CT Feature-Based Nomogram for Predicting Tumor Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

CT Feature-Based Nomogram for Predicting Tumor Spread Through Air Spaces in Stage IA Lung Adenocarcinoma | 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 CT Feature-Based Nomogram for Predicting Tumor Spread Through Air Spaces in Stage IA Lung Adenocarcinoma Bin Luo, Han Yang, Ningbo Fan, Pengfei Duan, Zhesheng Wen, Peng Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6324646/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Cancer Imaging → Version 1 posted 9 You are reading this latest preprint version Abstract Objectives This research aimed to examine the relationships between clinicopathological characteristics and the occurrence of Spread Through Air Spaces (STAS) in patients with stage IA lung adenocarcinoma (LUAD) and to develop a preoperative prediction model. Methods Data from 1,375 patients with stage IA LUAD at Sun Yat-sen University Cancer Center were analyzed. Propensity score matching (PSM) was employed to match 141 STAS-positive patients with 282 STAS-negative patients. Both univariate and multivariate logistic regression analyses were performed to determine independent variables among 16 clinicopathological and 13 CT imaging characteristics. A nomogram prediction model was developed and evaluated via receiver operating characteristic (ROC) and decision curve analyses (DCAs). Results Multivariate analysis identified several independent risk factors. Irregular nodule shape (OR = 1.817, 95% CI: 1.106–2.986, p = 0.018), irregular margin (OR = 2.050, 95% CI: 1.218–3.449, p = 0.007), lobulation (OR = 2.235, 95% CI: 1.336–3.739, p = 0.002), and vascular convergence (OR = 5.032, 95% CI: 2.050–12.349, p < 0.001) were significantly associated with an increased risk of STAS. Compared with a consolidation tumor ratio (CTR) = 0% (reference), a CTR of 75–100% (OR = 7.086, 95% CI: 2.542–19.750, p < 0.001) and a CTR = 100% (OR = 11.502, 95% CI: 4.752–27.840, p < 0.001) were significantly associated with an increased risk of STAS. The nomogram was developed and internally validated, demonstrating good predictive accuracy (AUC = 0.812, 95% CI: 0.761–0.863) and clinical utility. Conclusion The nomogram reliably predicts STAS preoperatively and may assist in guiding surgical decision-making. Lung adenocarcinoma Spread through air spaces Clinicopathologic features CT features Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung cancer exhibits one of the highest incidence and mortality rates globally. According to the global cancer statistics from 2020, lung cancer constitutes approximately 12.4% of all cancer cases and accounts for about 18.7% of all cancer deaths, rendering it the most prevalent and lethal malignant tumor among males ( 1 ). LUAD is the most common histological subtype of non-small cell lung cancer (NSCLC) ( 2 ). Traditionally, lobectomy has been the standard surgical approach for early-stage lung cancer ( 3 ). However, advancements in chest CT screening and imaging technologies over the past two decades have led to the detection of more early-stage tumors, sparking interest in sublobar resection as an alternative for stage IA NSCLC patients ( 4 – 8 ). In recent years, STAS has been recognized as a distinct invasive pattern in lung cancer. First introduced by Kadota et al. in 2015 ( 9 ), STAS was incorporated into the World Health Organization (WHO) classification of lung cancer in the same year. STAS is defined as the spread of tumor cells into alveolar spaces beyond the primary tumor margin within the lung parenchyma ( 10 ). Current studies indicate that STAS is a risk factor for postoperative recurrence in patients with early-stage LUAD ( 9 , 11 – 17 ). Accumulating evidence suggests that STAS-positive patients undergoing sublobar resection have poorer disease-free survival (DFS) and overall survival (OS) compared to those undergoing lobectomy ( 14 , 18 – 24 ). Moreover, a multicenter retrospective study by Chen et al. demonstrated that postoperative adjuvant chemotherapy can improve the prognosis of stage IA LUAD patients with STAS who underwent sublobar resection ( 25 ). These findings underscore the importance of implementing appropriate therapeutic strategies for STAS-positive patients to enhance their prognosis. The diagnosis of STAS currently relies on adequate postoperative pathological sampling and examination. However, preoperative or intraoperative identification is even more critical for selecting optimal therapeutic strategies. Preoperative STAS detection remains challenging due to the lack of reliable diagnostic tools, and intraoperative frozen section analysis has limited accuracy in identifying STAS ( 26 ). This study aims to explore the risk factors associated with the occurrence of STAS in stage IA LUAD patients treated at our center and to establish an accurate STAS prediction model based on preoperative independent influencing factors, guiding the selection of optimal surgical strategies for these patients. Methods Study population We retrospectively analyzed postoperative pathological data from 3,699 patients diagnosed with lung cancer who received surgical treatment at Sun Yat-sen University Cancer Center between December 2020 and October 2022. Patients were categorized into STAS-positive and STAS-negative groups based on postoperative pathological findings. The inclusion criteria were as follows: (i) histopathological confirmation of LUAD following surgical treatment; (ii) postoperative pathological stage confirmed as stage IA according to the 8th edition of the TNM staging system by the International Association for the Study of Lung Cancer (IASLC); and (iii) availability of complete clinical data. The exclusion criteria were as follows: (i) lack of preoperative chest CT scans within three months before surgery; (ii) receipt of neoadjuvant treatments (e.g., chemotherapy, radiotherapy, immunotherapy, or targeted therapy); (iii) postoperative pathological diagnosis of carcinoma in situ or minimally invasive adenocarcinoma; (iv) presence of multiple invasive adenocarcinomas in the same lung lobe; (v) history of prior lung surgery; (vi) history of other malignancies. Given the imbalance in the number of cases between STAS-positive and STAS-negative groups, PSM was applied to reduce confounding bias. The matching variables included demographic factors (age, gender), body mass index (BMI), and smoking status. Matching was performed at ratios of 1:1, 1:2, and 1:3, with a 1:2 matching ratio ultimately selected to optimize the balance between group size and data quality. A detailed flowchart of the patient selection process, including the inclusion and exclusion criteria, is presented in Fig. 1 . Evaluation of pathological data Pathological specimens obtained from lung cancer surgeries were evaluated by two specialized pathologists in accordance with the WHO definition of STAS. Hematoxylin and eosin (H&E) staining was performed on all specimens, with representative findings illustrated in Fig. 2 . Discrepancies between the two pathologists were resolved through joint discussions. Histological classification adhered to the criteria jointly proposed by the IASLC, the American Thoracic Society, and the European Respiratory Society. The percentages of histological subtypes were recorded in increments of 5%, with subtypes comprising at least 5% of the tumor considered present. The predominant histological subtype, defined as the subtype with the highest percentage, was utilized for classification and further analysis ( 27 ). Additionally, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion were specifically documented, along with other relevant pathological features. Evaluation of CT data The chest CT images were analyzed independently by two radiologists specializing in lung cancer. The picture archiving and communication system (PACS) facilitated the image analysis. Both radiologists were blinded to the postoperative pathological diagnosis, including the STAS status, to ensure an unbiased evaluation. The radiological features assessed included nodule type, size, CTR, lobulation, vascular convergence, spiculation, and other relevant characteristics (Fig. 3 ). The long- and short-axis diameters of the nodule and its solid component were measured on the largest cross-sectional plane. Discrepancies between the two radiologists were resolved through re-evaluation by a third independent radiologist. Statistical analysis Statistical analyses were conducted using SPSS (v27.0) and R software (v4.4.0). PSM was performed with the 'MatchIt' package in R to balance covariates, including age, gender, BMI, and smoking status, between the two groups. Continuous variables were summarized as mean ± standard deviation when normally distributed, or as median (interquartile range) otherwise, with group comparisons conducted using t-tests or Wilcoxon rank-sum tests. Categorical variables were expressed as frequencies and percentages and compared using chi-square or Fisher's exact tests. Both univariable and multivariable logistic regression analyses were performed to identify significant predictors of STAS. The dataset was randomly divided into training and validation sets in a 70:30 ratio using the 'base' package in R. A nomogram was developed using the 'rms' package in R, and its predictive performance was assessed through ROC curves and calibration curves. Statistical significance was defined as a two-sided p value of less than 0.05. Results Demographic and clinical characteristics A total of 3,789 patients diagnosed with primary lung cancer were initially screened. After the implementation of the inclusion and exclusion criteria, 1,375 patients with stage IA LUAD were retained for the final analysis, comprising 141 STAS-positive and 1,234 STAS-negative patients. Following PSM, 141 STAS-positive and 282 STAS-negative patients were selected for further analysis (Fig. 1 ). The study population consisted of 220 males and 203 females, with a mean age of 57.7 ± 10.9 years. Among the patients in the STAS-positive group, 19.1% (27/141) underwent sublobar resection, which