A model study of diagnosing mediastinal metastasis lymph nodes in non-small cell lung cancer based on CT radiomics
10.3760/cma.j.issn.0254-5098.2020.02.014
- VernacularTitle: 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
- Author:
Xue SHA
1
;
Guanzhong GONG
2
;
Qingtao QIU
2
;
Zhenjiang LI
2
;
Dengwang LI
1
;
Yong YIN
2
Author Information
1. Shandong Key Laboratory of Medical Physics and Image Processing & Shandong Provincial Engineering and Technical Center of Light Manipulations, School of Physics and Electronics, Shandong Normal University, Jinan 250358, China
2. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
- Publication Type:Journal Article
- Keywords:
Non-small cell lung cancer;
Computed tomography;
Radiomics;
Mediastinum lymph node
- From:
Chinese Journal of Radiological Medicine and Protection
2020;40(2):150-155
- CountryChina
- Language:Chinese
-
Abstract:
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.
