Correlation Between Tumor Mutational Burden and CT Radiographic Features in Lung Adenocarcinoma: A Diagnostic Accuracy Study

Correlation Between Tumor Mutational Burden and CT Radiographic Features in Lung Adenocarcinoma: A Diagnostic Accuracy Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation Between Tumor Mutational Burden and CT Radiographic Features in Lung Adenocarcinoma: A Diagnostic Accuracy Study Nie Qing, Shouyu Wang, Changzhi Liu, Hongli Leng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8089262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: 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-based radiological characteristics remains inadequately elucidated. Objective: This study aimed to investigate the correlation between TMB and CT imaging 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. Methods: 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 retraction, cavity formation, vascular convergence, and mediastinal lymph node enlargement. Results: 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 (χ²=5.678, 4.567, 4.789; P<0.05). Pleural retraction 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. Conclusion: 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. Tumor mutational burden Lung adenocarcinoma Computed tomography Radiological features Diagnostic accuracy Figures Figure 1 Figure 2 1 Introduction Lung adenocarcinoma, as the predominant subtype of non-small cell lung cancer (NSCLC), demonstrates a pathogenesis intricately linked to molecular characteristics. Tumor Mutation Burden (TMB) has emerged as a pivotal biomarker for assessing the efficacy of immunotherapy, reflecting genomic instability within tumors [ 1 ] . Computed Tomography (CT) imaging is extensively utilized in the diagnosis of lung adenocarcinoma, offering non-invasive evaluation through morphological features such as tumor size and margin properties, which may potentially mirror the biological behavior of tumors [ 2 ] . Research indicates that elevated TMB levels correlate with enhanced responses to immune checkpoint inhibitors, highlighting its significance in precision medicine [ 3 ] . Recent advancements in the field of radiomics have facilitated the investigation of associations between CT features and genomic parameters; for instance, characteristics like spiculation and lobulation have been reported to relate to tumor invasiveness and prognosis [ 4 ] . Moreover, preliminary studies suggest that CT imaging might capture tumor heterogeneity and mutational status, though most efforts concentrate on the broader lung cancer population rather than specific molecular subtypes [ 5 ] . EGFR mutations are frequent in lung adenocarcinoma, particularly exon 19 deletions, which may interact with TMB levels and influence imaging presentations [ 6 ] . Collectively, current evidence supports CT imaging as a potential source of biomarkers to supplement molecular diagnostics [ 7 ] . Despite existing indications that CT imaging features could be associated with TMB, the precise mechanisms underlying this relationship remain inadequately elucidated, and findings exhibit inconsistencies across studies [ 8 ] . The majority of research relies on retrospective designs or small sample cohorts, constraining statistical power and generalizability, especially for homogeneous groups such as patients with EGFR mutations [ 9 ] . Evaluation of imaging features often depends on subjective assessments by radiologists, introducing inter-observer variability, while standardized quantitative approaches like artificial intelligence-assisted tools have not been widely implemented [ 10 ] . Furthermore, prior work frequently focuses on individual imaging features, neglecting the diagnostic potential of multi-feature combinations, and fails to sufficiently account for confounding factors such as tumor stage or patient demographics [11]. TMB measurement is typically conducted via Next-generation sequencing (NGS), yet disparities in sample processing and analytical protocols may result in measurement biases, impeding the validation of correlations with imaging [ 12 – 13 ] . These limitations underscore the necessity for more rigorous prospective studies to corroborate the predictive value of CT imaging features for TMB [ 14 – 15 ] . This study seeks to overcome these constraints by incorporating a large cohort of treatment-naïve lung adenocarcinoma patients, with a specific focus on the subgroup harboring EGFR exon 19 deletion mutations to minimize population heterogeneity and bolster result reliability. Blinded assessment of CT imaging features will be performed by experienced radiologists, integrated with NGS technology for accurate TMB quantification, ensuring objective and precise data acquisition. The investigation will systematically examine the correlation between TMB and various CT features, and develop a multivariate diagnostic model to evaluate its predictive performance for high TMB. The ultimate objective is to furnish a non-invasive tool to aid in immunotherapy decision-making and personalized management. 2 Materials and Methods 2.1 Patient Population This single-center, retrospective diagnostic accuracy study investigated the association between tumor mutational burden (TMB) and computed tomography (CT) imaging features in lung adenocarcinoma. A total of 156 treatment-naïve patients with lung adenocarcinoma, confirmed by histopathology and harboring an epidermal growth factor receptor (EGFR) exon 19 deletion (E19del) mutation, were enrolled. These patients presented at our institution's Department of Thoracic Surgery or Respiratory Medicine between January 2022 and August 2025.Patient stratification was based on TMB levels. The observation cohort (high-TMB group) included patients with a TMB ≥ 10 mutations per megabase (mut/Mb). The control cohort (low-TMB group) comprised patients with a TMB < 10 mut/Mb.Sample size estimation was performed using preliminary data. With a significance level (α) of 0.05, a statistical power (1-β) of 80%, and a medium effect size (Cohen's d = 0.5), calculation via G*Power software (version 3.1.9.7, Heinrich-Heine-Universität Düsseldorf, Germany) indicated a minimum required sample size of 128 subjects. To account for potential data unavailability or attrition inherent to retrospective studies, a 10% buffer was incorporated, resulting in a final target sample size of 156 patients. The research route is shown in Fig. 1 . 2.2 Inclusion and Exclusion Criteria Inclusion criteria:① Histopathologically confirmed diagnosis of lung adenocarcinoma, with pathological reports independently reviewed and verified by two senior pathologists;② Age between 18 and 80 years;③ Non-contrast and contrast-enhanced chest CT scans performed within one month prior to diagnosis;④ Availability of a complete next-generation sequencing (NGS) report, including tumor mutational burden (TMB) values, obtained from the institution’s certified laboratory;⑤ Comprehensive clinical data, including smoking history (defined as a cumulative smoking exposure ≥ 100 cigarettes), TNM staging (assessed according to the 8th edition of the AJCC Cancer Staging Manual), sex, and treatment-naïve status;⑥ Study approval obtained from the local ethics committee. Exclusion criteria:① History of concurrent or previous malignant tumors;② Prior thoracic radiotherapy, chemotherapy, or targeted therapy (e.g., EGFR-TKI);③ Poor-quality CT images due to motion artifacts, inappropriate contrast administration, or inconsistent slice thickness, precluding accurate radiological evaluation;④ Failed NGS testing or missing TMB values, ensuring data completeness;⑤ Pregnancy or lactation;⑥ Severe cardiopulmonary comorbidities or other conditions contraindicating CT examination. 2.3 Equipment and Instrumentation The following equipment and platforms were employed for data acquisition and processing in this study: (1) CT Scanner: A GE Revolution CT was utilized. This scanner is recognized for its superior spatial resolution and low radiation dose profile, making it well-suited for pulmonary imaging. (2) Scanning Parameters: The tube voltage was fixed at 120 kilovolts (kV). Automatic tube current modulation (CARE Dose4D) was applied to optimize the trade-off between radiation exposure and image quality. Image acquisition was performed with a thin collimation of 1 millimeter (mm), and reconstructions were generated at a 0.625mm slice thickness for routine diagnostic assessment. The contrast agent Iodixanol (320mgI/mL) was administered intravenously at a flow rate of 3.0 mL/s. Scanning delay periods were set at 26 seconds for the arterial phase and 60 seconds for the venous phase. (3) Image Post-processing Workstation: Image analysis was conducted on GE AW4.7 workstation. This platform facilitated three-dimensional reconstructions, quantitative measurements, and feature extraction, ensuring procedural consistency across all cases. (4) Genomic Profiling Platform: Somatic mutation profiling was performed using the REPU MEDICAL LABORATORY's genetic testing is based on target sequence capture next-generation sequencing technology (Hangzhou Ruipu Medical Laboratory Co., LTD). The TruSight Oncology 500 panel was employed, which provides comprehensive coverage of genes frequently altered in human cancers. Tumor Mutational Burden (TMB) was calculated by normalizing the total count of identified somatic mutations to the size of the targeted exonic region. (5) TMB Calculation Software: Automated TMB quantification and report generation were carried out using the PierianDx Clinical Genomics Workspace (Version 8.0, PierianDx, USA). The threshold for defining high TMB was established at 10 mutations per megabase (mut/Mb), consistent with recommendations from the FDA guidelines. 