| 沙雪,巩贯忠,仇清涛,等.基于CT影像组学鉴别非小细胞肺癌纵隔转移性淋巴结的模型研究[J].中华放射医学与防护杂志,2020,40(2):150-155.Sha Xue,Gong Guanzhong,Qiu Qingtao,et al.A model study of diagnosing mediastinal metastasis lymph nodes in non-small cell lung cancer based on CT radiomics[J].Chin J Radiol Med Prot,2020,40(2):150-155 |
| 基于CT影像组学鉴别非小细胞肺癌纵隔转移性淋巴结的模型研究 |
| A model study of diagnosing mediastinal metastasis lymph nodes in non-small cell lung cancer based on CT radiomics |
| 投稿时间：2019-05-08 |
| DOI：10.3760/cma.j.issn.0254-5098.2020.02.014 |
| 中文关键词: 非小细胞肺癌 CT 影像组学 纵隔淋巴结 |
| 英文关键词:Non-small cell lung cancer Computed tomography Radiomics Mediastinum lymph node |
| 基金项目:山东省重点研发计划（2018GSF118006） |
|
| 摘要点击次数: 3784 |
| 全文下载次数: 1719 |
| 中文摘要: |
| 目的 建立不同CT扫描时相图像鉴别非小细胞肺癌（non-small cell lung cancer，NSCLC）纵隔淋巴结的影像组学模型，并探讨不同模型的诊断效能。方法 回顾性分析86例NSCLC患者的术前CT图像，所有患者均行平扫期、动脉期和静脉期CT扫描。选取231枚纵隔淋巴结为研究对象，将2015年1月-2017年6月入组的163枚淋巴结作为训练组，2017年7月-2018年6月入组的68枚淋巴结作为验证组。分别在三时相图像上勾画感兴趣区域（regions of interest，ROI），每个ROI提取841个影像特征，使用LASSO算法筛选特征，基于各时相CT影像组学特征和两不同时相CT影像组学特征的差值建立模型。比较不同模型的受试者工作特征曲线（receiver operating characteristic，ROC）下面积（AUC值）、敏感性、特异性、准确度、阳性预测值和阴性预测值的差异。结果 共建立6个模型，其AUC值均>0.800。平扫期CT模型具有最优的鉴别效能，其训练组的AUC值、特异性、准确度、阳性预测值分别为0.926、0.860、0.871、0.906，验证组的AUC值、特异性、准确度、阳性预测值分别为0.925、0.769、0.882、0.870，均高于其他模型。平扫和静脉期CT图像联合动脉期CT图像之后，训练组的敏感性、阴性预测值分别从0.879、0.821和0.919、0.789提高到0.949、0.878和0.979、0.900。结论 CT各时相影像组学模型均可用于辅助临床诊断淋巴结。平扫CT影像组学模型的AUC值最高，而联合动脉期CT图像可提高模型的敏感度及阴性预测值。 |
| 英文摘要: |
| Objective To establish radiomics models based on different CT scaning phases to distinguish mediastinal metastatic lymph nodes in NSCLC and to explore the diagnostic efficacy of these models. Methods The CT images of 86 preoperative patients with NSCLC who were performed both plain and enhanced CT scans were analyzed retrospectively. The 231 mediastinal lymph nodes were enrolled in this study which were divided into two independent cohorts:163 lymph nodes enrolled from January 2015 to June 2017 constituted the training cohort, and 68 lymph nodes enrolled from July 2017 to June 2018 constituted the validation cohort. The regions of interest (ROIs) were delineated on plain scan phase, arterial phase and venous phase CT images respectively, and 841 features were extracted from each ROI. LASSO-logistic regression analysis was used to select features and develop models. The area under the ROC curve (AUC value), sensitivity, specificity, accuracy, positive predictive value and negative predictive value of different models for distinguishing metastatic lymph nodes were compared. Results A total of 6 models were established, and the AUC values were all greater than 0.800. The plain CT model yielded the highest AUC, specificity, accuracy and positive predictive value with 0.926,0.860,0.871,0.906 in the training cohort and 0.925,0.769,0.882,0.870 in the validation cohort. When plain and venous phase CT images were combined with arterial phase CT images, the sensitivity and negative predictive value of the models increased from 0.879, 0.821 and 0.919, 0.789 to 0.949, 0.878 and 0.979, 0.900 respectively. Conclusions The CT radiomics model could be used to assist the clinical diagnosis of lymph nodes. The AUC value of the model based on plain scanning was the highest, while the sensitivity and negative predictive value of the model could be improved by combining the arterial phase CT images. |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|