目的:通过分析非结核分枝杆菌(NTM)肺部疾病和肺结核(PTB)的CT影像组学特征,以区分其腔。
方法:对山东省胸科医院和齐鲁医院收治的NTM肺部疾病患者73例和69例PTB伴空洞患者进行回顾性分析。收集济南传染病医院20例NTM肺部疾病患者和20例PTB患者进行模型的外部验证。由2名经验丰富的放射科医师从胸部CT图像中获得379个腔作为感兴趣区域(ROI)。使用计算机生成的随机数,将80%的腔分配给训练集,将20%分配给验证集。利用慧英Radcloud平台提取的1409影像组学特征,对两种疾病的CT腔特征进行分析。使用方差分析(ANOVA)和最小绝对收缩和选择算子(LASSO)方法进行特征选择,和六个监督学习分类器(KNN,SVM,XGBoost,射频,LR,和DT模型)用于分析特征。
结果:通过方差阈值方法选择了29个最佳特征,K最好的方法,和套索算法。得到ROC曲线值。在训练集中,6个模型的AUC值均大于0.97,95%CI为0.95~1.00,敏感性大于0.92,特异性大于0.92.在验证集中,6个模型的AUC值均大于0.84,95%CI为0.76~1.00,敏感性大于0.79,特异性大于0.79.在外部验证集中,6个模型的AUC值均大于0.84,LR分类器精度最高,召回和F1得分,分别为0.92、0.94、0.93。
结论:从CT图像中提取的影像组学特征可以为区分NTM肺部疾病和PTB提供有效的证据,影像组学分析显示了比放射科医生更准确的诊断。在六个分类器中,LR分类器在识别两种疾病方面具有最佳性能。
OBJECTIVE: To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity.
METHODS: 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases\' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features.
RESULTS: 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93.
CONCLUSIONS: The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.