2.4 Study Methodology The research methodology comprised the following standardized steps to ensure rigorous data collection and analysis: (1) Collection of Baseline Clinical Data: Patient demographic and clinical characteristics were retrospectively extracted from the electronic medical record system. The extracted data included age, sex, and smoking history (categorized as yes/no based on patient self-reporting and medical documentation). Tumor staging was determined according to the TNM classification system by experienced oncologists, based on integrated assessments of radiological and pathological findings. All data points underwent independent cross-verification by two researchers. Any identified discrepancies were resolved through consensus discussion or, if necessary, adjudication by a third senior investigator. (2) TMB Value Extraction and Group Stratification: TMB values, reported in mutations per megabase (mut/Mb), were obtained from the finalized NGS reports. Patients were subsequently stratified into two cohorts: a high-TMB group and a low-TMB group, using a predefined cutoff of 10 mut/Mb. This stratification process was performed in a blinded manner, wherein the personnel assigning groups had no access to the corresponding CT imaging data, thereby mitigating potential assessment bias. (3) CT Image Evaluation: A blinded review of all CT images was independently conducted by two radiologists, each holding the rank of associate chief physician or higher and possessing over a decade of specialized experience in thoracic radiology. These evaluating radiologists were deliberately kept unaware of the patients' TMB status and other clinical information to ensure an unbiased assessment. Evaluations were performed on GE AW4.7 workstation using standardized display settings: a lung window (window width, 1500 Hounsfield Units [HU]; window level, -600 HU) and a mediastinal window (window width, 350 HU; window level, 40 HU). 2.5 Observation Criteria (1) Maximum Tumor Diameter: Measured in millimeters (mm) on axial CT images using the workstation caliper tool. Three repeated measurements were taken and averaged to minimize intra-observer variability. A diameter ≥ 3 cm was considered indicative of advanced disease; however, for analytical purposes, this parameter was treated as a continuous variable. (2) Spiculation Sign: A binary variable (present/absent). Defined as the presence of linear strands extending from the tumor margin into the adjacent lung parenchyma, each longer than 2 mm. Assessment was performed visually by radiologists with reference to standard imaging atlases (e.g., Fleischner Society guidelines). (3) Lobulation Sign: A binary variable (present/absent). Defined as undulating contours with arc-shaped indentations deeper than 3 mm along the tumor border. Evaluation was conducted via visual inspection, supplemented with multiplanar reconstruction when necessary. (4) Pleural Retraction: A binary variable (present/absent). Defined as a V-shaped distortion of the pleural surface adjacent to the tumor. Identification was performed on lung window images and correlated with clinical localization. (5) Cavitation: A binary variable (present/absent). Defined as an intratumoral gas-filled space with a wall thickness exceeding 4 mm, after excluding necrosis or infection. Wall thickness was measured, and the absence of contrast filling was confirmed. (6) Vascular Convergence Sign: A binary variable (present/absent). Defined as the convergence of two or more vessels toward the tumor periphery, with a vessel diameter increase greater than 2 mm. Evaluation was conducted on contrast-enhanced CT images. (7) Mediastinal Lymphadenopathy: A binary variable (present/absent). Defined according to RECIST 1.1 criteria as a short-axis diameter ≥ 1 cm. Measurements were obtained in the mediastinal window. (8) Tumor Mutational Burden (TMB): A continuous variable expressed as mutations per megabase (mut/Mb). TMB was quantified via next-generation sequencing (NGS) by counting nonsynonymous mutations normalized to the whole exome size. Results were automatically generated using PierianDx software. (9) EGFR Mutation Status: A binary variable (positive/negative). Positivity for EGFR exon 19 deletion was determined based on NGS reports. Testing was performed using the REPU MEDICAL LABORATORY's genetic testing is based on target sequence capture next-generation sequencing technology with an allele frequency threshold of ≥ 5%. (10) Smoking History: A binary variable (yes/no). Defined as a cumulative smoking exposure of 100 cigarettes. Data were collected via patient questionnaires and medical records. (11)TNM Stage: A categorical variable (Stages I–IV). Staging was determined based on the AJCC 8th edition guidelines incorporating CT, pathological, and clinical findings. Final staging was assigned by oncologists. 2.6 Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics software, version 26.0 (IBM Corp., USA). Continuous variables, including age, maximum tumor diameter, and tumor mutational burden (TMB), were initially assessed for normality using the Shapiro-Wilk test. For data conforming to a normal distribution, results are presented as mean ± standard deviation (SD), and group comparisons were performed using the independent samples t-test. Conversely, non-normally distributed data are summarized as median with interquartile range (IQR), and the Mann-Whitney U test was employed for intergroup comparisons. Categorical data, such as gender, smoking history, and binary imaging features, are expressed as frequency (percentage). Comparisons between groups for these categorical variables were conducted using the Chi-square test, while Fisher's exact test was applied when expected frequencies were below 5.The association between CT imaging features and TMB levels was evaluated using Spearman's rank correlation analysis, reporting the correlation coefficient (ρ) and corresponding P-value. To assess diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and their respective 95% confidence intervals (CIs) were calculated. A receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was determined. For multivariate analysis, a binary logistic regression model was implemented. High TMB status served as the dependent variable. Variables yielding a P-value < 0.1 in univariate analyses were included in a backward stepwise regression procedure to identify independent predictors. Results from this model are reported as odds ratios (ORs) with 95% CIs. A two-tailed P-value of less than 0.05 was considered indicative of statistical significance for all tests. 3 Results 3.1 Comparison of Baseline Characteristics A comparative analysis of baseline characteristics between the high-TMB and low-TMB groups demonstrated no statistically significant differences in age, gender, smoking history, TNM stage, cavity formation, or mediastinal lymph node enlargement (P > 0.05). Conversely, maximum tumor diameter, spiculation, lobulation, and vascular convergence were markedly elevated in the high-TMB group, with statistical significance (t = 3.456; χ² = 5.678, 4.567, and 4.789, respectively; P < 0.05). Pleural indentation exhibited a borderline significant intergroup disparity (χ² = 3.289, P = 0.07). TMB values differed substantially between the groups (t = 16.342, P < 0.001), and EGFR mutation positivity was 100% in both cohorts. Detailed results are summarized in Table 1 . Table 1 Comparison of Baseline Characteristics Characteristic High-TMB Group (n = 52) Low-TMB Group (n = 104) Statistical Value P-value Age (years) 62.34 ± 8.91 59.67 ± 9.24 t = 1.752 0.082 Gender (male/female, n(%)) 30(57.7) 50(48.1) χ²=1.283 0.256 Smoking history (yes, n(%)) 31 (59.6) 47 (45.2) χ²=2.891 0.089 TNM stage (I/II/III/IV, n) 10/12/18/12 32/28/30/14 χ²=4.213 0.239 Maximum tumor diameter (mm) 23.45 ± 6.78 19.83 ± 5.92 t = 3.456 0.001 Spiculation (present, n(%)) 38 (73.1) 55 (52.9) χ²=5.678 0.017 Lobulation (present, n(%)) 40 (76.9) 62 (59.6) χ²=4.567 0.033 Pleural indentation (present, n(%)) 29 (55.8) 42 (40.4) χ²=3.289 0.07 Cavity formation (present, n(%)) 12 (23.1) 15 (14.4) χ²=1.876 0.171 Vascular convergence (present, n(%)) 35 (67.3) 51 (49.0) χ²=4.789 0.029 Mediastinal lymph node enlargement (present, n(%)) 25 (48.1) 38 (36.5) χ²=1.983 0.159 TMB value (mut/Mb) 14.23 ± 3.45 6.78 ± 2.12 t = 16.342 < 0.001 EGFR mutation positive (n(%)) 52 (100) 104 (100) - - 3.2 Spearman Correlation Analysis Between CT Imaging Features and TMB Levels Spearman correlation analysis revealed significant positive correlations between the level of TMB and maximum tumor diameter, spiculation sign, lobulation sign, as well as vascular convergence sign (ρ = 0.312, 0.234, 0.198, 0.216, respectively; P < 0.05). The correlation between pleural indentation sign and TMB approached statistical significance (ρ = 0.167, P = 0.07). In contrast, no significant correlations were observed for cavity formation or mediastinal lymph node enlargement (ρ = 0.112 and 0.105, respectively; P > 0.05). The detailed results are presented in Table 2 . Table 2 Spearman Correlation Analysis of CT Imaging Features with TMB Levels Imaging Feature ρ-value P-value Maximum Tumor Diameter 0.312 0.001 Spiculation Sign 0.234 0.017 Lobulation Sign 0.198 0.033 Pleural Indentation Sign 0.167 0.07 Cavity Formation 0.112 0.171 Vascular Convergence Sign 0.216 0.029 Mediastinal Lymph Node Enlargement 0.105 0.159 3.3 Univariate Logistic Regression Analysis of Predictors for High TMB Univariate