Radiomics Research
A CT-based radiomics analysis for clinical staging of non-small cell lung cancer
Lan He, Yanqi Huang, Zelan Ma, Cuishan Liang, Xiaomei Huang, Zixuan Cheng, Changhong Liang, Zaiyi Liu
Published 2017-12-10
Cite as Chin J Radiol, 2017, 51(12): 906-911. DOI: 10.3760/cma.j.issn.1005-1201.2017.12.004
Abstract
ObjectiveTo develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC).
MethodsIn this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014. The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014. All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage (PTNM) of patients with NSCLC were determined by the intraoperative and postoperative pathological findings, and were divided into early stage (Ⅰ,Ⅱ stage) and advanced stage (Ⅲ,Ⅳ stage). A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis.
ResultsThe discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval (CI):0.709 to 0.721] in the primary dataset and a AUC of 0.724(95%CI:0.717 to 0.731) in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen (CEA) level, and cytokeratin 19 fragment (CYFRA21-1) level were showed independently associated with the stage (Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage) of NSCLC. The prediction model showed good discrimination in both primary dataset (AUC=0.787, 95%CI:0.781 to 0.793;sensitivity=73.4%, specificity=72.2%,positive predictive value=0.707,negative predictive value=0.868) and independent validation dataset (AUC=0.777, 95%CI:0.771 to 0.783,sensitivity=91.3%,specificity=67.3%,positive predictive value=0.607, negative predictive value=0.946).
ConclusionThe radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage (Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage) of patients with NSCLC.
Key words:
Lung neoplasms; Tomography,X-ray computed; Radiomics
Contributor Information
Lan He
School of Medicine, South China University of Technology, Guangzhou 510006, China
Yanqi Huang
Zelan Ma
Cuishan Liang
Xiaomei Huang
Zixuan Cheng
Changhong Liang
Zaiyi Liu