was significantly lower than the 39.7% (112/282) in the STAS-negative group ( P < 0.001). Univariate analysis indicated no statistically significant differences between the two groups in terms of comorbidities, serum carcinoembryonic antigen (CEA) levels, or family history of lung cancer ( P > 0.05). However, among patients who underwent postoperative genetic testing, the EGFR mutation status differed significantly between the groups ( P < 0.05), with wild-type EGFR being more prevalent in the STAS-positive group. A summary of the clinical characteristics of the study population is presented in Table 1 . Table 1 Relationships between the STAS and clinicopathological features. Variable All patients(n = 423) STAS(+) STAS(-) P Value Age(year) 57.7 ± 10.9 57.7 ± 11.1 57.7 ± 10.9 0.965 Gender 0.945 Male 220(52.0) 73(51.8) 147(52.1) Female 203(48.0) 68(48.2) 135(47.9) Smoking status 0.543 Present 150(35.5) 50(64.5) 100(35.5) Absent 273(64.5) 91(64.5) 182(35.5) BMI 23.3 ± 3.2 23.4 ± 3.3 23.3 ± 3.2 0.828 Family history of lung cancer 0.543 Present 37(8.7) 14(9.9) 23(8.2) Absent 386(91.3) 127(90.1) 259(91.8) Complications 0.24 Hypertension 91(19.5) 58(20.6) 33(18.9) Diabetes 43(9.2) 15(9.4) 28(9.1) Heart disease 16(3.4) 9(5.6) 7(2.3) Hepatitis 9(1.9) 5(3.1) 4(1.3) Others 32(6.9) 12(7.5) 20(6.5) CEA(mg/dL) 0.306 Normal 343(81.1) 111(78.7) 222(82.3) Abnormal 33(7.8) 15(10.6) 18(6.4) N/A 47(11.1) 15(10.6) 32(11.3) Surgery <0.001 Lobectomy 284(67.1) 114(80.9) 170(60.3) Sublobar resection 139(32.9) 27(19.1) 112(39.7) pT stage <0.001 T1a 71(16.8) 10(7.1) 61(21.6) T1b 232(54.8) 78(55.3) 154(54.6) T1c 120(28.4) 53(37.6) 67(23.8) Tumor differentiation < 0.001 Well 30(7.1) 3(2.1) 27(9.6) Moderate 333(78.7) 94(66.7) 239(84.7) poor 60(14.2) 40(31.2) 16(5.7) Histologic subtypes Lepidic predominant 40(9.5) 1(0.7) 39(13.8) <0.0001 Acinar predominant 282(66.6) 92(65.2) 190(67.4) 0.662 Papillary predominant 64(15.1) 26(18.5) 38(13.4) 0.179 Micropapillary predominant 7(1.7) 6(4.3) 1(0.4) 0.006 a Solid predominant 8(1.9) 5(3.5) 3(1.1) 0.077 Others b 22(5.2) 11(7.8) 11(3.9) 0.089 Micropapillary Component 0.001 Present 105(24.8) 80(56.7) 25(8.9) Absent 318(75.2) 61(43.3) 257(91.1) Lymphovascular invasion <0.001 Present 25(5.9) 21(14.9) 4(1.4) Absent 398(94.1) 120(85.1) 278(98.6) Perineural invasion 1.000 Present 2(0.5) 1(0.4) 1(0.7) Absent 421(99.5) 281(99.6) 140(99.3) EGFR Mutation 125(61.0) 38(51.4) 87(66.4) 0.034 ALK Rearrangement 7(3.6) 5(7.0) 2(1.6) 0.103 EGFR: epidermal growth factor receptor; ALK: anaplastic lymphoma kinase; a: Fisher's exact test; b: other pathological subtypes, including complex glandular patterns and the mucinous type. Pathological features Univariate analysis of pathological characteristics revealed significant differences between the STAS-positive and STAS-negative groups in terms of tumor T stage, degree of differentiation, and predominant pathological subtype. In the STAS-positive group, 56.7% of tumors exhibited a micropapillary structure, and 14.9% demonstrated lymphovascular invasion, both of which were significantly greater than those in the STAS-negative group. Detailed comparisons of pathological characteristics are provided in Table 1 . Radiological features Univariate analysis of chest CT features revealed that both the maximum nodule diameter and the maximum diameter of the solid component within the nodule were significantly larger in the STAS-positive group compared to the STAS-negative group (19.2 mm vs. 16.5 mm and 15.0 mm vs. 8.6 mm, respectively; both P < 0.001). The CTR was also significantly different between the groups ( P 0.05). However, the type of nodule was significantly different ( P < 0.001). Radiological features such as irregular nodule shape, irregular margins, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion were significantly more prevalent in the STAS-positive group. These findings are summarized in Table 2 and visually illustrated in Supplementary Fig. 4. Table 2 Relationships between the STAS and CT features. Variable All patients(n = 423) STAS(+) STAS(-) P Value Maximum tumor diameter (mm) 17.4 ± 6.3 19.3 ± 5.9 16.5 ± 6.2 <0.001 a Maximum solid component diameter (mm) 10.7 ± 8.3 15.1 ± 7.9 8.6 ± 7.6 <0.001 a CTR (%) <0.001 0 78(18.4) 7(5.0) 71(25.2) 0<CTR ≤ 25 26(6.1) 4(2.8) 22(7.8) 25<CTR ≤ 50 73(17.3) 16(11.3) 57(20.2) 50<CTR ≤ 75 71(16.8) 18(12.8) 53(18.8) 75<CTR<100 43(10.2) 21(14.9) 22(7.8) 100 132(31.2) 75(53.2) 57(20.2) Nodule type <0.001 pGGO 78(18.4) 7(5.0) 71(25.2) Part solid 213(50.4) 59(41.8) 154(54.6) Solid 132(31.2) 75(53.2) 57(20.2) Nodule location 0.163 RUL 143(33.8) 39(27.7) 104(36.9) RML 28(6.6) 7(5.0) 21(7.4) RLL 89(21.0) 37(26.2) 52(18.4) LUL 106(25.1) 39(27.7) 67(23.8) LLL 57(13.5) 38(13.5) 19(13.5) Nodule Shape 0.002 Irregular 222(52.5) 89(63.1) 133(47.2) Round to oval 201(47.5) 52(36.9) 149(52.8) Irregular margin 0.003 Present 270(63.8) 104(73.8) 166(58.8) Absent 153(36.2) 37(26.2) 116(41.1) Lobulation <0.0001 Present 258(61.0) 107(75.9) 151(53.5) Absent 165(39.0) 34(24.1) 131(46.5) Spiculation <0.001 Present 180(42.6) 89(63.1) 91(32.3) Absent 243(57.4) 52(36.9) 191(67.7) Cavitation 0.418 Present 75(17.7) 28(19.9) 47(16.7) Absent 348(82.3) 113(80.1) 235(83.3) Vascular convergence <0.001 Present 368(87.0) 134(95.0) 234(83.0) Absent 55( 13 ) 7( 5 ) 48( 17 ) Air bronchogram 0.003 Present 173(40.9) 72(51.1) 101(35.8) Absent 250(59.1) 69(48.9) 181(64.2) Pleural invasion 0.002 Present 258(61.0) 101(71.6) 157(55.7) Absent 165(39.0) 40(28.4) 125(44.3) CTR: Consolidation tumor ratio; a: Fisher’s exact test. Multivariate logistic regression analysis Clinically significant pathological and CT features identified through univariate analysis were further evaluated using multivariate logistic regression analysis. This multivariate analysis revealed several independent risk factors associated with STAS. Among the tumor differentiation subgroups, well-differentiated tumors were significantly correlated with STAS ( p = 0.009). Lymphovascular invasion (OR = 4.677, 95% CI: 1.371–15.955, p = 0.014) and micropapillary structure (OR = 9.08, 95% CI: 5.172–15.938, p < 0.001) were also significantly associated with increased risk, whereas lepidic predominant histologic subtypes (OR = 0.069, 95% CI: 0.008–0.575, p = 0.013) served as protective factors against STAS (see Table 3 ). In terms of CT features, nodule shape (OR = 1.817, 95% CI: 1.106–2.986, p = 0.018), irregular margin (OR = 2.050, 95% CI: 1.218–3.449, p = 0.007), lobulation (OR = 2.235, 95% CI: 1.336–3.739, p = 0.002), and vascular convergence (OR = 5.032, 95% CI: 2.050–12.349, p < 0.001) were significantly associated with an increased risk. For the CTR, compared with CTR = 0% (reference), both CTR 75–100% (OR = 7.086, 95% CI: 2.542–19.750, p < 0.001) and CTR = 100% (OR = 11.502, 95% CI: 4.752–27.840, p < 0.001) were significantly associated with an increased risk of STAS. Other radiological features did not show statistical significance (see Table 4 ). Table 3 Multivariate analysis of pathological features related to STAS. Variable OR 95%CI P Value Tumor differentiation Well 1 ─ 0.009 Moderate 1.542 0.369–6.439 0.553 Poor 4.762 0.996–22.776 0.051 Lymphovascular invasion 4.677 1.371–15.955 0.014 Micropapillary structure 9.08 5.172–15.938 <0.001 Lepidic predominant Histologic subtypes 0.069 0.008–0.575 0.013 Table 4 Multivariate analysis of CT features related to STAS. Variable OR 95%CI P Value Nodule Shape 1.817 1.106–2.986 0.018 Irregular margin 2.050 1.218–3.449 0.007 Lobulation 2.235 1.336–3.739 0.002 Vascular convergence 5.032 2.050-12.349 <0.001 CTR% 0 1 ─ <0.001 0<CTR ≤ 25 1.634 0.412–6.481 0.485 25<CTR ≤ 50 2.118 0.785–5.712 0.138 50<CTR ≤ 75 2.533 0.952–6.738 0.063 75<CTR<100 7.086 2.542–19.750 <0.001 100 11.502 4.752–27.840 <0.001 Model development and evaluation Based on multivariate logistic regression analysis, we recognized independent risk factors derived from the features of preoperative CT scans., including nodule shape, irregular margin, lobulation sign, vascular convergence sign, and the CTR, all of which were statistically significant. Using the 'base' package in R software, we randomly divided the dataset into two groups at a ratio of 0.7:0.3, employing a fixed random seed to ensure reproducibility. The 0.7 group served as the training set, while the 0.3 group was designated as the internal validation set. Subsequently, the 'rms' package in R was utilized to construct a nomogram model based on the significant predictors identified in the training set to predict the occurrence of STAS in stage IA LUAD. As illustrated in Fig. 5 , each line on the nomogram corresponds to a variable, with the model assigning specific points to each variable. By aggregating these points, the total score can be used to determine the probability of STAS positivity. The nomogram demonstrated high predictive accuracy in the training set, achieving an AUC of 0.812 (95% CI: 0.761–0.863). Internal validation further corroborated its robustness, yielding an AUC of 0.781 (95% CI: 0.699–0.863). These AUC values, as shown in Figs. 6 a and 6 b, suggest a strong discriminatory ability of the model in predicting the occurrence of STAS. The bootstrap method was employed for internal validation, and the calibration curve (Figs. 6 c and 6 d) demonstrates good alignment between predicted and actual probabilities, confirming the model's accuracy. Furthermore, the decision curve analysis (DCA) illustrated in Fig. 7 indicates that the nomogram model provides a higher clinical net benefit across the risk threshold range of 0.1 to 0.8, underscoring its potential value in clinical practice. Discussion This study identified key clinicopathological factors associated with STAS in stage IA LUAD, including tumor differentiation, micropapillary structure, lymphovascular invasion, and lepidic predominant subtype. STAS was more frequent in tumors with moderate to poor differentiation, and the micropapillary structure was significantly more prevalent in STAS-positive patients, consistent with previous studies ( 11 , 24 , 28 – 33 ). Lymphovascular invasion also showed a significant correlation with STAS, highlighting its role