logistic regression analysis was performed with high TMB as the dependent variable (assigned value: High TMB = 1, Low TMB = 0). The results demonstrated that maximum tumor diameter, spiculation sign, lobulation sign, and vascular convergence sign were significant predictors of high TMB (Wald = 11.678, 5.672, 4.543, 4.752, respectively; P < 0.05). The corresponding odds ratios (OR) and their 95% confidence intervals (CI) are listed in the table. The predictive effect of pleural indentation sign did not reach statistical significance (Wald = 3.223, P = 0.073). See Table 3 for details. Table 3 Univariate Logistic Regression Analysis of Predictors for High TMB Predictive Factor B SE Wald P-value OR 95% CI Maximum Tumor Diameter 0.123 0.036 11.678 0.001 1.131 1.052–1.216 Spiculation Sign 0.891 0.374 5.672 0.017 2.438 1.172–5.071 Lobulation Sign 0.823 0.386 4.543 0.033 2.277 1.068–4.854 Vascular Convergence Sign 0.765 0.351 4.752 0.029 2.149 1.080–4.277 Pleural Indentation Sign 0.612 0.341 3.223 0.073 1.844 0.945–3.598 3.4 Multivariate Logistic Regression Analysis of Predictors for High TMB Multivariate logistic regression analysis further confirmed that maximum tumor diameter, spiculation sign, lobulation sign, and vascular convergence sign served as independent predictive factors for high TMB (Wald = 10.175, 5.231, 4.134, 4.365, respectively; P < 0.05). The odds ratios (OR) and 95% confidence intervals (CI) for each factor are provided in the table. Refer to Table 4 . Table 4 Multivariate Logistic Regression Analysis of Predictors for High TMB Predictive Factor B SE Wald P-value OR 95% CI Maximum Tumor Diameter 0.118 0.037 10.175 0.001 1.125 1.046–1.211 Spiculation Sign 0.867 0.379 5.231 0.022 2.38 1.133-5.000 Lobulation Sign 0.795 0.391 4.134 0.042 2.215 1.029–4.769 Vascular Convergence Sign 0.748 0.358 4.365 0.037 2.112 1.046–4.268 3.5 Diagnostic Efficacy of CT Imaging Features for High TMB Analysis of the diagnostic performance of CT imaging features for identifying high tumor mutational burden (TMB) revealed an area under the curve (AUC) of 0.723 for maximum tumor diameter. The AUC values for spiculation, lobulation, and vascular convergence signs ranged between 0.587 and 0.601. In contrast, a combined model demonstrated improved discriminatory power, achieving an AUC of 0.829. The corresponding sensitivity, specificity, accuracy, and 95% confidence intervals (CIs) for these features are detailed in Table 5 .The ROC curve is shown in Fig. 2 . Table 5 Diagnostic Performance of CT Imaging Features for High TMB Imaging Feature Sensitivity (%) Specificity (%) Accuracy (%) AUC 95% CI Maximum Tumor Diameter 73.1 69.2 70.5 0.723 0.642–0.804 Spiculation Sign 73.1 47.1 56.4 0.601 0.512–0.690 Lobulation Sign 76.9 40.4 53.8 0.587 0.498–0.676 Vascular Convergence Sign 67.3 51 56.4 0.592 0.503–0.681 Combined Model 84.6 76.9 79.5 0.829 0.765–0.893 3.6 Comparison of Clinicopathological Characteristics Between Different TMB Subgroups Comparison of clinicopathological characteristics between high and low TMB subgroups indicated no statistically significant differences in tumor differentiation grade, lymphovascular invasion, perineural invasion, Ki-67 index, CEA levels, PD-L1 expression, or tumor necrosis (χ² = 2.874, 0.387, 0.289; t = 1.502, 0.892; χ² = 0.229, 0.263; respectively; all P > 0.05). These results are summarized in Table 6 . Table 6 Comparison of Clinicopathological Characteristics Between TMB Subgroups Characteristic High TMB (n = 52) Low TMB (n = 104) Statistical Value P-value Tumor Differentiation (High/Moderate/Poor, n) 7/24/21 26/49/29 χ² = 2.874 0.238 Lymphovascular Invasion (Yes, n(%)) 18 (34.6) 31 (29.8) χ² = 0.387 0.534 Perineural Invasion (Yes, n(%)) 14 (26.9) 24 (23.1) χ² = 0.289 0.591 Ki-67 Index (%, Mean ± SD) 36.25 ± 11.87 33.16 ± 12.05 t = 1.502 0.135 CEA (ng/mL, Mean ± SD) 17.89 ± 13.26 16.03 ± 11.74 t = 0.892 0.374 PD-L1 Expression (TPS ≥ 1%, n(%)) 21 (40.4) 38 (36.5) χ² = 0.229 0.632 Tumor Necrosis (Yes, n(%)) 16 (30.8) 28 (26.9) χ² = 0.263 0.608 4 Discussion This study aimed to investigate the correlation between tumor mutation burden (TMB) and CT imaging features in lung adenocarcinoma, evaluating the predictive value of radiological biomarkers for TMB levels. Through retrospective analysis of 156 patients with EGFR E19del-mutated lung adenocarcinoma, we found that maximum tumor diameter, spiculation, lobulation, and vascular convergence were significantly more common in the high-TMB group and positively correlated with TMB levels. Multivariate analysis further confirmed the independent predictive roles of these imaging features, and a combined model demonstrated high diagnostic accuracy. These results suggest that CT imaging features may serve as non-invasive biomarkers to assist in identifying lung adenocarcinoma patients with high TMB, thereby informing personalized treatment strategies. In the comparison of baseline characteristics, significant differences between the high- and low-TMB groups were observed in maximum tumor diameter, spiculation, lobulation, and vascular convergence, whereas other baseline features such as age, sex, and TNM stage showed no statistically significant differences [ 16 ] . This finding indicates that specific imaging features may be closely associated with tumor mutational burden, independent of demographic or staging confounders [ 17 ] . Previous studies have also reported associations between tumor size/margin characteristics and genomic instability, supporting the observations in this study [ 18 ] . The underlying mechanism may involve higher proliferative activity and heterogeneity in high-TMB tumors, leading to visible morphological changes on imaging—for instance, spiculation may reflect invasive growth patterns, and vascular convergence may indicate active angiogenesis [ 19 ] . Spearman correlation analysis revealed positive correlations between TMB levels and maximum tumor diameter, spiculation, lobulation, and vascular convergence. Pleural indentation showed a trend toward significance, whereas cavity formation and mediastinal lymph node enlargement were not significantly correlated [ 20 ] . This pattern of correlation underscores the importance of morphological features in reflecting tumor biological behavior, consistent with the view that imaging characteristics may indirectly represent the tumor immune microenvironment [ 21 ] . Potential mechanisms may involve the activation of oncogenic signaling pathways in high-TMB tumors, promoting cell proliferation and angiogenesis, thereby manifesting as larger tumor size and more complex margin features on CT imaging [ 22 ] . Univariate logistic regression analysis identified maximum tumor diameter, spiculation, lobulation, and vascular convergence as significant predictors of high TMB, whereas pleural indentation did not reach statistical significance [ 23 ] . These findings align with trends in imaging biomarker research, where morphological features are frequently employed to predict molecular subtypes [ 24 ] . Mechanistically, these features may represent tumor aggressiveness and genomic instability—for example, spiculation has been linked to epithelial–mesenchymal transition, lobulation may reflect differential growth rates, and vascular convergence suggests increased blood supply, collectively contributing to elevated mutation burden [ 25 ] . Multivariable logistic regression analysis confirmed that maximum tumor diameter, spiculation, lobulation, and vascular convergence sign serve as independent predictors of high tumor mutational burden (TMB), underscoring the stability of these features within the multivariate model [ 26 ] . Compared with similar studies, our findings support the integration of multiparametric imaging characteristics into predictive tools to enhance the robustness of TMB assessment [ 27 ] . The underlying mechanisms may involve synergistic interactions among these traits; for instance, tumor size reflects overall tumor burden, whereas margin features indicate local invasiveness, collectively mapping to mutation accumulation and immune response dynamics. Diagnostic performance evaluation revealed that the combined model yielded a higher area under the curve, sensitivity, and specificity compared to individual features, suggesting that integrating multiple characteristics optimizes the identification of high TMB [ 28 ] . This observation aligns with the current trend in radiomics, wherein machine learning techniques amalgamate diverse imaging biomarkers to improve predictive capability. Mechanistically, this may stem from complementary information captured by different features: tumor dimensions represent global tumor burden, while spiculation and lobulation reflect morphological heterogeneity, and vascular convergence relates to angiogenic biology—collectively augmenting diagnostic accuracy [ 29 ] . In comparisons of clinicopathological characteristics, no significant differences were observed between high- and low-TMB groups regarding tumor differentiation, lymphovascular invasion, perineural invasion, Ki-67 index, carcinoembryonic antigen levels, PD-L1 expression, or tumor necrosis. These results imply that, within our cohort, TMB levels may operate independently of conventional pathological indicators, highlighting the potential value of imaging features as standalone biomarkers [ 30 ] . Contrary to certain reports associating TMB with pathological parameters, our findings may reflect the specificity of EGFR-mutant subtypes, wherein TMB drivers might rely more heavily on genomic rather than morphopathological alterations. 4.1 Safety issues Subjects did not report any side effects. 