Histology-Specific Radiotherapy Target Volume Delineation for NSCLC Based on Distinct Lymph Node Metastasis Patterns of Adenocarcinoma and Squamous Cell Carcinoma

Histology-Specific Radiotherapy Target Volume Delineation for NSCLC Based on Distinct Lymph Node Metastasis Patterns of Adenocarcinoma and Squamous Cell Carcinoma | 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 Histology-Specific Radiotherapy Target Volume Delineation for NSCLC Based on Distinct Lymph Node Metastasis Patterns of Adenocarcinoma and Squamous Cell Carcinoma Zheng Liu, Xiaomei Qian, Zhihui Li, Zhiming Chen, Guangjie Wang, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8318884/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background This study sought to characterize distinct regional lymph node metastasis (LNM) patterns of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) using PET-CT imaging and postoperative pathology, and to provide evidence-based guidance for precise radiotherapy target volume delineation in NSCLC. Methods We retrospectively analyzed 422 PET-CT scans (281 LUAD, 141 LUSC) and 305 surgical pathology reports (236 LUAD, 69 LUSC) from our institution. Inter-group comparisons were performed using chi-square or Fisher’s exact tests. Binary logistic regression models were utilized for multivariate analyses. Results PET-CT and pathological data exhibited high concordance in LNM distribution. Compared with LUSC, LUAD displayed more aggressive LNM behavior, with significantly higher metastasis rates to supraclavicular, contralateral mediastinal, and contralateral hilar nodes. In contrast, LUSC showed increased level 8 LNM, especially in left inferior lobe origin. Multivariate analysis revealed: LUAD with mediastinal invasion, ipsilateral multi-lobar nodules, or LNM at levels 2L/2R/3a/6 had higher 1L/1R metastasis risk; left inferior lobe origin or larger tumor diameter in LUSC hinted level 8 metastasis. Significant inter-nodal metastatic correlations were identified across different levels. Conclusion LUAD and LUSC exhibit distinct histology- and subsite-specific LNM patterns and inter-nodal metastatic correlations. Two optimized CTVn delineation recommendations for definitive concurrent chemoradiotherapy of LUAD and LUSC were proposed to enhance targeting precision. Lung Adenocarcinoma (LUAD) Lung Squamous Cell Carcinoma (LUSC) Lymph Node Metastasis Pattern PET/CT Pathology Radiotherapy Target Volume Figures Figure 1 1. Background Lung cancer remains a leading global public health burden, accounting for over 1.8 million deaths worldwide in 2022, representing 18.7% of all cancer-related mortality[1]. In China, the incidence of lung cancer constitutes 22.0% of all malignancies, with a mortality rate as high as 28.5%—significantly exceeding the global average[2]. Lung adenocarcinoma (LUAD, of bronchial glandular origin) and lung squamous cell carcinoma (LUSC, of bronchial epithelial origin) are the two predominant subtypes of non-small-cell lung cancer (NSCLC), accounting for approximately 50% and 20–30% of cases, respectively[3, 4]. As is well-known, these two histological subtypes exhibit distinct pathological origins, clinicopathological features, and tumor biological behaviors[5]. Despite the advent of novel anticancer agents[6], concurrent or sequential chemoradiotherapy remains an irreplaceable treatment modality for locally advanced unresectable NSCLC[7,8]. The 2018 ESTRO ACROP consensus emphasized the indispensable role of radiotherapy and provided