Noninvasive prediction of classic EGFR-activating mutations in non-small cell lung cancer using a combined spectral computed tomography model: an auxiliary tool for preoperative stratification of candidates for EGFR-TKI therapy
- 1Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian Province China
Abstract
Background: EGFR genotyping is essential for EGFR-TKI therapy in non-small cell lung cancer (NSCLC), but invasive biopsy has limitations. Noninvasive preoperative stratification tools are urgently needed.
Methods: We retrospectively enrolled 129 NSCLC patients to develop and internally validate a combined model integrating clinical features, conventional CT findings, and dual-layer spectral CT parameters for predicting classic EGFR-activating mutations. Performance was evaluated by ROC, DeLong test, DCA, and 1000-iteration bootstrap validation.
Results: The combined model achieved an AUC of 0.805, significantly outperforming the clinical-conventional CT model (AUC = 0.642, P < 0.001). Venous-phase normalized iodine concentration (NIC) was the only independent predictor (OR = 2.578, P = 0.018). The model showed robust performance in the lung adenocarcinoma subgroup (AUC = 0.810) with negligible overfitting.
Conclusions: This noninvasive spectral CT model provides a reliable, easy-to-implement auxiliary tool for preoperative EGFR mutation stratification to optimize EGFR-TKI therapy decisions.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12890-026-04575-7.
Related
- Prediction of incidental obturator hernia using preoperative computed tomography: the distance between the obturator internus muscle and the pubic bone
- Multimodal preoperative risk stratification of pancreatic mass lesions using clinical variables, imaging radiomics and plasma cfDNA methylation: protocol for an ambispective multicentre diagnostic prediction model study
- An interpretable computed tomography radiomics-based machine learning model for preoperative prediction of complicated appendicitis in children.
- Noninvasive tomography of tumor progression via dynamical changes of T-cells in surrounding microenvironment
- Development and Internal Validation of a Preoperative Risk Stratification Model for Difficult Laparoscopic Cholecystectomy in Acute Cholecystitis
BibTeX
@article{zhan2026noninvasive,
title = {{Noninvasive prediction of classic EGFR-activating mutations in non-small cell lung cancer using a combined spectral computed tomography model: an auxiliary tool for preoperative stratification of candidates for EGFR-TKI therapy}},
author = {Qianqian Zhan and Hui Lin and Dandan Lin and Xueyan Liao},
journal = {BMC Pulmonary Medicine},
year = {2026},
month = aug,
doi = {10.1186/s12890-026-04575-7},
url = {https://doi.org/10.1186/s12890-026-04575-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Noninvasive prediction of classic EGFR-activating mutations in non-small cell lung cancer using a combined spectral computed tomography model: an auxiliary tool for preoperative stratification of candidates for EGFR-TKI therapy
AU - Qianqian Zhan
AU - Hui Lin
AU - Dandan Lin
AU - Xueyan Liao
PY - 2026
DA - 2026/08/11
JO - BMC Pulmonary Medicine
DO - 10.1186/s12890-026-04575-7
UR - https://doi.org/10.1186/s12890-026-04575-7
AB - Background: EGFR genotyping is essential for EGFR-TKI therapy in non-small cell lung cancer (NSCLC), but invasive biopsy has limitations. Noninvasive preoperative stratification tools are urgently needed. Methods: We retrospectively enrolled 129 NSCLC patients to develop and internally validate a combined model integrating clinical features, conventional CT findings, and dual-layer spectral CT parameters for predicting classic EGFR-activating mutations. Performance was evaluated by ROC, DeLong test, DCA, and 1000-iteration bootstrap validation. Results: The combined model achieved an AUC of 0.805, significantly outperforming the clinical-conventional CT model (AUC = 0.642, P < 0.001). Venous-phase normalized iodine concentration (NIC) was the only independent predictor (OR = 2.578, P = 0.018). The model showed robust performance in the lung adenocarcinoma subgroup (AUC = 0.810) with negligible overfitting. Conclusions: This noninvasive spectral CT model provides a reliable, easy-to-implement auxiliary tool for preoperative EGFR mutation stratification to optimize EGFR-TKI therapy decisions. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12890-026-04575-7.
SN - 1471-2466
ER -