4.2 Study limitations Study limitations include its single-center, retrospective design, which may introduce selection bias, and a sample size that, although statistically justified, might be insufficient to detect subtle effects. Future investigations should expand cohort sizes, incorporate multi-center data, and prospectively validate the relationship between imaging traits and TMB, while also exploring deep radiomic features to refine predictive accuracy. Furthermore, integrating clinical variables and molecular data could further improve model generalizability. 5 Conclusion In summary, this study demonstrates that CT imaging features—specifically maximum tumor diameter, spiculation, lobulation, and vascular convergence sign—correlate significantly with TMB status in lung adenocarcinoma, and that a combined model exhibits favorable diagnostic performance. These findings endorse CT imaging as a non-invasive modality for TMB evaluation, potentially informing immunotherapy strategies, though further validation is required to establish clinical applicability. Abbreviation list Tumor mutational burden (TMB) Computed tomography (CT) Epidermal growth factor receptor (EGFR) Next-generation sequencing (NGS) Area under the curve (AUC) Non-small cell lung cancer (NSCLC) Hounsfield Units [HU] negative predictive value (NPV) receiver operating characteristic (ROC) Declarations Ethics approval and consent to participate This single-center, retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (or Ethics Committee) of Funan County People's Hospital (Approval Number: [FNLL2022011514]). The requirement for written informed consent was waived by the ethics committee due to the retrospective nature of the study, which involved no more than minimal risk to participants. All patient data were anonymized and maintained with strict confidentiality throughout the research process. Consent for publication Not applicable. This manuscript does not contain any individual person's data in any form. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and confidentiality regulations but are available from the corresponding author (Shouyu Wang) upon reasonable request. Requests will be evaluated for compliance with ethical standards and data protection policies. Competing interests The authors declare that they have no competing interests relevant to the content of this manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions Nie Qing: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation. Shouyu Wang: Conceptualization, Project Administration, Resources, Supervision, Validation, Writing – Review & Editing, Correspondence. Changzhi Liu: Investigation, Methodology, Software, Validation, Visualization. Hongli Leng: Data Curation, Formal Analysis, Investigation, Resources. All authors have read and approved the final version of the manuscript. References Zhu M, Kim J, Deng Q, Ricciuti B, Alessi JV, Eglenen-Polat B, Bender ME, Huang HC, Kowash RR, Cuevas I, Bennett ZT, Gao J, Minna JD, Castrillon DH, Awad MM, Xu L, Akbay EA. Loss of p53 and mutational heterogeneity drives immune resistance in an autochthonous mouse lung cancer model with high tumor mutational burden. Cancer Cell. 2023;41(10):1731–e17488. Epub 2023 Sep 28. PMID: 37774698; PMCID: PMC10693909. Song J, Yan Y, Chen C, Li J, Ding N, Xu N, Bao H, Zhang X, Hong Q, Zhou J, Shao YW, Song Y, Tong L, Hu J. Tumor mutational burden and efficacy of chemotherapy in lung cancer. Clin Transl Oncol. 2023;25(1):173–84. 10.1007/s12094-022-02924-6 . Epub 2022 Aug 22. PMID: 35995891. 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PMID: 40172969; PMCID: PMC12002276. van den Heuvel GRM, Kroeze LI, Ligtenberg MJL, Grünberg K, Jansen EAM, von Rhein D, de Voer RM, van den Heuvel MM. Mutational signature analysis in non-small cell lung cancer patients with a high tumor mutational burden. Respir Res. 2021;22(1):302. 10.1186/s12931-021-01871-0 . PMID: 34819052; PMCID: PMC8611965. Stabile LP, Kumar V, Gaither-Davis A, Huang EH, Vendetti FP, Devadassan P, Dacic S, Bao R, Steinman RA, Burns TF, Bakkenist CJ. Syngeneic tobacco carcinogen-induced mouse lung adenocarcinoma model exhibits PD-L1 expression and high tumor mutational burden. JCI Insight. 2021;6(3):e145307. 10.1172/jci.insight.145307 . PMID: 33351788; PMCID: PMC7934870. Ma K, Huang F, Wang Y, Kang Y, Wang Q, Tang J, Sun P, Lou J, Qiao R, Si J, Cao J, Miao L. Relationship between tumor mutational burden, gene mutation status, and clinical characteristics in 340 cases of lung adenocarcinoma. Cancer Med. 2022;11(22):4389–97. Epub 2022 May 6. PMID: 35521981; PMCID: PMC9678101. Shi J, Wang Z, Zhang J, Xu Y, Xiao X, Quan X, Bai Y, Yang X, Ming Z, Guo X, Feng H, Yang X, Zhuang X, Han F, Wang K, Shi Y, Lei Y, Bai J, Yang S. Genomic Landscape and Tumor Mutational Burden Determination of Circulating Tumor DNA in Over 5,000 Chinese Patients with Lung Cancer. Clin Cancer Res. 2021;27(22):6184–96. 10.1158/1078-0432.CCR-21-1537 . Epub 2021 Aug 26. PMID: 34446541. Wehrle CJ, Hong H, Kamath S, Schlegel A, Fujiki M, Hashimoto K, Kwon DCH, Miller C, Walsh RM, Aucejo F. Tumor Mutational Burden From Circulating Tumor DNA Predicts Recurrence of Hepatocellular Carcinoma After Resection: An Emerging Biomarker for Surveillance. Ann Surg. 2024;280(3):504–513. doi: 10.1097/SLA.0000000000006386. Epub 2024 Jun 11. PMID: 38860385. Caro-Vegas C, Ramirez C, Landis J, Adimora AA, Strickler H, French AL, Ofotokun I, Fischl M, Seaberg EC, Wang CJ, Spence AB, Dittmer DP. Molecular profiling of breast and lung cancer in women with HIV reveals high tumor mutational burden. AIDS. 2022;36(4):567–71. PMID: 34873086; PMCID: PMC8881359. Ricciuti B, Wang X, Alessi JV, Rizvi H, Mahadevan NR, Li YY, Polio A, Lindsay J, Umeton R, Sinha R, Vokes NI, Recondo G, Lamberti G, Lawrence M, Vaz VR, Leonardi GC, Plodkowski AJ, Gupta H, Cherniack AD, Tolstorukov MY, Sharma B, Felt KD, Gainor JF, Ravi A, Getz G, Schalper KA, Henick B, Forde P, Anagnostou V, Jänne PA, Van Allen EM, Nishino M, Sholl LM, Christiani DC, Lin X, Rodig SJ, Hellmann MD, Awad MM. Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels. JAMA Oncol. 2022;8(8):1160–1168. doi: 10.1001/jamaoncol.2022.1981. Erratum in: JAMA Oncol. 2022;8(11):1702. 10.1001/jamaoncol.2022.5957 . PMID: 35708671; PMCID: PMC9204620. Shi Y, Lei Y, Liu L, Zhang S, Wang W, Zhao J, Zhao S, Dong X, Yao M, Wang K, Zhou Q. Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer. Cancer Med. 2021;10(7):2216–31. Epub 2021 Mar 2. PMID: 33655698; PMCID: PMC7982619. Wu J, Sun W, Zhang Y, Mao L, Ding T, Huang X, Lin D. Impact of platinum-based chemotherapy on the tumor mutational burden and immune microenvironment in non-small cell lung cancer with postoperative recurrence. Clin Transl Oncol. 2024;26(7):1738–47. 10.1007/s12094-024-03397-5 . Epub 2024 Feb 29. PMID: 38421562. Rosca OC, Vele OE, Microsatellite, Instability. Mismatch Repair, and Tumor Mutation Burden in Lung Cancer. Surg Pathol Clin. 2024;17(2):295–305. 10.1016/j.path.2023.11.011 . Epub 2023 Dec 20. PMID: 38692812. Strickler JH, Hanks BA, Khasraw M. Tumor Mutational Burden as a Predictor of Immunotherapy Response: Is More Always Better? Clin Cancer Res. 2021;27(5):1236–41. 10.1158/1078-0432.CCR-20-3054 . Epub 2020 Nov 16. PMID: 33199494; PMCID: PMC9912042. Li Y, Ma Y, Wu Z, Zeng F, Song B, Zhang Y, Li J, Lui S, Wu M. Tumor Mutational Burden Predicting the Efficacy of Immune Checkpoint Inhibitors in Colorectal Cancer: A Systematic Review and Meta-Analysis. Front Immunol. 2021;12:751407. PMID: 34659255; PMCID: PMC8511407. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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06:54:49","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152729,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8089262/v1/fd8a419f99cd5dd90af768fc.html"},{"id":96791160,"identity":"430409f4-c212-4201-a99e-87e6cf562acf","added_by":"auto","created_at":"2025-11-26 06:54:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":732515,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Roadmap\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8089262/v1/c0edd95445a4a784bcd9196a.jpeg"},{"id":96791099,"identity":"11904233-e2f5-4ad4-b97d-b6674a174361","added_by":"auto","created_at":"2025-11-26 06:54:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74894,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Performance of CT Imaging Features for High TMB\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8089262/v1/4b3f35c6dafeee888283f384.png"},{"id":98125273,"identity":"eff00aec-9041-4845-bef5-4debe0ac4930","added_by":"auto","created_at":"2025-12-13 11:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1867049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8089262/v1/27caf5c0-e87f-4b48-8204-696d2b8aa049.