recommendations for target volume delineation[9]. Defining the clinical target volume (CTV) for lymph nodes requires a thorough understanding of LNM patterns; however, significant controversy persists regarding the biological LNM characteristics specific to LUAD and LUSC[10,11]. Involved-field irradiation (IFI) is currently recommended for locally advanced inoperable NSCLC instead of elective nodal irradiation (ENI); nonetheless, discrepancies remain regarding optimal CTV delineation for lymph nodes. Previous studies and major NSCLC CTV delineation guidelines recommend an 8 mm expansion margin for LUAD and 6 mm for LUSC to cover 95% of microscopic extension[12]. This recommendation acknowledges the difference in gross tumor microscopic extension between the two subtypes but fails to address disparities in regional nodal metastasis patterns or provide corresponding CTV margin recommendations. Furthermore, the core evidence supporting involved-field radiation therapy (IFRT) stems from retrospective studies reporting elective nodal failure (ENF) rates below 10%—defined as recurrence in initially uninvolved lymph nodes without local failure[13–15]. Critically, these studies pooled all NSCLC subtypes without stratifying ENF rates by histology (LUAD vs. LUSC) or providing subtype-specific analyses. Consequently, the risk of out-field failure in LUAD versus LUSC patients treated with IFRT remains unclear. More importantly, no prospective randomized head-to-head trials have compared the efficacy and safety of ENI versus IFRT in locally advanced unresectable NSCLC, particularly stratified by histological subtype. Positron emission tomography-computed tomography (PET-CT) offers superior diagnostic accuracy and is strongly recommended by the ESTRO ACROP and IAEA guidelines as a core technique for target volume delineation in locally advanced NSCLC radiotherapy[16,17]. It is widely recognized as the most precise imaging modality for investigating regional LNM patterns in NSCLC, especially in unresectable locally advanced disease. Our team previously explored regional LNM patterns in locally advanced NSCLC using PET-CT imaging[18]; however, PET-CT inherently has false-positive and false-negative limitations, particularly in cases complicated by tuberculous infections, granulomatous benign lesions, or chronic inflammatory conditions[19]. Postoperative pathological data can serve as a confirmatory tool to improve diagnostic accuracy but is limited by the fact that patients undergoing radical surgery typically present at earlier stages[20]. Additionally, surgical pathology lacks comprehensive regional LNM information (e.g., contralateral mediastinal or supraclavicular nodes are rarely dissected intraoperatively), failing to fully reflect inherent LNM patterns—especially in locally advanced unresectable NSCLC. Therefore, a comprehensive systematic analysis integrating high-precision imaging and pathological data is essential to elucidate the inherent regional LNM patterns in NSCLC. In this study, we further investigated differences in regional LNM patterns between LUAD and LUSC by integrating PET-CT imaging data and postoperative pathological findings. Our objectives were to: (1) identify consistent NSCLC LNM patterns validated by both modalities; (2) systematically characterize subtype-specific differences and inter-nodal metastatic correlations; and (3) provide evidence-based guidance for precise radiotherapy target delineation. 