CT影像组学模型对Ⅰ期尘肺病的诊断价值

Diagnostic value of CT-based machine learning model for stage I pneumoconiosis

目的 探讨基于胸部CT影像组学特征构建的模型对Ⅰ期尘肺病的诊断价值。方法 选取202例诊断为尘肺Ⅰ期和199例健康体检者的临床及胸部CT资料,按照7:3的比例随机分为训练集组及验证集组,使用3D-slicer软件在CT肺窗图像上勾画感兴趣区(ROI)并提取特征,利用最小绝对收缩和选择算子(LASSO)算法对影像特征进行筛选,然后采用支持向量机(SVM)算法,建立CT组学模型并采用受试者工作特征曲线下面积(AUC)和决策曲线分析(DCA),评估预测模型的效能和临床实用性。结果 共提取出851个特征,最终筛选出9个特征建立CT影像组学模型,该模型训练集组的AUC为0.930(95%CI 0.901~0.963),验证集组的AUC为0.820(95%CI 0.742~0.895),DCA曲线显示该模型具有较好的净收益。结论 基于CT图像的影像组学模型能有效鉴别正常和Ⅰ期尘肺,对于Ⅰ期尘肺有重要的诊断价值。

Objective To investigate the diagnostic value of CT-based machine learning model for stage I pneumoconiosis.Methods We retrospectively collected clinical data and CT images of 202 patients diagnosed with stage I pneumoconiosis and 199 normal individuals from our hospital.We selected regions of interest(ROI)on each patient's CT lung window image using 3D-slicer software,and the patients were randomly split into training and validation cohorts in the ratio of 7:3.We used the least absolute shrinkage and selection operator algorithm to screen the features extracted from each case.The support vector machine algorithm was then used to build a CT-based machine learning model.The area under the ROC curve(AUC)and decision curve analysis(DCA)were applied to evaluate the performance of the model.Results 851 features were extracted from the CT im-ages and 9 features were filtered to build a CT-based machine learning model.The AUC of the model was 0.930(95%CI 0.901~0.963)in the training cohort and 0.820(95%CI 0.742~0.895)in the validation cohort.DCA showed the net benefit of the model.Conclusion CT-based machine learning model can effectively differentiate between normal and stage I pneumoconio-sis,which is of great diagnostic value for stage I pneumoconiosis.