Application Value of CT Radiomic Nomogram in Predicting T790M Mutation of Lung Adenocarcinoma

Overview

Journal BMC Pulm Med

Publisher Biomed Central

Specialty Pulmonary Medicine

Date 2023 Sep 11

PMID 37697337

Authors

Xiumei Li

Jianwei Chen

Chengxiu Zhang

Zewen Han

Xiuying Zheng

Dairong Cao

Affiliations

Soon will be listed here.

Abstract

Background: The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images.

Methods: This 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.

Results: Three 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.

Conclusion: The 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.

Citing Articles

The predictive value of the change of the number of pixels under different CT value intervals in the CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions.

Zhou J, Yu B, Guo P, Wang S BMC Pulm Med. 2023; 23(1):426.

PMID: 37924039 PMC: 10623708. DOI: 10.1186/s12890-023-02732-w.

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