2. Methods 2.1 Patient Eligibility We retrospectively enrolled 422 pathologically confirmed NSCLC patients (281 LUAD, 141 LUSC) who underwent PET-CT examinations at the General Hospital of Western Theater Command of PLA between January 2018 and August 2024. Postoperative pathology data were collected from 305 additional NSCLC patients (236 LUAD, 69 LUSC) who underwent radical surgical resection at the same institution between January 2017 and August 2024. Exclusion criteria included: (1) N0 stage disease; (2) prior antitumor therapy; (3) incomplete clinical data; (4) mixed NSCLC subtypes; and (5) secondary lung malignancies. All cases were staged according to the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) 8th edition TNM staging system. This study was approved by the Ethics Committee of the General Hospital of Western Theater Command of PLA (Approval No. : EC5-ky004), with a waiver of informed consent due to its retrospective nature. 2.2 Imaging Examination and LNM Confirmation Lymph node mapping followed the 2009 International Association for the Study of Lung Cancer (IASLC) nodal classification. The PET-CT examination protocol and image interpretation criteria were consistent with our previously published methodology[18]. A lymph node was defined as metastatic on PET-CT if it met all the following criteria: (1) short-axis diameter ≥ 1.0 cm; (2) maximum standardized uptake value (SUVmax) ≥ 2.5; and (3) absence of plaque-like calcification. Lymph nodes enlarged on CT without significant radiotracer uptake were not considered metastatic. For postoperative pathological specimens, the Ki-67 proliferation index was assessed by immunohistochemistry to quantify tumor cell nuclear positivity. Pathologists microscopically evaluated and documented vascular invasion, neural invasion, pleural involvement, and spread through air spaces (STAS). The maximum diameter of the primary tumor was measured intraoperatively and confirmed pathologically, then categorized according to T-stage criteria. 2.3 Statistical Analysis Data were organized using Microsoft Excel and analyzed with IBM SPSS Statistics® 27 software. Categorical variables were presented as frequencies (percentages), with inter-group comparisons performed using the chi-square test or Fisher’s exact test (when expected frequencies < 5). Continuous variables conforming to a normal distribution (e.g., age, maximum tumor diameter) were expressed as mean ± standard deviation; non-normal variables were presented as median (interquartile range). Inter-group comparisons of continuous variables were performed using the independent samples t-test or Mann-Whitney U test, as appropriate. Binary logistic regression models were employed for multivariate analysis to explore correlations between metastatic lymph node levels. The Hosmer-Lemeshow test was used to assess logistic regression model goodness-of-fit, with a p-value > 0.05 indicating satisfactory model calibration. All tests were two-sided, with a p-value < 0.05 considered statistically significant. 