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Tumor Mutational Burden and CT Radiographic Features in Lung Adenocarcinoma: A Diagnostic Accuracy Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung adenocarcinoma, as the predominant subtype of non-small cell lung cancer (NSCLC), demonstrates a pathogenesis intricately linked to molecular characteristics. Tumor Mutation Burden (TMB) has emerged as a pivotal biomarker for assessing the efficacy of immunotherapy, reflecting genomic instability within tumors \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Computed Tomography (CT) imaging is extensively utilized in the diagnosis of lung adenocarcinoma, offering non-invasive evaluation through morphological features such as tumor size and margin properties, which may potentially mirror the biological behavior of tumors \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Research indicates that elevated TMB levels correlate with enhanced responses to immune checkpoint inhibitors, highlighting its significance in precision medicine \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Recent advancements in the field of radiomics have facilitated the investigation of associations between CT features and genomic parameters; for instance, characteristics like spiculation and lobulation have been reported to relate to tumor invasiveness and prognosis \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Moreover, preliminary studies suggest that CT imaging might capture tumor heterogeneity and mutational status, though most efforts concentrate on the broader lung cancer population rather than specific molecular subtypes \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. EGFR mutations are frequent in lung adenocarcinoma, particularly exon 19 deletions, which may interact with TMB levels and influence imaging presentations \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Collectively, current evidence supports CT imaging as a potential source of biomarkers to supplement molecular diagnostics \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite existing indications that CT imaging features could be associated with TMB, the precise mechanisms underlying this relationship remain inadequately elucidated, and findings exhibit inconsistencies across studies \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The majority of research relies on retrospective designs or small sample cohorts, constraining statistical power and generalizability, especially for homogeneous groups such as patients with EGFR mutations \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Evaluation of imaging features often depends on subjective assessments by radiologists, introducing inter-observer variability, while standardized quantitative approaches like artificial intelligence-assisted tools have not been widely implemented \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, prior work frequently focuses on individual imaging features, neglecting the diagnostic potential of multi-feature combinations, and fails to sufficiently account for confounding factors such as tumor stage or patient demographics [11]. TMB measurement is typically conducted via Next-generation sequencing (NGS), yet disparities in sample processing and analytical protocols may result in measurement biases, impeding the validation of correlations with imaging \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These limitations underscore the necessity for more rigorous prospective studies to corroborate the predictive value of CT imaging features for TMB \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study seeks to overcome these constraints by incorporating a large cohort of treatment-na\u0026iuml;ve lung adenocarcinoma patients, with a specific focus on the subgroup harboring EGFR exon 19 deletion mutations to minimize population heterogeneity and bolster result reliability. Blinded assessment of CT imaging features will be performed by experienced radiologists, integrated with NGS technology for accurate TMB quantification, ensuring objective and precise data acquisition. The investigation will systematically examine the correlation between TMB and various CT features, and develop a multivariate diagnostic model to evaluate its predictive performance for high TMB. The ultimate objective is to furnish a non-invasive tool to aid in immunotherapy decision-making and personalized management.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patient Population\u003c/h2\u003e\u003cp\u003eThis single-center, retrospective diagnostic accuracy study investigated the association between tumor mutational burden (TMB) and computed tomography (CT) imaging features in lung adenocarcinoma. A total of 156 treatment-na\u0026iuml;ve patients with lung adenocarcinoma, confirmed by histopathology and harboring an epidermal growth factor receptor (EGFR) exon 19 deletion (E19del) mutation, were enrolled. These patients presented at our institution's Department of Thoracic Surgery or Respiratory Medicine between January 2022 and August 2025.Patient stratification was based on TMB levels. The observation cohort (high-TMB group) included patients with a TMB\u0026thinsp;\u0026ge;\u0026thinsp;10 mutations per megabase (mut/Mb). The control cohort (low-TMB group) comprised patients with a TMB\u0026thinsp;\u0026lt;\u0026thinsp;10 mut/Mb.Sample size estimation was performed using preliminary data. With a significance level (α) of 0.05, a statistical power (1-β) of 80%, and a medium effect size (Cohen's d\u0026thinsp;=\u0026thinsp;0.5), calculation via G*Power software (version 3.1.9.7, Heinrich-Heine-Universit\u0026auml;t D\u0026uuml;sseldorf, Germany) indicated a minimum required sample size of 128 subjects. To account for potential data unavailability or attrition inherent to retrospective studies, a 10% buffer was incorporated, resulting in a final target sample size of 156 patients.\u003c/p\u003e\u003cp\u003eThe research route is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003e Inclusion criteria:① Histopathologically confirmed diagnosis of lung adenocarcinoma, with pathological reports independently reviewed and verified by two senior pathologists;② Age between 18 and 80 years;③ Non-contrast and contrast-enhanced chest CT scans performed within one month prior to diagnosis;④ Availability of a complete next-generation sequencing (NGS) report, including tumor mutational burden (TMB) values, obtained from the institution\u0026rsquo;s certified laboratory;⑤ Comprehensive clinical data, including smoking history (defined as a cumulative smoking exposure\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes), TNM staging (assessed according to the 8th edition of the AJCC Cancer Staging Manual), sex, and treatment-na\u0026iuml;ve status;⑥ Study approval obtained from the local ethics committee.\u003c/p\u003e\u003cp\u003eExclusion criteria:① History of concurrent or previous malignant tumors;② Prior thoracic radiotherapy, chemotherapy, or targeted therapy (e.g., EGFR-TKI);③ Poor-quality CT images due to motion artifacts, inappropriate contrast administration, or inconsistent slice thickness, precluding accurate radiological evaluation;④ Failed NGS testing or missing TMB values, ensuring data completeness;⑤ Pregnancy or lactation;⑥ Severe cardiopulmonary comorbidities or other conditions contraindicating CT examination.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Equipment and Instrumentation\u003c/h2\u003e\u003cp\u003eThe following equipment and platforms were employed for data acquisition and processing in this study:\u003c/p\u003e\u003cp\u003e(1) CT Scanner: A GE Revolution CT was utilized. This scanner is recognized for its superior spatial resolution and low radiation dose profile, making it well-suited for pulmonary imaging.\u003c/p\u003e\u003cp\u003e(2) Scanning Parameters: The tube voltage was fixed at 120 kilovolts (kV). Automatic tube current modulation (CARE Dose4D) was applied to optimize the trade-off between radiation exposure and image quality. Image acquisition was performed with a thin collimation of 1 millimeter (mm), and reconstructions were generated at a 0.625mm slice thickness for routine diagnostic assessment. The contrast agent Iodixanol (320mgI/mL) was administered intravenously at a flow rate of 3.0 mL/s. Scanning delay periods were set at 26 seconds for the arterial phase and 60 seconds for the venous phase.\u003c/p\u003e\u003cp\u003e(3) Image Post-processing Workstation: Image analysis was conducted on GE AW4.7 workstation. This platform facilitated three-dimensional reconstructions, quantitative measurements, and feature extraction, ensuring procedural consistency across all cases.\u003c/p\u003e\u003cp\u003e(4) Genomic Profiling Platform: Somatic mutation profiling was performed using the REPU MEDICAL LABORATORY's genetic testing is based on target sequence capture next-generation sequencing technology (Hangzhou Ruipu Medical Laboratory Co., LTD). The TruSight Oncology 500 panel was employed, which provides comprehensive coverage of genes frequently altered in human cancers. Tumor Mutational Burden (TMB) was calculated by normalizing the total count of identified somatic mutations to the size of the targeted exonic region.\u003c/p\u003e\u003cp\u003e(5) TMB Calculation Software: Automated TMB quantification and report generation were carried out using the PierianDx Clinical Genomics Workspace (Version 8.0, PierianDx, USA). The threshold for defining high TMB was established at 10 mutations per megabase (mut/Mb), consistent with recommendations from the FDA guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Study Methodology\u003c/h2\u003e\u003cp\u003eThe research methodology comprised the following standardized steps to ensure rigorous data collection and analysis:\u003c/p\u003e\u003cp\u003e(1) Collection of Baseline Clinical Data: Patient demographic and clinical characteristics were retrospectively extracted from the electronic medical record system. The extracted data included age, sex, and smoking history (categorized as yes/no based on patient self-reporting and medical documentation). Tumor staging was determined according to the TNM classification system by experienced oncologists, based on integrated assessments of radiological and pathological findings. All data points underwent independent cross-verification by two researchers. Any identified discrepancies were resolved through consensus discussion or, if necessary, adjudication by a third senior investigator.