in tumor spread ( 9 , 13 , 15 ). A meta-analysis further supported that STAS is common in micropapillary subtypes but rare in lepidic subtypes, aligning with our findings ( 34 ). In terms of molecular features, some studies have reported a significant correlation between STAS positivity and wild-type EGFR ( 11 , 12 ). However, Toyokawa et al. (2018) found no such association with EGFR mutations ( 16 ). In our cohort, STAS-positive patients predominantly exhibited wild-type EGFR ( P < 0.05), which is consistent with its association with poor prognosis in LUAD ( 35 ). Although 71.4% of patients with ALK rearrangements were STAS-positive, the difference was not statistically significant, likely due to the limited sample size. Additionally, while Shiono et al. reported higher CEA levels in STAS-positive patients ( 36 ), our findings did not show a significant difference. Larger cohorts are needed to validate these associations. While STAS is traditionally confirmed through postoperative pathological examination, numerous retrospective studies have explored its correlation with preoperative CT features, aiming to predict its occurrence ( 17 , 37 – 40 ). Given that STAS is a microscopic phenomenon beyond the spatial resolution of current CT technology, it is suggested that indirect indicators should be utilized for its prediction ( 17 ). Studies by Toyokawa et al. and Kim et al. identified significant associations between STAS and CT features such as maximum nodule diameter, solid component proportion, and CTR, highlighting their predictive potential ( 16 , 17 , 41 ). In our cohort of 423 patients with stage IA LUAD, STAS was significantly associated with the maximum diameters of both the nodule and its solid component, as well as the CTR. Notably, CTR emerged as an independent risk factor for predicting STAS occurrence, with higher CTR values correlating with an increased likelihood of STAS. Consequently, CTR, which is readily measurable by CT, may facilitate the selection of the optimal surgical strategy. Regarding nodule type, Kim et al. reported no STAS in pure ground-glass nodules (GGNs) ( 17 ). However, other studies have reported the presence of STAS in pure GGNs, albeit with a relatively low incidence( 37 , 41 , 42 ). In our study, 5% (7/141) of the STAS-positive patients exhibited pure GGNs, and 14.1% (20/141) had nodules where the ground-glass component predominated. These findings suggest that the choice between sublobar resection and lobectomy should not be based solely on the presence of pure GGNs but should also incorporate a comprehensive evaluation of imaging features. Consistent with Shiono et al., we found that pure solid nodules had a significantly higher STAS positivity rate compared to subsolid nodules ( 36 ). Additionally, several CT features, such as irregular margin, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion, were frequently observed in STAS-positive patients ( 38 , 41 , 43 , 44 ). Similar to these studies, our research confirms that irregular nodule shape, irregular margin, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion are commonly seen in CT images of stage IA LUAD patients with STAS. Among these, irregular nodule shape, irregular margin, lobulation sign, and vascular convergence sign were identified as independent risk factors for STAS occurrence in stage IA LUAD. These findings highlight the utility of preoperative chest CT imaging in predicting STAS and optimizing surgical planning for stage IA LUAD patients. The clinical significance of our findings is noteworthy; however, several limitations should be acknowledged. First, as a retrospective cohort study focusing exclusively on surgically treated stage IA LUAD patients, there is an inherent risk of selection bias, which may have influenced the representativeness of the sample population. Second, the relatively short postoperative follow-up period limited the evaluation of long-term outcomes, such as disease recurrence and overall prognosis. Future studies with longer follow-up durations are necessary to further elucidate the prognostic impact of STAS. Finally, the single-center design restricts the generalizability of our results. Multicenter studies involving diverse patient populations are needed to validate and strengthen the reliability of these findings. Conclusion In summary, this study analyzed the clinicopathologic and CT features associated with STAS in patients with stage IA LUAD. We identified lower tumor differentiation, non-lepidic predominant subtypes, micropapillary structure, and lymphovascular invasion as significant clinicopathologic risk factors for STAS. Additionally, CT features such as solid components, irregular shape, irregular margin, lobulation, and vascular convergence were significant predictors of STAS. On the basis of these five CT features, we developed a nomogram model with high predictive accuracy and clinical applicability. This model provides a valuable tool for preoperative STAS diagnosis and can assist thoracic surgeons in optimizing surgical strategies for stage IA LUAD patients. Abbreviations LUAD Lung Adenocarcinoma STAS Spread through Air Spaces PSM Propensity Score Matching NSCLC Non-small Cell Lung Cancer CT Computed Tomography ROC Receiver Operating Characteristic AUC Area Under the Curve DCA Decision Curve Analysis CI Confidence Interval DFS Disease-Free Survival OS Overall Survival IASLC International Association for the Study of Lung Cancer PACS Picture Archiving and Communication System BMI Body Mass Index CTR Consolidation Tumor Ratio CEA Carcinoembryonic Antigen GGN Ground-Glass Nodules Declarations Ethics approval and consent to participate The authors confirm that all procedures involving human participants received approval from the ethics committee at Sun Yat-sen University Cancer Center. The Institutional Review Board waived the requirement for written informed consent. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available, as they are part of an ongoing longitudinal study that requires controlled access to maintain research integrity. However, they are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors state that this work has not received any funding. Authors' contributions BL and HY contributed to the conception and design of the study. NF collected and organized the clinical data. PD performed the statistical analysis. ZW supervised the study and revised the manuscript. PL provided overall guidance and finalized the manuscript. All the authors read and approved the final manuscript. 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Warth A, Muley T, Kossakowski CA, Goeppert B, Schirmacher P, Dienemann H, et al. Prognostic Impact of Intra-alveolar Tumor Spread in Pulmonary Adenocarcinoma. Am J Surg Pathol. 2015;39(6):793–801. Morimoto J, Nakajima T, Suzuki H, Nagato K, Iwata T, Yoshida S, et al. Impact of free tumor clusters on prognosis after resection of pulmonary adenocarcinoma. J Thorac Cardiovasc Surg. 2016;152(1):64–e721. Dai C, Xie H, Su H, She Y, Zhu E, Fan Z, et al. Tumor Spread through Air Spaces Affects the Recurrence and Overall Survival in Patients with Lung Adenocarcinoma > 2 to 3 cm. J Thorac Oncol. 2017;12(7):1052–60. Uruga H, Fujii T, Fujimori S, Kohno T, Kishi K. Semiquantitative Assessment of Tumor Spread through Air Spaces (STAS) in Early-Stage Lung Adenocarcinomas. J Thorac Oncol. 2017;12(7):1046–51. Toyokawa G, Yamada Y, Tagawa T, Kozuma Y, Matsubara T, Haratake N, et al. Significance of Spread Through Air Spaces in Resected Pathological Stage I Lung Adenocarcinoma. Ann Thorac Surg. 2018;105(6):1655–63. Kim SK, Kim TJ, Chung MJ, Kim TS, Lee KS, Zo JI, et al. Lung Adenocarcinoma: CT Features Associated with Spread through Air Spaces. Radiology. 2018;289(3):831–40. Eguchi T, Kameda K, Lu S, Bott MJ, Tan KS, Montecalvo J, et al. Lobectomy Is Associated with Better Outcomes than Sublobar Resection in Spread through Air Spaces (STAS)-Positive T1 Lung Adenocarcinoma: A Propensity Score-Matched Analysis. J Thorac Oncol. 2019;14(1):87–98. Kadota K, Kushida Y, Kagawa S, Ishikawa R, Ibuki E, Inoue K, et al. Limited Resection Is Associated With a Higher Risk of Locoregional Recurrence than Lobectomy in Stage I Lung Adenocarcinoma With Tumor Spread Through Air Spaces. Am J Surg Pathol. 2019;43(8):1033–41. Ren Y, Xie H, Dai C, She Y, Su H, Xie D, et al. Prognostic Impact of Tumor Spread Through Air Spaces in Sublobar Resection for 1A Lung Adenocarcinoma Patients. Ann Surg Oncol. 2019;26(6):1901–8. Shiono S, Endo M, Suzuki K, Yarimizu K, Hayasaka K, Yanagawa N. Spread Through Air Spaces Is a Prognostic Factor in Sublobar Resection of Non-Small Cell Lung Cancer. Ann Thorac Surg. 2018;106(2):354–60. Shiono S, Endo M, Suzuki K, Hayasaka K, Yanagawa N. Spread through air spaces in lung cancer patients is a risk factor for pulmonary metastasis after surgery. J Thorac Dis. 2019;11(1):177–87. Bains S, Eguchi T, Warth A, Yeh YC, Nitadori JI, Woo KM, et al. Procedure-Specific Risk Prediction for Recurrence in Patients Undergoing Lobectomy or Sublobar Resection for Small (≤ 2 cm) Lung Adenocarcinoma: An International Cohort Analysis. J Thorac Oncol. 2019;14(1):72–86. Masai K, Sakurai H, Sukeda A, Suzuki S, Asakura K, Nakagawa K, et al. Prognostic Impact of Margin Distance and Tumor Spread Through Air Spaces in Limited Resection for Primary Lung Cancer. J Thorac Oncol. 2017;12(12):1788–97. Chen D, Wang X, Zhang F, Han R, Ding Q, Xu X, et al. Could tumor spread through air spaces benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-institutional study. Ther Adv Med Oncol. 2020;12:1758835920978147. Mino-Kenudson M. Significance of tumor spread through air spaces (STAS) in lung cancer from the pathologist perspective. Transl Lung Cancer Res. 2020;9(3):847–59. Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6(2):244–85. Morales-Oyarvide V, Mino-Kenudson M. Tumor islands and spread through air spaces: Distinct patterns of invasion in lung adenocarcinoma. Pathol Int. 2016;66(1):1–7. Miyoshi T, Satoh Y, Okumura S, Nakagawa K, Shirakusa T, Tsuchiya E, et al. Early-stage lung adenocarcinomas with a micropapillary pattern, a distinct pathologic marker for a significantly poor prognosis. Am J Surg Pathol. 