3. Results 3.1 Baseline Patient Characteristics The PET-CT cohort included 422 NSCLC patients (281 LUAD [66.6%], 141 LUSC [33.4%]). Baseline characteristics are detailed in Table 1 . No significant differences were observed in age distribution or primary tumor location between subtypes. LUSC was associated with larger maximum tumor diameter (5.48 ± 2.53 cm vs. 3.87 ± 2.03 cm, p < 0.01) and lower distant metastasis rate (33.3% vs. 64.4%, p < 0.01) compared with LUAD. LUAD exhibited higher rates of lymph vascular invasion (62.3% vs. 47.8%, p = 0.032) and spread through air spaces (STAS; 21.6% vs. 5.8%, p = 0.003). The pathological cohort included 305 NSCLC patients (236 LUAD [77.4%], 69 LUSC [22.6%]). Baseline characteristics are presented in Table 2 . No significant differences were noted in median age or overall lobar distribution. Statistically significant differences are highlighted in bold. Table 1. Clinical Baseline Characteristics of LUAD and LUSC Based on PET-CT Characteristic LUAD, n=281(66.6%) LUSC, n=141(33.4%) p Value Median age[year, M(P 25, P 75 )] 64(56,71) 65(58,71) 0.408 Gender <0.001 Male 154(54.8%) 125(88.7%) Female 127(45.2%) 16(11.3%) Location of primary tumor 0.275 Left Lung 120(42.7%) 70(49.6%) Superior lobe 78(27.8%) 38(27.0%) Inferior lobe 42(14.9%) 32(22.6%) Right lung 161(57.3%) 71(50.4%) Superior lobe 79(28.1%) 31(22.0%) Middle lobe 19(6.8%) 7(5.0%) Inferior lobe 63(22.4%) 33(23.4%) Tumor type <0.001 Peripheral lung cancer 238(84.7%) 53(37.6%) Central lung cancer 43(15.3%) 88(62.4%) Maximum diameter [cm,x±s] 3.87±2.03 5.48 ± 2.53 <0.001 Local invasion 166(59.1%) 91(64.5%) 0.278 Visceral pleura 136(48.4%) 64(45.4%) 0.559 Mediastinum 29(10.3%) 21(14.9) 0.170 Separate nodules in same lobe 51(18.1%) 12(8.5%) 0.009 Separate nodules in different ipsilateral lobe 49(17.3%) 8(5.7%) <0.001 Others 6(2.1%) 13(9.2%) <0.001 N classification 0.208 N1 23(8.2%) 18(12.8%) N2 100(35.6%) 54(38.3%) N3 158(56.2%) 69(48.9%) Characteristic LUAD, n=281(66.6%) LUSC, n=141(33.4%) p Value T classification 0.196 T1a 1(0.4%) 0(0) T1b 34(12.1%) 5(3.5%) T1c 36(12.8%) 11(7.8%) T2a 58(20.6%) 25(20.6%) T2b 26(9.3%) 16(11.3%) T3 49(17.4%) 32(22.7%) T4 77(27.4%) 52(36.9%) M classification <0.001 M0 100(35.6%) 96(68.1%) M1 181(64.4%) 45(31.9%) M1a 32(11.4%) 10(7.1%) M1b 72(25.6%) 16(11.3%) M1c 77(27.4%) 19(13.5%) TNM Stage 0.101 ⅡB 11(3.9%) 18(4.3%) ⅢA 33(11.7%) 59(14.0%) ⅢB 36(12.8%) 70(16.6%) ⅢC 20(7.1%) 49(11.6%) ⅣA 104(37%) 129(30.6%) ⅣB 77(27.4%) 97(23.0%) Distant metastasis 181(64.4%) 47(33.3%) <0.001 Bone 128(45.6%) 24(17.0%) <0.001 Pleural dissemination 49(17.4%) 15(10.6%) 0.066 Liver 24(8.5%) 9(6.4%) 0.436 Contralateral lung 37(13.2%) 7(5.0%) 0.009 Brain 20(7.1%) 2(1.4) 0.013 Adrenal gland 28(9.7%) 7(5.0%) 0.079 Table 2. Clinical Baseline Characteristics of LUAD and LUSC Based on Pathological Data Characteristic LUAD n=236 (77.4%) LUSC n=69(22.6%) p Value Median age [year, M (P25, P75)] 56.5(25, 75) 58(IQR, 53-63) 0.232 Gender <0.001 Male 119(50.4%) 62(89.9%) Female 117(49.6%) 7(10.1%) Location of primary tumor 0.328 Left Lung 86(36.4%) 29(42.0%) 0.399 Superior lobe 43(18.2%) 14(20.3%) Inferior lobe 43(18.2%) 15(21.7%) Right lung 150(63.6%) 40(58.0%) 0.399 Superior lobe 70(29.7%) 13(18.8%) Middle lobe 23(9.7%) 11(15.9%) Inferior lobe 57(24.2%) 16(23.2%) Maximum diameter [cm, M (P25, P75)] 2.50 (2.0, 3.5) 3.20 (2.5, 3.0) <0.001 Pathological characteristics Visceral pleura 158(66.9%) 32(46.4%) 0.002 Neural invasion 55(23.3%) 10(14.5%) 0.116 Vascular invasion 147(62.3%) 33(47.8%) 0.032 Spread through air spaces 61(21.6%) 4(5.8%) 0.003 Bronchial stump 1(0.4%) 2(2.9%) 0.067 Ki-67 score <0.001 Median score [M (P25, P75)] 20%(10%, 30%) 40%(30%, 60%) Range 2%-60% 3%-70% N classification 0.019 N1 59(25.0%) 29(42.0%) N2 175(74.2%) 40(58.0%) N3 2(2.8%) 0 3.2 Concordance Between PET-CT and Pathological LNM Patterns Overall, PET-CT and postoperative pathological data exhibited high concordance in LNM distribution patterns for both LUAD and LUSC, confirming the reliability