Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. (January 2020)

• Record Type: Journal Article
• Authors: Cong, Mengdi; Feng, Hui; Ren, Jia-Liang; Xu, Qian; Cong, Lining; Hou, Zhenzhou; Wang, Yuan-yuan; Shi, Gaofeng
• Is Part Of: Lung cancer. Volume 139(2020)
• Page Start: 73
• Page End: 79
• Publication Date: 2020-01
• Subjects: Non-small cell lung cancer -- Lymph nodes metastases -- Contrast-enhanced computed tomography -- Prediction model -- Radiomics; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2019.11.003
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: About 20 % pre-surgical stage IA NSCLC patients may have LN metastases. A radiomic model can predict LN metastases in pre-surgical stage IA NSCLC patients. The predictive radiomic model can help patients to get an appropriate treatment. Abstract: Objectives: To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. Methods: This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. Results: The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001). Conclusion: A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.