2003;27(1):101–9. Makimoto Y, Nabeshima K, Iwasaki H, Miyoshi T, Enatsu S, Shiraishi T, et al. Micropapillary pattern: a distinct pathological marker to subclassify tumours with a significantly poor prognosis within small peripheral lung adenocarcinoma (=20 mm) with mixed bronchioloalveolar and invasive subtypes (Noguchi’s type C tumours)</at. Histopathology. 2005;46(6):677–84. Nitadori Jichi, Bograd AJ, Kadota K, Sima CS, Rizk NP, Morales EA, et al. Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2cm or smaller. J Natl Cancer Inst. 2013;105(16):1212–20. Yi E, Bae MK, Cho S, Chung JH, Jheon S, Kim K. Pathological prognostic factors of recurrence in early stage lung adenocarcinoma. ANZ J Surg. 2018;88(4):327–31. Shih AR, Mino-Kenudson M. Updates on spread through air spaces (STAS) in lung cancer. Histopathology. 2020;77(2):173–80. Pyo JS, Kim NY. Clinicopathological Impact of the Spread through Air Space in Non-Small Cell Lung Cancer: A Meta-Analysis. Diagnostics (Basel). 2022;12(5):1112. Yoon HY, Ryu JS, Sim YS, Kim D, Lee SY, Choi J, et al. Clinical significance of EGFR mutation types in lung adenocarcinoma: A multi-centre Korean study. PLoS ONE. 2020;15(2):e0228925. Shiono S, Yanagawa N. Spread through air spaces is a predictive factor of recurrence and a prognostic factor in stage I lung adenocarcinoma. Interact Cardiovasc Thorac Surg. 2016;23(4):567–72. Zhang Z, Liu Z, Feng H, Xiao F, Shao W, Liang C, et al. Predictive value of radiological features on spread through air space in stage cIA lung adenocarcinoma. J Thorac Dis. 2020;12(11):6494–504. Toyokawa G, Yamada Y, Tagawa T, Kamitani T, Yamasaki Y, Shimokawa M, et al. Computed tomography features of resected lung adenocarcinomas with spread through air spaces. J Thorac Cardiovasc Surg. 2018;156(4):1670–e16764. Ding Y, Chen Y, Wen H, Li J, Chen J, Xu M, et al. Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg. 2022;62(3):ezac248. Li C, Jiang C, Gong J, Wu X, Luo Y, Sun G. A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma. Quant Imaging Med Surg. 2020;10(10):1984–93. de Margerie-Mellon C, Onken A, Heidinger BH, VanderLaan PA, Bankier AA. CT Manifestations of Tumor Spread Through Airspaces in Pulmonary Adenocarcinomas Presenting as Subsolid Nodules. J Thorac Imaging. 2018;33(6):402–8. Zhong Y, Xu Y, Deng J, Wang T, Sun X, Chen D, et al. Prognostic impact of tumour spread through air space in radiological subsolid and pure solid lung adenocarcinoma. Eur J Cardiothorac Surg. 2021;59(3):624–32. Qi L, Xue K, Cai Y, Lu J, Li X, Li M. Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma. Front Oncol. 2020;10:548430. Gu Y, Zheng B, Zhao T, Fan Y. Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis. J Thorac Imaging. 2023;38(2):W19–29. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Cancer Imaging → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 05 May, 2025 Reviews received at journal 03 May, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers invited by journal 10 Apr, 2025 Editor assigned by journal 31 Mar, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 27 Mar, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6324646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441990577,"identity":"88ec1f19-e282-43f6-8f07-8fe6acecda65","order_by":0,"name":"Bin Luo","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Luo","suffix":""},{"id":441990578,"identity":"276b6c09-e8ec-4932-b25f-973ab9be6ea6","order_by":1,"name":"Han Yang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Yang","suffix":""},{"id":441990580,"identity":"3cac9369-d3d0-44de-bdf7-1c01fdcee471","order_by":2,"name":"Ningbo Fan","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Ningbo","middleName":"","lastName":"Fan","suffix":""},{"id":441990582,"identity":"f7203541-1a46-46d7-8b58-bed729da2720","order_by":3,"name":"Pengfei Duan","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Duan","suffix":""},{"id":441990587,"identity":"80f651bc-7543-4a8a-9741-e36c20306a21","order_by":4,"name":"Zhesheng Wen","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Zhesheng","middleName":"","lastName":"Wen","suffix":""},{"id":441990590,"identity":"1f29a5ba-33e6-49a5-aee1-1ba9e7b64227","order_by":5,"name":"Peng Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPmYILUe8FjaoFmMStEDpxAbitbDzHnvwcUdt+vwZ6c8kftQwyJn3L2D8XIDXYXzphjPPHM/dcCPHTLLnGIOxzI0HzNIz8GrhMZPmbTuWu0Eih02asYEhcYbEAaAgIS1/246lywMdRoIWxraaBIYbCWYQLfwNBLWYG/a2HTDccOaNsWXPMQljCQnGZml8Wvj5z5g9+NlWJy/fnv7wxo8aGzkJ/sMHP+PTwgCJmsMMDAIJII4EEBGOI5CWOqB9B2AWH8CtdhSMglEwCkYkAABZaEC5yFcRSQAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-03-28 04:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6324646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6324646/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40644-025-00893-x","type":"published","date":"2025-06-11T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80712716,"identity":"0cf286ed-4ddd-431f-a913-c9f00e284ef6","added_by":"auto","created_at":"2025-04-16 09:19:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":595732,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/149a5052aba440d233510b38.png"},{"id":80712709,"identity":"dd40180e-a355-441f-940c-34893de03492","added_by":"auto","created_at":"2025-04-16 09:19:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1256495,"visible":true,"origin":"","legend":"\u003cp\u003eHistopathological features of patients with stage IA LUAD who are positive for STAS. This figure shows the diagnosis of STAS through H\u0026amp;E staining. The tumor that spreads through air spaces is located outside the edge of the primary tumor (black dashed line). \u003cstrong\u003e(a)\u003c/strong\u003e In a 33-year-old male patient with LUAD, the pathological subtype was mainly acinar; \u003cstrong\u003e(b)\u003c/strong\u003e In a 38-year-old male patient with LUAD, the pathological subtype was mainly micropapillary.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/31ee3a530172000d76e724e5.png"},{"id":80714056,"identity":"4bef010d-8fdf-440e-931f-fd0c032215da","added_by":"auto","created_at":"2025-04-16 09:27:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":499741,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative imaging features in patients with stage IA LUAD who are positive for STAS. \u003cstrong\u003e(a)\u003c/strong\u003e Air bronchogram sign; \u003cstrong\u003e(b)\u003c/strong\u003eLobulation sign; \u003cstrong\u003e(c)\u003c/strong\u003e Vascular convergence sign; \u003cstrong\u003e(d)\u003c/strong\u003e Spiculation sign.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/1b2c5d3c066438f00f48e7b2.png"},{"id":80712720,"identity":"9013392e-d652-4d85-8b31-f1151dd6b2bf","added_by":"auto","created_at":"2025-04-16 09:19:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":698044,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of STAS-positive and STAS-negative features in lung nodules.\u003cstrong\u003e (a)\u003c/strong\u003e The clustered column chart compares the predominant pathological subtypes between the STAS-positive and STAS-negative groups. \u003cstrong\u003e(b)\u003c/strong\u003e The stacked bar chart illustrates the significnat differences in nodule types between the STAS-positive and STAS-negative groups. \u003cstrong\u003e(c)\u003c/strong\u003e The stacked bar chart shows that STAS is more frequently observed in patients with irregular nodules, irregular margins, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion signs. **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ns, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/e73e8ec619cb5172b4a120b3.png"},{"id":80712724,"identity":"8ae275f6-a06d-4d64-b455-3c227d0fc54e","added_by":"auto","created_at":"2025-04-16 09:19:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":840346,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model for predicting the occurrence of STAS in stage IA LUAD.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/7f459a1dd6476f5b9381e536.png"},{"id":80714069,"identity":"080398da-e915-4560-ab86-e07b52a536b3","added_by":"auto","created_at":"2025-04-16 09:27:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1341127,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of the nomogram model in the training set and internal validation set. ROC curves for the nomogram model for predicting stage IA LUAD STAS in the training set \u003cstrong\u003e(a)\u003c/strong\u003e and validation set \u003cstrong\u003e(b)\u003c/strong\u003e. The calibration curves for evaluating the nomogram model in the training set\u003cstrong\u003e (c)\u003c/strong\u003e and validation set\u003cstrong\u003e (d)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/f8a7cd90b37fc70176d2e3e8.png"},{"id":80712728,"identity":"25f838b5-b1ba-46c4-b1af-0243130abb32","added_by":"auto","created_at":"2025-04-16 09:19:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":551667,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the line chart model.