of PET-CT-derived LNM patterns. For LUAD across pulmonary lobes, the top five metastatic nodal levels identified by both modalities showed substantial overlap: left superior lobe (10L, 5, 4L, 6); left inferior lobe (10L, 7, 4L, 5); right upper lobe (10R, 4R, 2R, 7); right middle lobe (10R, 7, 4R, 2R); and right inferior lobe (7, 10R, 4R, 2R). For LUSC across pulmonary lobes, concordant top metastatic levels included: left superior lobe (10L, 4L, 5, 7); left inferior lobe (10L, 7, 4L, 5); right upper lobe (10R, 4R, 2R, 7); right middle lobe (10R, 4R, 2R); and right inferior lobe (10R, 7, 4R, 2R) (Table 3 ). Table 3 Comparative Analysis of Lobar LNM Distribution in LUAD vs. LUSC by Pulmonary Lobe Based on PET-CT and Postoperative Pathology Data Characteristic LUAD n = 236 (77.4%) LUSC n = 69(22.6%) p Value Median age [year, M (P25, P75)] 56.5(25, 75) 58(IQR, 53–63) 0.232 Gender < 0.001 Male 119(50.4%) 62(89.9%) Female 117(49.6%) 7(10.1%) Location of primary tumor 0.328 Left Lung 86(36.4%) 29(42.0%) 0.399 Superior lobe 43(18.2%) 14(20.3%) Inferior lobe 43(18.2%) 15(21.7%) Right lung 150(63.6%) 40(58.0%) 0.399 Superior lobe 70(29.7%) 13(18.8%) Middle lobe 23(9.7%) 11(15.9%) Inferior lobe 57(24.2%) 16(23.2%) Maximum diameter [cm, M (P25, P75)] 2.50 (2.0, 3.5) 3.20 (2.5, 3.0) < 0.001 Pathological characteristics Visceral pleura 158(66.9%) 32(46.4%) 0.002 Neural invasion 55(23.3%) 10(14.5%) 0.116 Vascular invasion 147(62.3%) 33(47.8%) 0.032 Spread through air spaces 61(21.6%) 4(5.8%) 0.003 Bronchial stump 1(0.4%) 2(2.9%) 0.067 Ki-67 score < 0.001 Median score [M (P25, P75)] 20%(10%, 30%) 40%(30%, 60%) Range 2%-60% 3%-70% N classification 0.019 N1 59(25.0%) 29(42.0%) N2 175(74.2%) 40(58.0%) N3 2(2.8%) 0 3.3 Subtype-Specific LNM Distribution Patterns Pathological analysis of resectable NSCLC revealed distinct LNM distribution patterns between LUAD and LUSC (Figure S1, Table S1). Specifically, LUAD demonstrated a significantly higher metastasis rate at level 4R (29.2% vs. 13.0%, p = 0.007) and a trend toward increased level 2R involvement (25.0% vs. 14.5%, p = 0.067) compared with LUSC—particularly in the right lung (Tables S3, S4). Univariate and multivariate analyses identified inter-nodal metastatic correlations in resected LUAD: 2R metastasis correlated with ipsilateral 4R; 4L correlated with 6; and level 7 correlated with level 9 (Table S5). Limitations of surgical pathology included infrequent dissection of supraclavicular lymph nodes (SCLN) and inability to assess nodes outside the surgical field—gaps effectively addressed by PET-CT. PET-CT analysis further confirmed subtype-specific LNM distributions (Figure S2, Table S2). As shown in Table 4 , LUAD had higher SCLN metastasis rates (1L: 21.0% vs. 12.1%, p = 0.024; 1R: 31.7% vs. 19.9%, p = 0.011), while LUSC exhibited higher level 8 metastasis (29.8% vs. 19.9%, p = 0.024). Multivariate analysis revealed that LUAD conferred 1.87-fold (1R, 95%CI: 1.15–3.04) and 1.94-fold (1L, 95%CI: 1.08–3.47) increased SCLN metastasis risks compared with LUSC. Table 4 Comparison of Overall LNM Rates Between LUAD and LUSC based on PET-CT data. LNM Levels The number and rate of LNM P value LUAD (n = 281) LUSC (n = 141) 1L 59(21.0%) 17(12.1%) 0.024 1R 89(31.7%) 28(19.9%) 0.011 2L 36(12.8%) 12(8.5%) 0.189 2R 109(38.8%) 49(34.8%) 0.419 3a 42(14.9%) 21(14.9%) 0.989 3p 11(3.9%) 2(1.4%) 0.162 4L 116(41.3%) 60(42.6%) 0.803 4R 175(62.3%) 83(58.9%) 0.498 5 96(34.2%) 46(32.6%) 0.752 6 64(22.8%) 31(22.0%) 0.855 7 155(55.2%) 87(61.7%) 0.200 8 