Correlation between tumor mutational burden and CT radiographic features in EGFR exon 19 deletion-mutated lung...

As the predominant subtype of non-small cell lung cancer, lung adenocarcinoma exhibits a pathogenesis closely associated with molecular characteristics. Tumor mutational burden (TMB) has emerged as a critical biomarker for predicting responses to immunotherapy. Although computed tomography (CT) imaging is widely utilized in diagnosing lung adenocarcinoma and its morphological features may reflect genomic attributes, the precise relationship between TMB and CT radiographic features remains inadequately elucidated.

This study aimed to investigate the correlation between TMB and CT radiographic features in lung adenocarcinoma and to evaluate the diagnostic value of these features in identifying high TMB, thereby providing a non-invasive approach for TMB assessment.

A total of 156 treatment-naïve lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) exon 19 deletion mutations, admitted to Funan County People's Hospital between January 2022 and August 2025, were enrolled. Based on TMB levels, patients were stratified into high-TMB (TMB ≥ 10 mut/Mb, n = 52) and low-TMB (TMB < 10 mut/Mb, n = 104) groups. All participants underwent non-contrast and contrast-enhanced chest CT scans, and TMB was quantified via next-generation sequencing (NGS). Two experienced radiologists, blinded to TMB status, independently evaluated CT morphological features, including maximum tumor diameter, spiculation, lobulation, pleural indentation, cavity formation, vascular convergence, and mediastinal lymph node enlargement.

The high-TMB group exhibited a significantly larger maximum tumor diameter compared to the low-TMB group (t = 3.456, p < 0.05). The incidences of spiculation, lobulation, and vascular convergence were also significantly higher in the high-TMB group (spiculation: χ² = 5.678, p = 0.017; lobulation: χ² = 4.567, p = 0.033; vascular convergence: χ² = 4.789, p = 0.029). pleural indentation showed a borderline intergroup difference (χ² = 3.289, p = 0.07). Spearman correlation analysis revealed positive correlations between TMB levels and maximum tumor diameter, spiculation, lobulation, and vascular convergence (ρ = 0.312, 0.234, 0.198, 0.216; p < 0.05). Univariate logistic regression identified these features as significant predictors of high TMB (Wald = 11.678, 5.672, 4.543, 4.752; p < 0.05), and multivariate analysis confirmed their independent predictive value (Wald = 10.175, 5.231, 4.134, 4.365; p < 0.05). In diagnostic performance evaluation, a combined model of these features achieved an area under the curve (AUC) of 0.829 for predicting high TMB.

CT-based radiological features are significantly correlated with TMB status in lung adenocarcinoma. A composite model incorporating these features demonstrates high diagnostic accuracy for identifying high TMB, offering a valuable non-invasive tool for guiding personalized treatment strategies.