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/03f26a012c5d669522eff8cc.png"},{"id":84726736,"identity":"be4034cf-b63d-4970-af75-bd3b2ad554fb","added_by":"auto","created_at":"2025-06-16 16:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7192589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6324646/v1/5172cb34-94ae-4b6c-8d8c-a9bd0c5639c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CT Feature-Based Nomogram for Predicting Tumor Spread Through Air Spaces in Stage IA Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer exhibits one of the highest incidence and mortality rates globally. According to the global cancer statistics from 2020, lung cancer constitutes approximately 12.4% of all cancer cases and accounts for about 18.7% of all cancer deaths, rendering it the most prevalent and lethal malignant tumor among males (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). LUAD is the most common histological subtype of non-small cell lung cancer (NSCLC) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Traditionally, lobectomy has been the standard surgical approach for early-stage lung cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, advancements in chest CT screening and imaging technologies over the past two decades have led to the detection of more early-stage tumors, sparking interest in sublobar resection as an alternative for stage IA NSCLC patients (\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, STAS has been recognized as a distinct invasive pattern in lung cancer. First introduced by Kadota et al. in 2015 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), STAS was incorporated into the World Health Organization (WHO) classification of lung cancer in the same year. STAS is defined as the spread of tumor cells into alveolar spaces beyond the primary tumor margin within the lung parenchyma (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Current studies indicate that STAS is a risk factor for postoperative recurrence in patients with early-stage LUAD (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Accumulating evidence suggests that STAS-positive patients undergoing sublobar resection have poorer disease-free survival (DFS) and overall survival (OS) compared to those undergoing lobectomy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Moreover, a multicenter retrospective study by Chen et al. demonstrated that postoperative adjuvant chemotherapy can improve the prognosis of stage IA LUAD patients with STAS who underwent sublobar resection (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings underscore the importance of implementing appropriate therapeutic strategies for STAS-positive patients to enhance their prognosis.\u003c/p\u003e \u003cp\u003eThe diagnosis of STAS currently relies on adequate postoperative pathological sampling and examination. However, preoperative or intraoperative identification is even more critical for selecting optimal therapeutic strategies. Preoperative STAS detection remains challenging due to the lack of reliable diagnostic tools, and intraoperative frozen section analysis has limited accuracy in identifying STAS (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to explore the risk factors associated with the occurrence of STAS in stage IA LUAD patients treated at our center and to establish an accurate STAS prediction model based on preoperative independent influencing factors, guiding the selection of optimal surgical strategies for these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed postoperative pathological data from 3,699 patients diagnosed with lung cancer who received surgical treatment at Sun Yat-sen University Cancer Center between December 2020 and October 2022. Patients were categorized into STAS-positive and STAS-negative groups based on postoperative pathological findings.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (i) histopathological confirmation of LUAD following surgical treatment; (ii) postoperative pathological stage confirmed as stage IA according to the 8th edition of the TNM staging system by the International Association for the Study of Lung Cancer (IASLC); and (iii) availability of complete clinical data.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (i) lack of preoperative chest CT scans within three months before surgery; (ii) receipt of neoadjuvant treatments (e.g., chemotherapy, radiotherapy, immunotherapy, or targeted therapy); (iii) postoperative pathological diagnosis of carcinoma in situ or minimally invasive adenocarcinoma; (iv) presence of multiple invasive adenocarcinomas in the same lung lobe; (v) history of prior lung surgery; (vi) history of other malignancies.\u003c/p\u003e \u003cp\u003eGiven the imbalance in the number of cases between STAS-positive and STAS-negative groups, PSM was applied to reduce confounding bias. The matching variables included demographic factors (age, gender), body mass index (BMI), and smoking status. Matching was performed at ratios of 1:1, 1:2, and 1:3, with a 1:2 matching ratio ultimately selected to optimize the balance between group size and data quality. A detailed flowchart of the patient selection process, including the inclusion and exclusion criteria, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation of pathological data\u003c/h3\u003e\n\u003cp\u003ePathological specimens obtained from lung cancer surgeries were evaluated by two specialized pathologists in accordance with the WHO definition of STAS. Hematoxylin and eosin (H\u0026amp;E) staining was performed on all specimens, with representative findings illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Discrepancies between the two pathologists were resolved through joint discussions. Histological classification adhered to the criteria jointly proposed by the IASLC, the American Thoracic Society, and the European Respiratory Society. The percentages of histological subtypes were recorded in increments of 5%, with subtypes comprising at least 5% of the tumor considered present. The predominant histological subtype, defined as the subtype with the highest percentage, was utilized for classification and further analysis (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, the degree of tumor differentiation, lymphovascular invasion, and perineural invasion were specifically documented, along with other relevant pathological features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEvaluation of CT data\u003c/h3\u003e\n\u003cp\u003eThe chest CT images were analyzed independently by two radiologists specializing in lung cancer. The picture archiving and communication system (PACS) facilitated the image analysis. Both radiologists were blinded to the postoperative pathological diagnosis, including the STAS status, to ensure an unbiased evaluation. The radiological features assessed included nodule type, size, CTR, lobulation, vascular convergence, spiculation, and other relevant characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The long- and short-axis diameters of the nodule and its solid component were measured on the largest cross-sectional plane. Discrepancies between the two radiologists were resolved through re-evaluation by a third independent radiologist.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS (v27.0) and R software (v4.4.0). PSM was performed with the 'MatchIt' package in R to balance covariates, including age, gender, BMI, and smoking status, between the two groups. Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation when normally distributed, or as median (interquartile range) otherwise, with group comparisons conducted using t-tests or Wilcoxon rank-sum tests. Categorical variables were expressed as frequencies and percentages and compared using chi-square or Fisher's exact tests. Both univariable and multivariable logistic regression analyses were performed to identify significant predictors of STAS. The dataset was randomly divided into training and validation sets in a 70:30 ratio using the 'base' package in R. A nomogram was developed using the 'rms' package in R, and its predictive performance was assessed through ROC curves and calibration curves. Statistical significance was defined as a two-sided \u003cem\u003ep\u003c/em\u003e value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 3,789 patients diagnosed with primary lung cancer were initially screened. After the implementation of the inclusion and exclusion criteria, 1,375 patients with stage IA LUAD were retained for the final analysis, comprising 141 STAS-positive and 1,234 STAS-negative patients. Following PSM, 141 STAS-positive and 282 STAS-negative patients were selected for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study population consisted of 220 males and 203 females, with a mean age of 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years. Among the patients in the STAS-positive group, 19.1% (27/141) underwent sublobar resection, which was significantly lower than the 39.7% (112/282) in the STAS-negative group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Univariate analysis indicated no statistically significant differences between the two groups in terms of comorbidities, serum carcinoembryonic antigen (CEA) levels, or family history of lung cancer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, among patients who underwent postoperative genetic testing, the EGFR mutation status differed significantly between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with wild-type EGFR being more prevalent in the STAS-positive group. A summary of the clinical characteristics of the study population is presented 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\u003eRelationships between the STAS and clinicopathological features.\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients(n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTAS(+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTAS(-)\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\u003e\u003cb\u003eAge(year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of lung cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386(91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259(91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEA(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343(81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222(82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284(67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170(60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSublobar resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139(32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154(54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor differentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333(78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239(84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic subtypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLepidic predominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinar predominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282(66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190(67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary predominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicropapillary predominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid predominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers \u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(3.9)\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\u003e\u003cb\u003eMicropapillary Component\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318(75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphovascular invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278(98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerineural invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421(99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281(99.