ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1630119
This article is part of the Research TopicThe Emerging Role of Biomarker Mutations in Early Stage Non-small Cell Lung Cancer (NSCLC) ManagementView all 5 articles
CT-based nomogram for predicting EGFR mutation status in ground-glass nodules of lung adenocarcinoma
Provisionally accepted
Li Li
Yantao Yang
Xinjie Zhou
Chen Zhou
Huang Qiubo
JIE ZHAOYaowu DuanWangcai Li
Hong YaoLiuyang Yang*
Lianhua Ye*- Yunnan Cancer Hospital, Kunming, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Purpose This study aimed to establish a nomogram based on computed tomography (CT) imaging characteristics to predict epidermal growth factor receptor (EGFR) mutation status in patients with ground-glass nodules (GGNs), thereby aiding medication decision-making. Materials and methods In total, 935 patients diagnosed with GGNs were enrolled. Patients undergoing surgery from August 2019 to December 2023 (n=709) comprised the training cohort, whereas those treated between January 2024 and March 2025 (n=226) constituted the validation cohort. Clinical parameters and radiological features were recorded for all participants. The training group underwent univariate and multivariate logistic regression analyses to identify significant predictive variables, subsequently facilitating the construction of a nomogram prediction model. The model's discrimination, calibration, and clinical applicability were validated in both patient cohorts. Results Multivariate logistic regression analysis revealed maximum nodule diameter, consolidation-to-tumor ratio (CTR), mean CT values, presence of air bronchogram signs, and vascular convergence signs as independent predictors of EGFR mutations. The resulting nomogram demonstrated robust predictive capability, achieving an area under the curve (AUC) of 0.87 (95% CI: 0.85–0.90) in the training group and 0.87 (95% CI: 0.82–0.92) in the validation group. Bootstrap internal validation yielded an AUC of 0.89, confirming strong model discrimination. Calibration plots and decision curve analysis further supported the model had a good calibration degree and clinical practicability across both groups. Conclusion The nomogram integrating maximum diameter, CTR, mean CT value, air bronchogram signs, and vascular convergence signs effectively predicts EGFR mutation status in GGNs, offering a valuable tool for clinical guidance and patient management strategies.
Keywords: Ground glass nodule, radiologic characteristic, Lung Adenocarcinoma, EGFR, Prediction model, nomogram
Received: 16 May 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Li, Yang, Zhou, Zhou, Qiubo, ZHAO, Duan, Li, Yao, Yang and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liuyang Yang, yangliuyang@fudan.edu.cn
Lianhua Ye, lhye1204@aliyun.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.