56(19.9%) 42(29.8%) 0.024 9 32(11.4%) 22(15.6%) 0.221 10L 112(39.9%) 64(45.4%) 0.277 10R 168(59.8%) 82(58.2%) 0.748 L-IPLN 43(15.3%) 26(18.4%) 0.411 R-IPLN 65(23.1%) 30(21.3%) 0.667 Side-stratified analysis showed that left lung LUSC had higher level 8 metastasis (38.6% vs. 18.3%, p = 0.002), while left lung LUAD had greater contralateral 1R involvement (24.2% vs. 10.0%, p = 0.016). For right lung tumors, LUAD exhibited increased contralateral 1L (17.4% vs. 7.0%, p = 0.038) and contralateral hilar 10L metastasis (10.6% vs. 2.8%, p = 0.048) (Table S6). Lobar-specific analysis demonstrated that left inferior lobe LUSC had higher level 8 metastasis (50.0% vs. 26.2%, p = 0.035), while right inferior lobe LUAD showed increased contralateral 1L (20.0% vs. 3.0%, p = 0.020) and 10L metastasis (11.0% vs. 0%, p = 0.047) (Tables S7, S8). Employing the same method as resected LUAD data, we also explored potential relationships among metastatic lymph node levels. Specifically, for the LUAD patients, significant correlations were confirmed between level 1L and 1R, 3a, 4L and 6, as well as correlation between 1R and 1L, 2L, 2R and 10R. Similarly, 2L was linked with 1R, 4L and 6, and 2R exhibited pronounced connections with 1R and 4R (both p < 0.001). Level 3a correlated with 1L and 6 (See Table S9). 3.4 High-Risk Factors for Site-Specific Metastasis Given the identified propensity for SCLN metastasis in LUAD, we sought to characterize the high-risk patient to guide radiotherapy planning. LUAD patients with SCLN metastasis were compared with those without to identify high-risk features (Table 5 ). Multivariate analysis revealed that LUAD with mediastinal invasion (T3), ipsilateral multi-lobar separate nodules (T4), or LNM at levels 2L, 2R, 3a, or 6 had significantly higher SCLN metastasis risk (all p < 0.05). Comparison of LUSC patients with and without level 8 metastasis identified key risk factors (Table 6 ): left inferior lobe origin (38.1% vs. 16.2%, p < 0.05) and larger maximum tumor diameter (6.305 ± 2.744 cm vs. 5.129 ± 2.36 cm, p = 0.011) were independently associated with level 8 metastasis. Fisher’s exact test was used for location analysis due to expected frequencies < 5 in 20% of cells. Table 5 Comparasion of LUAD patients with/without supraclavicular lymph nodes metastasis Characteristic SCLNM (+) n = 110 SCLNM (-) n = 170 p Value Local invasion 0.013 Visceral pleura 56(50.9%) 80(47.1%) Mediastinum 15(13.6%) 14(8.2%) < 0.05 Separate nodules in same lobe (T3) 32(29.1%) 19(11.2%) Separate nodules in different ipsilateral lobe (T4) 30(27.3%) 19(11.2%) < 0.05 Others 1(0.91%) 5(2.9%) / Lymph node metastasis < 0.01 2L 29(26.4%) 7(4.1%) < 0.05 2R 76(69.1%) 33(19.4%) < 0.05 3a 36(32.7%) 6(3.5%) < 0.05 3p 9(8.2%) 2(1.2%) 4L 64(58.2%) 52(30.6%) 4R 91(82.7%) 84(49.4%) 5 47(42.7%) 49(28.8%) 6 48(43.6%) 16(9.4%) < 0.05 7 79(71.8%) 75(44.1%) 8 33(30%) 23(13.5%) 9 17(15.5%) 15(8.8%) 10L 44(40%) 68(40%) 10R 82(74.5%) 85(50%) L-IPLN 18(16.4%) 25(14.7%) R-IPLN 39(35.5%) 25(14.7%) Table 6 Comparison of LUSC patients with/without level 8 metastasis Characteristic Level 8 (+) n = 42 Level 8 (-) n = 99 p Value Location of primary tumor 0.009* Left Lung 27 (64.3%) 43 (43.4%) Superior lobe 11 (26.2%) 27 (27.3%) Inferior lobe 16 (38.1%) 16 (16.2%) < 0.05 Right lung 15 (35.7%) 56 (56.6%) Superior lobe 4 (9.5%) 27 (27.3%) < 0.05 Middle lobe 0 7 (7.1%) Inferior lobe 11 (26.2%) 22 (22.2%) Maximum diameter [cm, x ± s] 6.305 ± 2.744 5.129 ± 2.36 0.011 *: As the theoretical frequency was less than 5 in 20% of the data, the Fisher's exact test was employed for this analysis. 