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGFR Mutation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87(66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALK Rearrangement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\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\u003eEGFR: epidermal growth factor receptor; ALK: anaplastic lymphoma kinase; a: Fisher's exact test; b: other pathological subtypes, including complex glandular patterns and the mucinous type.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathological features\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis of pathological characteristics revealed significant differences between the STAS-positive and STAS-negative groups in terms of tumor T stage, degree of differentiation, and predominant pathological subtype. In the STAS-positive group, 56.7% of tumors exhibited a micropapillary structure, and 14.9% demonstrated lymphovascular invasion, both of which were significantly greater than those in the STAS-negative group. Detailed comparisons of pathological characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eRadiological features\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis of chest CT features revealed that both the maximum nodule diameter and the maximum diameter of the solid component within the nodule were significantly larger in the STAS-positive group compared to the STAS-negative group (19.2 mm vs. 16.5 mm and 15.0 mm vs. 8.6 mm, respectively; both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CTR was also significantly different between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While the right upper lobe was the most common location for nodules (33.8% of cases), no significant difference in nodule location was observed between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the type of nodule was significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Radiological features such as irregular nodule shape, irregular margins, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion were significantly more prevalent in the STAS-positive group. These findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visually illustrated in Supplementary Fig.\u0026nbsp;4.\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\u003eRelationships between the STAS and CT features.\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients(n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTAS(+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTAS(-)\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\u003e\u003cb\u003eMaximum tumor diameter (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum solid component diameter (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026lt;CTR\u0026lt;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodule type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epGGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213(50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154(54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodule location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodule Shape\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133(47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRound to oval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149(52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIrregular margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270(63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLobulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258(61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151(53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpiculation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243(57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191(67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCavitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348(82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular convergence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368(87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234(83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAir bronchogram\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePleural invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258(61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125(44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCTR: Consolidation tumor ratio; a: Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eClinically significant pathological and CT features identified through univariate analysis were further evaluated using multivariate logistic regression analysis. This multivariate analysis revealed several independent risk factors associated with STAS. Among the tumor differentiation subgroups, well-differentiated tumors were significantly correlated with STAS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Lymphovascular invasion (OR\u0026thinsp;=\u0026thinsp;4.677, 95% CI: 1.371\u0026ndash;15.955, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and micropapillary structure (OR\u0026thinsp;=\u0026thinsp;9.08, 95% CI: 5.172\u0026ndash;15.938, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly associated with increased risk, whereas lepidic predominant histologic subtypes (OR\u0026thinsp;=\u0026thinsp;0.069, 95% CI: 0.008\u0026ndash;0.575, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) served as protective factors against STAS (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In terms of CT features, nodule shape (OR\u0026thinsp;=\u0026thinsp;1.817, 95% CI: 1.106\u0026ndash;2.986, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), irregular margin (OR\u0026thinsp;=\u0026thinsp;2.050, 95% CI: 1.218\u0026ndash;3.449, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), lobulation (OR\u0026thinsp;=\u0026thinsp;2.235, 95% CI: 1.336\u0026ndash;3.739, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and vascular convergence (OR\u0026thinsp;=\u0026thinsp;5.032, 95% CI: 2.050\u0026ndash;12.349, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with an increased risk. For the CTR, compared with CTR\u0026thinsp;=\u0026thinsp;0% (reference), both CTR 75\u0026ndash;100% (OR\u0026thinsp;=\u0026thinsp;7.086, 95% CI: 2.542\u0026ndash;19.750, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CTR\u0026thinsp;=\u0026thinsp;100% (OR\u0026thinsp;=\u0026thinsp;11.502, 95% CI: 4.752\u0026ndash;27.840, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with an increased risk of STAS. Other radiological features did not show statistical significance (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eMultivariate analysis of pathological features related to STAS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor differentiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e─\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369\u0026ndash;6.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u0026ndash;22.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphovascular invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.371\u0026ndash;15.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMicropapillary structure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.172\u0026ndash;15.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLepidic predominant Histologic subtypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u0026ndash;0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\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 \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 analysis of CT features related to STAS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003e\u003cb\u003eNodule Shape\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.106\u0026ndash;2.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIrregular margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.218\u0026ndash;3.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLobulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.336\u0026ndash;3.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVascular convergence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.050-12.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTR%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e─\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412\u0026ndash;6.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785\u0026ndash;5.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026lt;CTR\u0026thinsp;\u0026le;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.952\u0026ndash;6.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026lt;CTR\u0026lt;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.542\u0026ndash;19.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.752\u0026ndash;27.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel development and evaluation\u003c/h2\u003e \u003cp\u003eBased on multivariate logistic regression analysis, we recognized independent risk factors derived from the features of preoperative CT scans., including nodule shape, irregular margin, lobulation sign, vascular convergence sign, and the CTR, all of which were statistically significant. Using the 'base' package in R software, we randomly divided the dataset into two groups at a ratio of 0.7:0.3, employing a fixed random seed to ensure reproducibility. The 0.7 group served as the training set, while the 0.3 group was designated as the internal validation set. Subsequently, the 'rms' package in R was utilized to construct a nomogram model based on the significant predictors identified in the training set to predict the occurrence of STAS in stage IA LUAD. