Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

• Published in: BMC medical imaging Vol. 20; no. 1; pp. 12 - 8
• Main Authors: Sha, Xue, Gong, Guanzhong, Qiu, Qingtao, Duan, Jinghao, Li, Dengwang, Yin, Yong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.02.2020; BioMed Central; BMC
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Algorithms; Cancer; Cancer metastasis; Cancer therapies; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; CAT scans; Chemotherapy; Computed tomography; Diagnostic imaging; Discrimination; Feature extraction; Female; Humans; Lung cancer; Lung Neoplasms - diagnostic imaging; Lymph nodes; Lymphatic Metastasis - diagnostic imaging; Lymphatic system; Male; Mediastinal lymph nodes; Mediastinum - diagnostic imaging; Medical imaging; Medical records; Medical research; Metastases; Metastasis; Middle Aged; Multivariate Analysis; Non-small cell lung cancer; Non-small cell lung carcinoma; Phases; Radiation therapy; Radiographic Image Interpretation, Computer-Assisted; Radiomic model; Radiomics; Regression analysis; Retrospective Studies; ROC Curve; Sensitivity; Sensitivity and Specificity; Small cell lung cancer; Small cell lung carcinoma; Software; Statistical analysis; Tomography; Tomography, X-Ray Computed; Training; Tumors; Mediastinal lymph nodes; Computed tomography; Radiomic model; Non-small cell lung cancer
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-020-0416-3

We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.