Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas

Insights into Imaging

Original Article

Hui Feng¹  Lijia Wang¹  Xiaojia Cai¹  Qian Xu¹  Gaofeng Shi¹  Jialiang Ren² 

[1] Department of Radiology, The Fourth Hospital of Hebei Medical University, No. 12 of Health Road, 050011, Shijiazhuang, China;GE Healthcare China, 100176, Beijing, China;

关键词: Radiomics;    Lung adenocarcinoma;    Ground glass opacity;    Tomography (X-Ray Computed);    Pulmonary nodules;   

DOI  :  10.1186/s13244-022-01363-9

 received in 2022-07-11, accepted in 2022-12-28,  发布年份 2022

来源: Springer

【 摘 要 】

ObjectiveThe purpose of the study is to investigate the performance of radiomics-based analysis in prediction of pure ground-glass nodule (pGGN) lung adenocarcinomas invasiveness using thin-section computed tomography images.MethodsA total of 382 patients surgically resected single pGGN and pathologically confirmed were enrolled in the retrospective study. The pGGN cases were divided into two groups: the noninvasive group and the invasive adenocarcinoma (IAC) group. 330 patients were randomly assigned to the training and testing cohorts with a ratio of 7:3 (245 noninvasive lesions, 85 IAC lesions), while 52 patients (30 noninvasive lesions, 22 IAC lesions) were assigned to the external validation cohort. Amodel, radiomics model, and combined clinical-radiographic-radiomic model were built using the LASSO and multivariate backward stepwise regression analysis on the basis of the selectedand radiomics features. The area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate and compare the model performance for invasiveness discrimination among the three cohorts.ResultsThree clinical-radiographic features (including age, gender and the mean CT value) and three radiomics features were selected for model building. The combined model and radiomics model performed better than the clinical-radiographic model. The AUCs of the combined model in the training, testing, and validation cohorts were 0.856, 0.859, and 0.765, respectively. The DCA demonstrated the radiomics signatures incorporating clinical-radiographic feature was clinically useful in predicting pGGN invasiveness.ConclusionsThe proposed radiomics-based analysis incorporating the clinical-radiographic feature could accurately predict pGGN invasiveness, providing a noninvasive biomarker for the individualized and precise medical treatment of patients.

【 授权许可】

CC BY   
© The Author(s) 2023

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