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
闫成凤 1焦天宇 2曾庆师3
作者信息
- 1. 山东大学 山东 济南 250012;山东省淄博市职业病防治院放射科 山东 淄博 271016
- 2. 山东省公共卫生临床中心影像科 山东济南 250102
- 3. 山东大学 山东 济南 250012;山东第一医科大学第一附属医院(山东省千佛山医院)影像科 山东 济南 250012
- 折叠
摘要
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.
Abstract
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.
关键词
尘肺/模型构建/效能评价/体层摄影术,X线计算机Key words
Pneumoconiosis/Model development/Performance evaluation/Tomography,X-ray computed引用本文复制引用
出版年
2024
NETL
NSTL
万方数据