 探究基于超声影像组学构建肝棘球蚴病分型模型的可行性,从而为肝棘球蚴病精准超声诊断提供参考依据。方法 回顾性收集2014年10月于四川省甘孜藏族自治州石渠县采集的200例肝棘球蚴病患者超声声像图,勾画肝棘球蚴病病灶感兴趣区域。采用25种方法提取肝棘球蚴病影像组学特征,应用预选方式与最小绝对收缩和选择算法进行特征筛选,按7∶3比例将图像根据病灶类型随机划分为训练集与独立测试集。基于内核逻辑回归(kernel logistic regression, KLR)与高斯核函数型支持向量机(medium Gaussian support vector machine, MGSVM)两种分类器构建肝棘球蚴病分型的机器学习模型,绘制受试者工作特征(receiver operating characteristic, ROC)曲线,计算构建的机器模型用于肝棘球蚴病分型的敏感度、特异度及曲线下面积(area under the curve, AUC)。结果 25种方法累计提取5 005个棘球蚴病患者超声影像组学特征,经特征选择筛选出36个最优影像组学特征,并在此基础上建立了KLR和MGSVM两种机器学习模型。ROC曲线分析显示,MGSVM模型在训练集中用于肝棘球蚴病分型效果更优,敏感度、特异度和AUC分别为0.82、0.78和0.88,而KLR模型在独立测试集中表现更佳,敏感度、特异度和AUC分别为0.82、0.72和0.86。结论 基于超声影像组学的机器学习模型可用于肝棘球蚴病分型。  

关键词: 肝棘球蚴病, 分型, 超声图像, 影像组学, 机器学习

Abstract: Objective To investigate the feasibility of establishment of ultrasound radiomics⁃based models for classification of hepatic echinococcosis, so as to provide insights into precision ultrasound diagnosis of hepatic echinococcosis. Methods The ultrasonographic images were retrospectively collected from 200 patients with hepatic echinococcosis in Shiqu County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province in October 2014, and the regions of interest were plotted in ultrasonographic images of hepatic echinococcosis lesions. The ultrasound radiomics features of hepatic echinococcosis were extracted with 25 methods, and screened using pre⁃selection and the least absolute shrinkage and selection operator. Then, all ultrasonographic images were randomly assigned into the training and independent test sets according to the type of lesions at a ratio of 7∶3. Machine learning models for classification of hepatic echinococcosis were created based on two classifiers, including kernel logistic regression (KLR) and medium Gaussian support vector machine (MGSVM). The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity and areas under the curves (AUC) of the created machine learning models for classification of hepatic echinococcosis were calculated. Results A total of 5 005 ultrasound radiomics features were extracted from 200 patients with hepatic echinococcosis using 25 methods, and 36 optimal radiomics features were screened through feature selection, based on which two machine learning models were created, including KLR and MGSVM. ROC curve analysis showed that MGSVM presented a higher efficacy for hepatic echinococcosis classification than KLR in the training set, with a sensitivity of 0.82, a specificity of 0.78 and AUC of 0.88, while KLR presented a higher efficacy for hepatic echinococcosis classification than MGSVM in the independent test set, with a sensitivity of 0.82, a specificity of 0.72 and AUC of 0.86, respectively. Conclusion Ultrasound radiomics⁃based machine learning models are feasible for hepatic echinococcosis classification.

Key words: Hepatic echinococcosis, Classification, Ultrasonographic image, Radiomics, Machine learning

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