4. Discussion Building on our prior work in Radiation Oncology demonstrating significant LNM correlations in inoperable NSCLC[18], this study integrated PET-CT data from 422 treatment-naive patients and pathological data from 305 surgical cases to investigate lobar-specific LNM patterns and inter-nodal correlations between LUAD and LUSC. Our findings confirm that LUAD exhibits a more aggressive metastatic phenotype and underscore fundamental differences in LNM patterns between subtypes—supporting histology-adapted CTV delineation to enhance radiotherapy precision. Lobar-specific analyses revealed distinct metastatic signatures: LUAD primarily involved levels 5/10L/4L/6/7 in the left lung and 10R/2R/4R/7 in the right lung, with the highest metastasis rates in the right middle/lower lobes (particularly the right inferior lobe). In contrast, LUSC predominantly metastasized to levels 10L/7/4L in the left lung and 7/10R/4R/2R in the right lung. CTV delineation in NSCLC radiotherapy remains heavily experience-dependent due to the paucity of prospective evidence. To some extent, our findings address this critical gap by providing subtype-specific data to be expected to optimize definitive radiotherapy planning (though prospective validation is still needed). The ESTRO ACROP guidelines recommend IFRT for locally advanced inoperable NSCLC and offer two CTV delineation options: Option 1 (inclusion of entire involved nodal stations with ≥ 5–8 mm margin around GTV) and Option 2 (geometric GTV-to-CTV expansion [5–8 mm] with smaller volume) [13–15]. Consistent with prior evidence of low out-field recurrence with IFRT, we advocate for IFRT over ENI but propose subtype stratification: LUAD may benefit from Option 1 (larger volume) while LUSC is better suited for Option 2 (smaller volume). The ESTRO ACROP guidelines also mention elective inclusion of hilar and/or neighboring nodal stations but do not define "neighboring" or specify inclusion criteria[21]. Our data partially clarify these ambiguities by identifying some subtype-specific high-risk stations such as SCLN for LUAD and level 8 for large-volume LUSC. Integrating these findings with guideline recommendations, we propose optimized CTVn delineation strategies for LUAD and LUSC (Table 7 ). Of course, future studies should specifically evaluate out-field failure rates by subtype and conduct prospective trials of subtype-adapted CTV delineation. This study has several limitations. First, its retrospective design means our CTVn recommendations require prospective validation. Second, factors including tumor differentiation grade, central/peripheral location, and EGFR mutation status—potential modifiers of LNM patterns—were not analyzed (e.g., poorly differentiated LUSC may mimic LUAD behavior). Third, single-center enrollment introduces potential selection bias despite large sample sizes. Fourth, PET-CT false positives/negatives and unevaluated molecular features (EGFR/PD-L1) may limit interpretation. Future multicenter prospective studies should integrate clinical, imaging, and pathological data to refine LNM prediction models, explore molecular/subtype modifiers, and leverage radiomics/artificial intelligence to enhance detection accuracy. Table 7 Suggestion for CTV delineation. LUAD LUSC ESTRO ACROP guideline’s Option 1 (lymph node stations): (1) inclusion of the whole pathologically affected lymph node station (Fig. 1 a) including at