中国防痨杂志2024，Vol.46Issue(9)：1042-1049,8.DOI:10.19982/j.issn.1000-6621.20240095
基于CT图像结合放射组学和语义特征的机器学习模型诊断非结核分枝杆菌肺病和肺结核的研究
A machine learning model based on CT images combined with radiomics and semantic features for diagnosis of nontuberculous mycobacterium lung disease and pulmonary tuberculosis
摘要
Abstract
Objective:To explore a machine learning model based on chest CT images for differential diagnosis of nontuberculous mycobacterium lung disease(NTM-LD)and pulmonary tuberculosis(PTB).Methods:Chest CT images of 120 patients(NTM-LD)and 120 patients(PTB)were retrospectively collected in Tianjin Haihe Hospital from January 2017 to December 2020.168 cases(70％)were randomly selected as the training set,and 72 cases(30％)were selected as the testing set.Chest CT images of 25 patients(NTM-LD)and 25 patients(PTB)from Xi'an Chest Hospital were collected as an external validation set.A total of 12 radiologist semantic features and 2107 radiomic features were extracted from chest CT images,and 40 radiomic features were retained through feature dimensionality reduction.Three distinct machine learning classification models were constructed utilizing the Support Vector Machines(SVM)algorithm.These models encompass a semantic model,a radiomics model,and a hybrid radiomics-semantic model.The diagnostic performance of the three models were evaluated by the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The statistical significance of differences between the three models were compared by DeLong test.Results:In the testing set,the AUC of radiomics-semantic model,radiomics model and semantic model were 0.9853,0.9282,and 0.7901,respectively.There were statistically significant differences between semantic model and radiomics-semantic model,as well as between semantic model and radiomics model(Z=2.759,P=0.006;Z=2.230,P=0.026).However,there was no statistically significant difference between radiomics-semantic model and radiomics model(Z=0.761,P=0.502).In the external validation set,the AUC of radiomics-semantic model,radiomics model and semantic model were 0.9216,0.9024 and 0.7624,respectively.There was a statistically significant difference between radiomics-semantic model and semantic model(Z=2.126,P=0.034).However,there was no statistically significant difference between radiomics-semantic model and radiomics model(Z=0.368,P=0.713).Conclusion:Compared with semantic model,the machine learning model combining radiomics and semantic features showed an excellent diagnostic efficiency and great clinical application value in distinguishing NTM-LD and PTB.Although its performance improvement was not significant compared to radiomics model.关键词
结核,肺/分枝杆菌感染/诊断,鉴别/体层摄影术,X线计算机/影像组学Key words
Tuberculosis,pulmonary/Mycobacterium infections/Diagnosis,differential/Tomography,X-ray computed/Radiomics分类
医药卫生引用本文复制引用
仲玲珊,王莉,张硕,李楠,杨晴媛,丁文龙,陈星枝,黄陈翠,邢志珩..基于CT图像结合放射组学和语义特征的机器学习模型诊断非结核分枝杆菌肺病和肺结核的研究[J].中国防痨杂志,2024,46(9):1042-1049,8.基金项目
Tianjin Science and Technology Plan Project-NTM Diagnostic Application Research Based on CT Annotated Big Data Resources(21JCYBJC00510) （21JCYBJC00510）
Tianjin Haihe Hospital Science and Technology Fund Project-Al Assisted NTM-LD Diagnosis Application Research Based on CT Annotated Big Data Resources(HHYY-202007) （HHYY-202007）
Tianjin Key Medical Discipline(Specialty)Construction Project(TJYXZDXK-067C （Specialty）
TJYXZDXK-063B) 天津市科技计划项目-基于CT标注大数据资源NTM诊断应用研究(21JCYBJC00510) （21JCYBJC00510）
天津市海河医院科技基金项目-基于CT标注大数据资源的AI辅助NTM-LD诊断应用研究(HHYY-202007) （HHYY-202007）
天津市医学重点学科(专科)建设项目资助(TJYXZDXK-067C （专科）
TJYXZDXK-063B) （）
