Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma

BMC Pulmonary Medicine

Research

Jianwei Chen¹  Xiumei Li²  Xiuying zheng²  Zewen Han²  Dairong Cao³  Chengxiu Zhang⁴ 

[1] Department of Radiology, Fujian Provincial Cancer Hospital, 350014, Fuzhou, Fujian, China;Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 350005, Fuzhou, Fujian, China;Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 350005, Fuzhou, Fujian, China;Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, 350212, Fuzhou, Fujian, China;Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, 350005, Fuzhou, Fujian, China;Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Shanghai200062, China;Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai200062, China;

关键词: Lung adenocarcinoma;    Radiomics;    Computed tomography;    T790M;   

DOI  :  10.1186/s12890-023-02609-y

 received in 2023-06-04, accepted in 2023-08-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images.MethodsThis retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram.ResultsThree demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619–0.843), 0.810(95%CI, 0.696–0.907), 0.841(95%CI, 0.743–0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130.ConclusionThe nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

Fig. 3

13690_2023_1170_Article_IEq13.gif

Fig. 1

13690_2023_1170_Article_IEq92.gif

Fig. 4

Fig. 6

Fig. 2