Multimodal ultrasonography for predicting epidermal growth factor receptor mutation in subpleural non-small cell lung carcinoma

Objective: Currently precise target treatment based on gene status significantly improved the outcome for patients with non-small cell lung cancer (NSCLC) and epidermal growth factor receptor (EGFR) was the most important gene. We aimed to develop a multimodal ultrasound model for predicting EGFR mutation status in patients with subpleural NSCLC, to provide important information for precise target treatment. Methods: 75 patients with pathologically confirmed NSCLC were included in this retrospective study. Patients were divided into two groups based on EGFR mutation status: wild-type (n=57) and mutant (n=18). The clinical characteristics (C), conventional ultrasound (US) features, contrast-enhanced ultrasound (CEUS) characteristics, and time-intensity curve (TIC) parameters of the lung lesions were analyzed and compared between the two groups. Univariate and multivariate logistic regression determined independent predictors of EGFR mutations. Two predictive models were constructed: a C+ US model and a FULL model. Both were presented using nomograms. Receiver operating characteristic and calibration curves evaluated predictive performance of two models, while decision curve analysis (DCA) assessed clinical utility. Results: Multivariate analysis identified smoking status, lesion boundaries, and air bronchogram as predictors in the C + US model. The FULL model identified lesion boundaries and air bronchogram on US, enhancement intensity of lesions and internal necrosis on CEUS and RT (rise time) from TIC as predictors. The C + US model achieved an AUC of 0.843, and the FULL model achieved 0.939. DCA confirmed substantial net clinical benefits. Conclusion: The models developed in this study enabled patients who are unable to undergo invasive procedures to predict EGFR mutation status noninvasively. These findings provided an ultrasound-based diagnostic reference to support clinician decision-making and personalized treatment planning.