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, each line on the nomogram corresponds to a variable, with the model assigning specific points to each variable. By aggregating these points, the total score can be used to determine the probability of STAS positivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe nomogram demonstrated high predictive accuracy in the training set, achieving an AUC of 0.812 (95% CI: 0.761\u0026ndash;0.863). Internal validation further corroborated its robustness, yielding an AUC of 0.781 (95% CI: 0.699\u0026ndash;0.863). These AUC values, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, suggest a strong discriminatory ability of the model in predicting the occurrence of STAS. The bootstrap method was employed for internal validation, and the calibration curve (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) demonstrates good alignment between predicted and actual probabilities, confirming the model's accuracy. Furthermore, the decision curve analysis (DCA) illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicates that the nomogram model provides a higher clinical net benefit across the risk threshold range of 0.1 to 0.8, underscoring its potential value in clinical practice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified key clinicopathological factors associated with STAS in stage IA LUAD, including tumor differentiation, micropapillary structure, lymphovascular invasion, and lepidic predominant subtype. STAS was more frequent in tumors with moderate to poor differentiation, and the micropapillary structure was significantly more prevalent in STAS-positive patients, consistent with previous studies (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Lymphovascular invasion also showed a significant correlation with STAS, highlighting its role in tumor spread (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). A meta-analysis further supported that STAS is common in micropapillary subtypes but rare in lepidic subtypes, aligning with our findings (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In terms of molecular features, some studies have reported a significant correlation between STAS positivity and wild-type EGFR (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, Toyokawa et al. (2018) found no such association with EGFR mutations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In our cohort, STAS-positive patients predominantly exhibited wild-type EGFR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is consistent with its association with poor prognosis in LUAD (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Although 71.4% of patients with ALK rearrangements were STAS-positive, the difference was not statistically significant, likely due to the limited sample size. Additionally, while Shiono et al. reported higher CEA levels in STAS-positive patients (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), our findings did not show a significant difference. Larger cohorts are needed to validate these associations.\u003c/p\u003e \u003cp\u003eWhile STAS is traditionally confirmed through postoperative pathological examination, numerous retrospective studies have explored its correlation with preoperative CT features, aiming to predict its occurrence (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Given that STAS is a microscopic phenomenon beyond the spatial resolution of current CT technology, it is suggested that indirect indicators should be utilized for its prediction (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Studies by Toyokawa et al. and Kim et al. identified significant associations between STAS and CT features such as maximum nodule diameter, solid component proportion, and CTR, highlighting their predictive potential (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In our cohort of 423 patients with stage IA LUAD, STAS was significantly associated with the maximum diameters of both the nodule and its solid component, as well as the CTR. Notably, CTR emerged as an independent risk factor for predicting STAS occurrence, with higher CTR values correlating with an increased likelihood of STAS. Consequently, CTR, which is readily measurable by CT, may facilitate the selection of the optimal surgical strategy.\u003c/p\u003e \u003cp\u003eRegarding nodule type, Kim et al. reported no STAS in pure ground-glass nodules (GGNs) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, other studies have reported the presence of STAS in pure GGNs, albeit with a relatively low incidence(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In our study, 5% (7/141) of the STAS-positive patients exhibited pure GGNs, and 14.1% (20/141) had nodules where the ground-glass component predominated. These findings suggest that the choice between sublobar resection and lobectomy should not be based solely on the presence of pure GGNs but should also incorporate a comprehensive evaluation of imaging features. Consistent with Shiono et al., we found that pure solid nodules had a significantly higher STAS positivity rate compared to subsolid nodules (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, several CT features, such as irregular margin, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion, were frequently observed in STAS-positive patients (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Similar to these studies, our research confirms that irregular nodule shape, irregular margin, lobulation, spiculation, vascular convergence, air bronchogram, and pleural invasion are commonly seen in CT images of stage IA LUAD patients with STAS. Among these, irregular nodule shape, irregular margin, lobulation sign, and vascular convergence sign were identified as independent risk factors for STAS occurrence in stage IA LUAD. These findings highlight the utility of preoperative chest CT imaging in predicting STAS and optimizing surgical planning for stage IA LUAD patients.\u003c/p\u003e \u003cp\u003eThe clinical significance of our findings is noteworthy; however, several limitations should be acknowledged. First, as a retrospective cohort study focusing exclusively on surgically treated stage IA LUAD patients, there is an inherent risk of selection bias, which may have influenced the representativeness of the sample population. Second, the relatively short postoperative follow-up period limited the evaluation of long-term outcomes, such as disease recurrence and overall prognosis. Future studies with longer follow-up durations are necessary to further elucidate the prognostic impact of STAS. Finally, the single-center design restricts the generalizability of our results. Multicenter studies involving diverse patient populations are needed to validate and strengthen the reliability of these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study analyzed the clinicopathologic and CT features associated with STAS in patients with stage IA LUAD. We identified lower tumor differentiation, non-lepidic predominant subtypes, micropapillary structure, and lymphovascular invasion as significant clinicopathologic risk factors for STAS. Additionally, CT features such as solid components, irregular shape, irregular margin, lobulation, and vascular convergence were significant predictors of STAS. On the basis of these five CT features, we developed a nomogram model with high predictive accuracy and clinical applicability. This model provides a valuable tool for preoperative STAS diagnosis and can assist thoracic surgeons in optimizing surgical strategies for stage IA LUAD patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eSTAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eSpread through Air Spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003ePropensity Score Matching\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eNon-small Cell Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eDisease-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eIASLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eInternational Association for the Study of Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003ePicture Archiving and Communication System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eBody Mass Index\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eConsolidation Tumor Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eCarcinoembryonic Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eGGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 501px;\"\u003e\n \u003cp\u003eGround-Glass Nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all procedures involving human participants received approval from the ethics committee at Sun Yat-sen University Cancer Center. The Institutional Review Board waived the requirement for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available, as they are part of an ongoing longitudinal study that requires controlled access to maintain research integrity. However, they are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that this work has not received any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBL and HY contributed to the conception and design of the study. NF collected and organized the clinical data. PD performed the statistical analysis. ZW supervised the study and revised the manuscript. PL provided overall guidance and finalized the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the Sun Yat-sen University Cancer Center (SYSUCC) for providing essential support, including data, facilities, and technical expertise, which were crucial to the success of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Vaccarella S, Morgan E, Li M, Etxeberria J, Chokunonga E, et al. Global variations in lung cancer incidence by histological subtype in 2020: a population-based study. 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Radiology. 