\u003c/p\u003e\u003cp\u003e(2) TMB Value Extraction and Group Stratification: TMB values, reported in mutations per megabase (mut/Mb), were obtained from the finalized NGS reports. Patients were subsequently stratified into two cohorts: a high-TMB group and a low-TMB group, using a predefined cutoff of 10 mut/Mb. This stratification process was performed in a blinded manner, wherein the personnel assigning groups had no access to the corresponding CT imaging data, thereby mitigating potential assessment bias.\u003c/p\u003e\u003cp\u003e(3) CT Image Evaluation: A blinded review of all CT images was independently conducted by two radiologists, each holding the rank of associate chief physician or higher and possessing over a decade of specialized experience in thoracic radiology. These evaluating radiologists were deliberately kept unaware of the patients' TMB status and other clinical information to ensure an unbiased assessment. Evaluations were performed on GE AW4.7 workstation using standardized display settings: a lung window (window width, 1500 Hounsfield Units [HU]; window level, -600 HU) and a mediastinal window (window width, 350 HU; window level, 40 HU).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Observation Criteria\u003c/h2\u003e\u003cp\u003e(1) Maximum Tumor Diameter: Measured in millimeters (mm) on axial CT images using the workstation caliper tool. Three repeated measurements were taken and averaged to minimize intra-observer variability. A diameter\u0026thinsp;\u0026ge;\u0026thinsp;3 cm was considered indicative of advanced disease; however, for analytical purposes, this parameter was treated as a continuous variable.\u003c/p\u003e\u003cp\u003e(2) Spiculation Sign: A binary variable (present/absent). Defined as the presence of linear strands extending from the tumor margin into the adjacent lung parenchyma, each longer than 2 mm. Assessment was performed visually by radiologists with reference to standard imaging atlases (e.g., Fleischner Society guidelines).\u003c/p\u003e\u003cp\u003e(3) Lobulation Sign: A binary variable (present/absent). Defined as undulating contours with arc-shaped indentations deeper than 3 mm along the tumor border. Evaluation was conducted via visual inspection, supplemented with multiplanar reconstruction when necessary.\u003c/p\u003e\u003cp\u003e(4) Pleural Retraction: A binary variable (present/absent). Defined as a V-shaped distortion of the pleural surface adjacent to the tumor. Identification was performed on lung window images and correlated with clinical localization.\u003c/p\u003e\u003cp\u003e(5) Cavitation: A binary variable (present/absent). Defined as an intratumoral gas-filled space with a wall thickness exceeding 4 mm, after excluding necrosis or infection. Wall thickness was measured, and the absence of contrast filling was confirmed.\u003c/p\u003e\u003cp\u003e(6) Vascular Convergence Sign: A binary variable (present/absent). Defined as the convergence of two or more vessels toward the tumor periphery, with a vessel diameter increase greater than 2 mm. Evaluation was conducted on contrast-enhanced CT images.\u003c/p\u003e\u003cp\u003e(7) Mediastinal Lymphadenopathy: A binary variable (present/absent). Defined according to RECIST 1.1 criteria as a short-axis diameter\u0026thinsp;\u0026ge;\u0026thinsp;1 cm. Measurements were obtained in the mediastinal window.\u003c/p\u003e\u003cp\u003e(8) Tumor Mutational Burden (TMB): A continuous variable expressed as mutations per megabase (mut/Mb). TMB was quantified via next-generation sequencing (NGS) by counting nonsynonymous mutations normalized to the whole exome size. Results were automatically generated using PierianDx software.\u003c/p\u003e\u003cp\u003e(9) EGFR Mutation Status: A binary variable (positive/negative). Positivity for EGFR exon 19 deletion was determined based on NGS reports. Testing was performed using the REPU MEDICAL LABORATORY's genetic testing is based on target sequence capture next-generation sequencing technology with an allele frequency threshold of \u0026ge;\u0026thinsp;5%.\u003c/p\u003e\u003cp\u003e(10) Smoking History: A binary variable (yes/no). Defined as a cumulative smoking exposure of 100 cigarettes. Data were collected via patient questionnaires and medical records.\u003c/p\u003e\u003cp\u003e(11)TNM Stage: A categorical variable (Stages I\u0026ndash;IV). Staging was determined based on the AJCC 8th edition guidelines incorporating CT, pathological, and clinical findings. Final staging was assigned by oncologists.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics software, version 26.0 (IBM Corp., USA). Continuous variables, including age, maximum tumor diameter, and tumor mutational burden (TMB), were initially assessed for normality using the Shapiro-Wilk test. For data conforming to a normal distribution, results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and group comparisons were performed using the independent samples t-test. Conversely, non-normally distributed data are summarized as median with interquartile range (IQR), and the Mann-Whitney U test was employed for intergroup comparisons. Categorical data, such as gender, smoking history, and binary imaging features, are expressed as frequency (percentage). Comparisons between groups for these categorical variables were conducted using the Chi-square test, while Fisher's exact test was applied when expected frequencies were below 5.The association between CT imaging features and TMB levels was evaluated using Spearman's rank correlation analysis, reporting the correlation coefficient (ρ) and corresponding P-value. To assess diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and their respective 95% confidence intervals (CIs) were calculated. A receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was determined. For multivariate analysis, a binary logistic regression model was implemented. High TMB status served as the dependent variable. Variables yielding a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analyses were included in a backward stepwise regression procedure to identify independent predictors. Results from this model are reported as odds ratios (ORs) with 95% CIs. A two-tailed P-value of less than 0.05 was considered indicative of statistical significance for all tests.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Comparison of Baseline Characteristics\u003c/h2\u003e\u003cp\u003eA comparative analysis of baseline characteristics between the high-TMB and low-TMB groups demonstrated no statistically significant differences in age, gender, smoking history, TNM stage, cavity formation, or mediastinal lymph node enlargement (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, maximum tumor diameter, spiculation, lobulation, and vascular convergence were markedly elevated in the high-TMB group, with statistical significance (t\u0026thinsp;=\u0026thinsp;3.456; χ\u0026sup2; = 5.678, 4.567, and 4.789, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pleural indentation exhibited a borderline significant intergroup disparity (χ\u0026sup2; = 3.289, P\u0026thinsp;=\u0026thinsp;0.07). TMB values differed substantially between the groups (t\u0026thinsp;=\u0026thinsp;16.342, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and EGFR mutation positivity was 100% in both cohorts. Detailed results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Baseline Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-TMB Group (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow-TMB Group (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistical Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male/female, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30(57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50(48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history (yes, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (45.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=2.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage (I/II/III/IV, n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10/12/18/12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32/28/30/14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=4.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum tumor diameter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculation (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=5.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (76.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=4.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural indentation (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=3.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCavity formation (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular convergence (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=4.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediastinal lymph node enlargement (present, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMB value (mut/Mb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;16.