A CT-based radiomics analysis for clinical staging of non-small cell lung cancer
10.3760/cma.j.issn.1005-1201.2017.12.004
- VernacularTitle:CT影像组学在非小细胞肺癌临床分期中的价值
- Author:
Lan HE
1
,
2
;
广东省医学科学院广东省人民医院放射科
;
Yanqi HUANG
;
Zelan MA
;
Cuishan LIANG
;
Xiaomei HUANG
;
Zixuan CHENG
;
Changhong LIANG
;
Zaiyi LIU
Author Information
1. 510006广州,华南理工大学医学院
2. 广东省医学科学院广东省人民医院放射科
- Keywords:
Lung neoplasms;
Tomography;
X-ray computed;
Radiomics
- From:
Chinese Journal of Radiology
2017;51(12):906-911
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC). Methods In 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,Ⅱstage)and advanced stage(Ⅲ,Ⅳstage).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. Results The 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). Conclusion The 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.