2018;289(3):831\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEguchi T, Kameda K, Lu S, Bott MJ, Tan KS, Montecalvo J, et al. Lobectomy Is Associated with Better Outcomes than Sublobar Resection in Spread through Air Spaces (STAS)-Positive T1 Lung Adenocarcinoma: A Propensity Score-Matched Analysis. J Thorac Oncol. 2019;14(1):87\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKadota K, Kushida Y, Kagawa S, Ishikawa R, Ibuki E, Inoue K, et al. Limited Resection Is Associated With a Higher Risk of Locoregional Recurrence than Lobectomy in Stage I Lung Adenocarcinoma With Tumor Spread Through Air Spaces. Am J Surg Pathol. 2019;43(8):1033\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Y, Xie H, Dai C, She Y, Su H, Xie D, et al. Prognostic Impact of Tumor Spread Through Air Spaces in Sublobar Resection for 1A Lung Adenocarcinoma Patients. Ann Surg Oncol. 2019;26(6):1901\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiono S, Endo M, Suzuki K, Yarimizu K, Hayasaka K, Yanagawa N. Spread Through Air Spaces Is a Prognostic Factor in Sublobar Resection of Non-Small Cell Lung Cancer. Ann Thorac Surg. 2018;106(2):354\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiono S, Endo M, Suzuki K, Hayasaka K, Yanagawa N. Spread through air spaces in lung cancer patients is a risk factor for pulmonary metastasis after surgery. J Thorac Dis. 2019;11(1):177\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBains S, Eguchi T, Warth A, Yeh YC, Nitadori JI, Woo KM, et al. Procedure-Specific Risk Prediction for Recurrence in Patients Undergoing Lobectomy or Sublobar Resection for Small (\u0026le;\u0026thinsp;2 cm) Lung Adenocarcinoma: An International Cohort Analysis. J Thorac Oncol. 2019;14(1):72\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasai K, Sakurai H, Sukeda A, Suzuki S, Asakura K, Nakagawa K, et al. Prognostic Impact of Margin Distance and Tumor Spread Through Air Spaces in Limited Resection for Primary Lung Cancer. J Thorac Oncol. 2017;12(12):1788\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, Wang X, Zhang F, Han R, Ding Q, Xu X, et al. Could tumor spread through air spaces benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-institutional study. Ther Adv Med Oncol. 2020;12:1758835920978147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMino-Kenudson M. Significance of tumor spread through air spaces (STAS) in lung cancer from the pathologist perspective. 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Am J Surg Pathol. 2003;27(1):101\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakimoto Y, Nabeshima K, Iwasaki H, Miyoshi T, Enatsu S, Shiraishi T, et al. Micropapillary pattern: a distinct pathological marker to subclassify tumours with a significantly poor prognosis within small peripheral lung adenocarcinoma (=20 mm) with mixed bronchioloalveolar and invasive subtypes (Noguchi\u0026rsquo;s type C tumours)\u0026lt;/at. Histopathology. 2005;46(6):677\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNitadori Jichi, Bograd AJ, Kadota K, Sima CS, Rizk NP, Morales EA, et al. Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2cm or smaller. J Natl Cancer Inst. 2013;105(16):1212\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi E, Bae MK, Cho S, Chung JH, Jheon S, Kim K. Pathological prognostic factors of recurrence in early stage lung adenocarcinoma. ANZ J Surg. 2018;88(4):327\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShih AR, Mino-Kenudson M. Updates on spread through air spaces (STAS) in lung cancer. Histopathology. 2020;77(2):173\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePyo JS, Kim NY. Clinicopathological Impact of the Spread through Air Space in Non-Small Cell Lung Cancer: A Meta-Analysis. Diagnostics (Basel). 2022;12(5):1112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon HY, Ryu JS, Sim YS, Kim D, Lee SY, Choi J, et al. Clinical significance of EGFR mutation types in lung adenocarcinoma: A multi-centre Korean study. PLoS ONE. 2020;15(2):e0228925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiono S, Yanagawa N. Spread through air spaces is a predictive factor of recurrence and a prognostic factor in stage I lung adenocarcinoma. Interact Cardiovasc Thorac Surg. 2016;23(4):567\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Liu Z, Feng H, Xiao F, Shao W, Liang C, et al. Predictive value of radiological features on spread through air space in stage cIA lung adenocarcinoma. J Thorac Dis. 2020;12(11):6494\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToyokawa G, Yamada Y, Tagawa T, Kamitani T, Yamasaki Y, Shimokawa M, et al. Computed tomography features of resected lung adenocarcinomas with spread through air spaces. J Thorac Cardiovasc Surg. 2018;156(4):1670\u0026ndash;e16764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing Y, Chen Y, Wen H, Li J, Chen J, Xu M, et al. Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg. 2022;62(3):ezac248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Jiang C, Gong J, Wu X, Luo Y, Sun G. A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma. Quant Imaging Med Surg. 2020;10(10):1984\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Margerie-Mellon C, Onken A, Heidinger BH, VanderLaan PA, Bankier AA. CT Manifestations of Tumor Spread Through Airspaces in Pulmonary Adenocarcinomas Presenting as Subsolid Nodules. J Thorac Imaging. 2018;33(6):402\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong Y, Xu Y, Deng J, Wang T, Sun X, Chen D, et al. Prognostic impact of tumour spread through air space in radiological subsolid and pure solid lung adenocarcinoma. Eur J Cardiothorac Surg. 2021;59(3):624\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi L, Xue K, Cai Y, Lu J, Li X, Li M. Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma. Front Oncol. 2020;10:548430.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Zheng B, Zhao T, Fan Y. Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis. J Thorac Imaging. 2023;38(2):W19\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, Spread through air spaces, Clinicopathologic features, CT features, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6324646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6324646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis research aimed to examine the relationships between clinicopathological characteristics and the occurrence of Spread Through Air Spaces (STAS) in patients with stage IA lung adenocarcinoma (LUAD) and to develop a preoperative prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 1,375 patients with stage IA LUAD at Sun Yat-sen University Cancer Center were analyzed. Propensity score matching (PSM) was employed to match 141 STAS-positive patients with 282 STAS-negative patients. Both univariate and multivariate logistic regression analyses were performed to determine independent variables among 16 clinicopathological and 13 CT imaging characteristics. A nomogram prediction model was developed and evaluated via receiver operating characteristic (ROC) and decision curve analyses (DCAs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate analysis identified several independent risk factors. Irregular nodule shape (OR\u0026thinsp;=\u0026thinsp;1.817, 95% CI: 1.106\u0026ndash;2.986, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), irregular margin (OR\u0026thinsp;=\u0026thinsp;2.050, 95% CI: 1.218\u0026ndash;3.449, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), lobulation (OR\u0026thinsp;=\u0026thinsp;2.235, 95% CI: 1.336\u0026ndash;3.739, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and vascular convergence (OR\u0026thinsp;=\u0026thinsp;5.032, 95% CI: 2.050\u0026ndash;12.349, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with an increased risk of STAS. Compared with a consolidation tumor ratio (CTR)\u0026thinsp;=\u0026thinsp;0% (reference), a CTR of 75\u0026ndash;100% (OR\u0026thinsp;=\u0026thinsp;7.086, 95% CI: 2.542\u0026ndash;19.750, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a CTR\u0026thinsp;=\u0026thinsp;100% (OR\u0026thinsp;=\u0026thinsp;11.502, 95% CI: 4.752\u0026ndash;27.840, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with an increased risk of STAS. The nomogram was developed and internally validated, demonstrating good predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.812, 95% CI: 0.761\u0026ndash;0.863) and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram reliably predicts STAS preoperatively and may assist in guiding surgical decision-making.\u003c/p\u003e","manuscriptTitle":"CT Feature-Based Nomogram for Predicting Tumor Spread Through Air Spaces in Stage IA Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 09:19:14","doi":"10.21203/rs.3.rs-6324646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-08T07:56:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T22:08:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T09:48:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222625052136743508655706631893500606686","date":"2025-04-12T08:59:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96363077035137778602818390114616981822","date":"2025-04-11T15:25:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-10T08:51:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T05:57:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T04:42:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2025-03-28T03:52:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3207a1a-5402-4991-bcdf-83dea7378896","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:04:58+00:00","versionOfRecord":{"articleIdentity":"rs-6324646","link":"https://doi.org/10.1186/s40644-025-00893-x","journal":{"identity":"cancer-imaging","isVorOnly":false,"title":"Cancer Imaging"},"publishedOn":"2025-06-11 15:57:08","publishedOnDateReadable":"June 11th, 2025"},"versionCreatedAt":"2025-04-16 09:19:14","video":"","vorDoi":"10.1186/s40644-025-00893-x","vorDoiUrl":"https://doi.org/10.1186/s40644-025-00893-x","workflowStages":[]},"version":"v1","identity":"rs-6324646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6324646","identity":"rs-6324646","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}