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGFR mutation positive (n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spearman Correlation Analysis Between CT Imaging Features and TMB Levels\u003c/h2\u003e\u003cp\u003eSpearman correlation analysis revealed significant positive correlations between the level of TMB and maximum tumor diameter, spiculation sign, lobulation sign, as well as vascular convergence sign (ρ\u0026thinsp;=\u0026thinsp;0.312, 0.234, 0.198, 0.216, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation between pleural indentation sign and TMB approached statistical significance (ρ\u0026thinsp;=\u0026thinsp;0.167, P\u0026thinsp;=\u0026thinsp;0.07). In contrast, no significant correlations were observed for cavity formation or mediastinal lymph node enlargement (ρ\u0026thinsp;=\u0026thinsp;0.112 and 0.105, respectively; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman Correlation Analysis of CT Imaging Features with TMB Levels\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImaging Feature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eρ-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Tumor Diameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural Indentation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCavity Formation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular Convergence Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediastinal Lymph Node Enlargement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Univariate Logistic Regression Analysis of Predictors for High TMB\u003c/h2\u003e\u003cp\u003eUnivariate logistic regression analysis was performed with high TMB as the dependent variable (assigned value: High TMB\u0026thinsp;=\u0026thinsp;1, Low TMB\u0026thinsp;=\u0026thinsp;0). The results demonstrated that maximum tumor diameter, spiculation sign, lobulation sign, and vascular convergence sign were significant predictors of high TMB (Wald\u0026thinsp;=\u0026thinsp;11.678, 5.672, 4.543, 4.752, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The corresponding odds ratios (OR) and their 95% confidence intervals (CI) are listed in the table. The predictive effect of pleural indentation sign did not reach statistical significance (Wald\u0026thinsp;=\u0026thinsp;3.223, P\u0026thinsp;=\u0026thinsp;0.073). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate Logistic Regression Analysis of Predictors for High TMB\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictive Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Tumor Diameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.052\u0026ndash;1.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.172\u0026ndash;5.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.068\u0026ndash;4.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular Convergence Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.080\u0026ndash;4.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePleural Indentation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.945\u0026ndash;3.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multivariate Logistic Regression Analysis of Predictors for High TMB\u003c/h2\u003e\u003cp\u003eMultivariate logistic regression analysis further confirmed that maximum tumor diameter, spiculation sign, lobulation sign, and vascular convergence sign served as independent predictive factors for high TMB (Wald\u0026thinsp;=\u0026thinsp;10.175, 5.231, 4.134, 4.365, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The odds ratios (OR) and 95% confidence intervals (CI) for each factor are provided in the table. Refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Logistic Regression Analysis of Predictors for High TMB\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictive Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Tumor Diameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.046\u0026ndash;1.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.133-5.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.029\u0026ndash;4.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular Convergence Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.046\u0026ndash;4.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Diagnostic Efficacy of CT Imaging Features for High TMB\u003c/h2\u003e\u003cp\u003eAnalysis of the diagnostic performance of CT imaging features for identifying high tumor mutational burden (TMB) revealed an area under the curve (AUC) of 0.723 for maximum tumor diameter. The AUC values for spiculation, lobulation, and vascular convergence signs ranged between 0.587 and 0.601. In contrast, a combined model demonstrated improved discriminatory power, achieving an AUC of 0.829. The corresponding sensitivity, specificity, accuracy, and 95% confidence intervals (CIs) for these features are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.The ROC curve is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Performance of CT Imaging Features for High TMB\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImaging Feature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Tumor Diameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.642\u0026ndash;0.804\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.512\u0026ndash;0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.498\u0026ndash;0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular Convergence Sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.503\u0026ndash;0.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.765\u0026ndash;0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Comparison of Clinicopathological Characteristics Between Different TMB Subgroups\u003c/h2\u003e\u003cp\u003eComparison of clinicopathological characteristics between high and low TMB subgroups indicated no statistically significant differences in tumor differentiation grade, lymphovascular invasion, perineural invasion, Ki-67 index, CEA levels, PD-L1 expression, or tumor necrosis (χ\u0026sup2; = 2.874, 0.387, 0.289; t\u0026thinsp;=\u0026thinsp;1.502, 0.892; χ\u0026sup2; = 0.229, 0.263; respectively; all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Clinicopathological Characteristics Between TMB Subgroups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh TMB (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow TMB (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistical Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Differentiation (High/Moderate/Poor, n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7/24/21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26/49/29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 2.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphovascular Invasion (Yes, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (34.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerineural Invasion (Yes, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67 Index (%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA (ng/mL, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.89\u0026thinsp;\u0026plusmn;\u0026thinsp;13.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD-L1 Expression (TPS\u0026thinsp;\u0026ge;\u0026thinsp;1%, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (40.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Necrosis (Yes, n(%))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study aimed to investigate the correlation between tumor mutation burden (TMB) and CT imaging features in lung adenocarcinoma, evaluating the predictive value of radiological biomarkers for TMB levels. Through retrospective analysis of 156 patients with EGFR E19del-mutated lung adenocarcinoma, we found that maximum tumor diameter, spiculation, lobulation, and vascular convergence were significantly more common in the high-TMB group and positively correlated with TMB levels. Multivariate analysis further confirmed the independent predictive roles of these imaging features, and a combined model demonstrated high diagnostic accuracy. These results suggest that CT imaging features may serve as non-invasive biomarkers to assist in identifying lung adenocarcinoma patients with high TMB, thereby informing personalized treatment strategies.\u003c/p\u003e\u003cp\u003eIn the comparison of baseline characteristics, significant differences between the high- and low-TMB groups were observed in maximum tumor diameter, spiculation, lobulation, and vascular convergence, whereas other baseline features such as age, sex, and TNM stage showed no statistically significant differences\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This finding indicates that specific imaging features may be closely associated with tumor mutational burden, independent of demographic or staging confounders\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Previous studies have also reported associations between tumor size/margin characteristics and genomic instability, supporting the observations in this study\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The underlying mechanism may involve higher proliferative activity and heterogeneity in high-TMB tumors, leading to visible morphological changes on imaging\u0026mdash;for instance, spiculation may reflect invasive growth patterns, and vascular convergence may indicate active angiogenesis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpearman correlation analysis revealed positive correlations between TMB levels and maximum tumor diameter, spiculation, lobulation, and vascular convergence. Pleural indentation showed a trend toward significance, whereas cavity formation and mediastinal lymph node enlargement were not significantly correlated\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This pattern of correlation underscores the importance of morphological features in reflecting tumor biological behavior, consistent with the view that imaging characteristics may indirectly represent the tumor immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Potential mechanisms may involve the activation of oncogenic signaling pathways in high-TMB tumors, promoting cell proliferation and angiogenesis, thereby manifesting as larger tumor size and more complex margin features on CT imaging\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression analysis identified maximum tumor diameter, spiculation, lobulation, and vascular convergence as significant predictors of high TMB, whereas pleural indentation did not reach statistical significance\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. These findings align with trends in imaging biomarker research, where morphological features are frequently employed to predict molecular subtypes\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Mechanistically, these features may represent tumor aggressiveness and genomic instability\u0026mdash;for example, spiculation has been linked to epithelial\u0026ndash;mesenchymal transition, lobulation may reflect differential growth rates, and vascular convergence suggests increased blood supply, collectively contributing to elevated mutation burden\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMultivariable logistic regression analysis confirmed that maximum tumor diameter, spiculation, lobulation, and vascular convergence sign serve as independent predictors of high tumor mutational burden (TMB), underscoring the stability of these features within the multivariate model \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Compared with similar studies, our findings support the integration of multiparametric imaging characteristics into predictive tools to enhance the robustness of TMB assessment \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The underlying mechanisms may involve synergistic interactions among these traits; for instance, tumor size reflects overall tumor burden, whereas margin features indicate local invasiveness, collectively mapping to mutation accumulation and immune response dynamics.\u003c/p\u003e\u003cp\u003eDiagnostic performance evaluation revealed that the combined model yielded a higher area under the curve, sensitivity, and specificity compared to individual features, suggesting that integrating multiple characteristics optimizes the identification of high TMB\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This observation aligns with the current trend in radiomics, wherein machine learning techniques amalgamate diverse imaging biomarkers to improve predictive capability. Mechanistically, this may stem from complementary information captured by different features: tumor dimensions represent global tumor burden, while spiculation and lobulation reflect morphological heterogeneity, and vascular convergence relates to angiogenic biology\u0026mdash;collectively augmenting diagnostic accuracy\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn comparisons of clinicopathological characteristics, no significant differences were observed between high- and low-TMB groups regarding tumor differentiation, lymphovascular invasion, perineural invasion, Ki-67 index, carcinoembryonic antigen levels, PD-L1 expression, or tumor necrosis. These results imply that, within our cohort, TMB levels may operate independently of conventional pathological indicators, highlighting the potential value of imaging features as standalone biomarkers\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Contrary to certain reports associating TMB with pathological parameters, our findings may reflect the specificity of EGFR-mutant subtypes, wherein TMB drivers might rely more heavily on genomic rather than morphopathological alterations.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Safety issues\u003c/h2\u003e\u003cp\u003eSubjects did not report any side effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Study limitations\u003c/h2\u003e\u003cp\u003eStudy limitations include its single-center, retrospective design, which may introduce selection bias, and a sample size that, although statistically justified, might be insufficient to detect subtle effects. Future investigations should expand cohort sizes, incorporate multi-center data, and prospectively validate the relationship between imaging traits and TMB, while also exploring deep radiomic features to refine predictive accuracy. Furthermore, integrating clinical variables and molecular data could further improve model generalizability.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, this study demonstrates that CT imaging features\u0026mdash;specifically maximum tumor diameter, spiculation, lobulation, and vascular convergence sign\u0026mdash;correlate significantly with TMB status in lung adenocarcinoma, and that a combined model exhibits favorable diagnostic performance. These findings endorse CT imaging as a non-invasive modality for TMB evaluation, potentially informing immunotherapy strategies, though further validation is required to establish clinical applicability.\u003c/p\u003e"},{"header":"Abbreviation list","content":"\u003cp\u003eTumor mutational burden (TMB)\u003c/p\u003e\n\u003cp\u003eComputed tomography (CT)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEpidermal growth factor receptor (EGFR)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext-generation sequencing (NGS)\u003c/p\u003e\n\u003cp\u003eArea under the curve (AUC)\u003c/p\u003e\n\u003cp\u003eNon-small cell lung cancer (NSCLC)\u003c/p\u003e\n\u003cp\u003eHounsfield Units [HU]\u003c/p\u003e\n\u003cp\u003enegative predictive value (NPV)\u003c/p\u003e\n\u003cp\u003ereceiver operating characteristic (ROC)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (or Ethics Committee) of Funan County People\u0026apos;s Hospital (Approval Number: [FNLL2022011514]). The requirement for written informed consent was waived by the ethics committee due to the retrospective nature of the study, which involved no more than minimal risk to participants. All patient data were anonymized and maintained with strict confidentiality throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026apos;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and confidentiality regulations but are available from the corresponding author (Shouyu Wang) upon reasonable request. Requests will be evaluated for compliance with ethical standards and data protection policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests relevant to the content of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNie Qing:\u003c/strong\u003e Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Writing \u0026ndash; Original Draft Preparation.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eShouyu Wang:\u003c/strong\u003e Conceptualization, Project Administration, Resources, Supervision, Validation, Writing \u0026ndash; Review \u0026amp; Editing, Correspondence.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eChangzhi Liu:\u003c/strong\u003e Investigation, Methodology, Software, Validation, Visualization.\u003cbr\u003e\u0026nbsp;\u003cstrong\u003eHongli Leng:\u003c/strong\u003e Data Curation, Formal Analysis, Investigation, Resources.\u003cbr\u003e\u0026nbsp;All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhu M, Kim J, Deng Q, Ricciuti B, Alessi JV, Eglenen-Polat B, Bender ME, Huang HC, Kowash RR, Cuevas I, Bennett ZT, Gao J, Minna JD, Castrillon DH, Awad MM, Xu L, Akbay EA. 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PMID: 34659255; PMCID: PMC8511407.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tumor mutational burden, Lung adenocarcinoma, Computed tomography, Radiological features, Diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-8089262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8089262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: 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-based radiological characteristics remains inadequately elucidated.\u003c/p\u003e\n\u003cp\u003eObjective: This study aimed to investigate the correlation between TMB and CT imaging 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.\u003c/p\u003e\n\u003cp\u003eMethods: 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 \u0026lt;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 retraction, cavity formation, vascular convergence, and mediastinal lymph node enlargement.\u003c/p\u003e\n\u003cp\u003eResults: The high-TMB group exhibited a significantly larger maximum tumor diameter compared to the low-TMB group (t=3.456, P\u0026lt;0.05). The incidences of spiculation, lobulation, and vascular convergence were also significantly higher in the high-TMB group (χ²=5.678, 4.567, 4.789; P\u0026lt;0.05). Pleural retraction 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\u0026lt;0.05). Univariate logistic regression identified these features as significant predictors of high TMB (Wald=11.678, 5.672, 4.543, 4.752; P\u0026lt;0.05), and multivariate analysis confirmed their independent predictive value (Wald=10.175, 5.231, 4.134, 4.365; P\u0026lt;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.\u003c/p\u003e\n\u003cp\u003eConclusion: 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.\u003c/p\u003e","manuscriptTitle":"Correlation Between Tumor Mutational Burden and CT Radiographic Features in Lung Adenocarcinoma: A Diagnostic Accuracy Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:54:00","doi":"10.21203/rs.3.rs-8089262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bc81780-bbf8-47a3-aab5-c7cd9c65de50","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-13T11:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:54:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8089262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8089262","identity":"rs-8089262","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}