least a 8 mm margin around the GTV. (2) Inclusion of the hilum and uninvolved areas between involved stations. (3) Inclusion of the neighbouring lymph node stations should be considered l as below. ESTRO ACROP guideline’s Option 2 (geometric expansion): (1) geometric expansion of nodal GTV to CTV in analogy to the primary tumour (5–8 mm) (Fig. 1 b). (2) Inclusion of hilum station is optional. (3) Inclusion of uninvolved areas between involved stations (especially the hilum) is optional (4) Inclusion of the neighbouring lymph node stations should be considered as below. Note : If the primary tumor directly invades the mediastinum, metastasizes to different lobes of the ipsilateral lung (T4) and metastasizes to lymph nodes in level of 2, 3a, and 6, it is recommended that CTVn include 1L/R on the same side or both sides. Note : If the tumor is located in the left lower lobe especially for central-type lung cancer and tumor volume is large (≥ 6 cm in diameter), it is recommended that CTVn include level of 8. 5. Conclusions By integrating PET-CT imaging and postoperative pathological data, this histology-stratified, lobar-specific analysis confirms distinct regional LNM patterns and inter-nodal correlations between LUAD and LUSC. The optimized CTVn delineation recommendations proposed for definitive concurrent chemoradiotherapy of LUAD and LUSC provide a evidence-based framework to enhance targeting precision and personalize radiotherapy planning. List of abbreviations Abbreviations Full Forms LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma NSCLC Non-small-cell lung cancer ESTRO ACROP European Society for Radiotherapy & Oncology Advisory Committee on Radiation Oncology Practice CTV Clinical target volume GTV Gross tumor volume LNM Lymph node metastasis IFI Involved-field irradiation ENI Elective nodal irradiation ENF Elective nodal failure IFRT Involved-field radiation therapy IAEA International Atomic Energy Agency Guidelines PET-CT Positron emission tomography-computed tomography AJCC American Joint Committee on Cancer UICC Union for International Cancer Control STAS Spread through air spaces SCLN Supraclavicular lymph nodes Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol, including the waiver of informed consent, was reviewed and approved by the Ethics Committee of the General Hospital of Western Theater Command of PLA (Approval No.: EC5-ky004). Due to the retrospective nature of this study, which involved the analysis of existing anonymized data, the requirement for obtaining individual informed consent was formally waived by the aforementioned Ethics Committee. Consent for publication Not applicable. Competing interests The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Not applicable. Author Contribution Zheng Liu: Writing – Original Draft, Validation, Methodology, Investigation, Software, Formal Analysis; Xiaomei Qian:Data curation, Investigation; Zhihui Li:Methodology; Zhiming Chen: Validation, Resources; Guangjie Wang: Project administration, Resources; Feifan Sun: Software; Hui Gao: Investigation; Jingjing Peng: Methodology; Xiaoli Huang: Validation; Jianqiong Feng: Resources; Min Du: Supervision; Jing Xian: Validation; Lingbo Bao: Validation; Hong Luo: Validation; Binglin Tang: Supervision; Yiyang Hu: Supervision; Yi Li: Supervision; Chao Wang: Validation, Supervision; Chaoyang Jiang: Resources, Validation; Daijun Zhou: Conceptualization, Validation, Supervision; Dong Li: Conceptualization, Resources, Validation, Supervision, Project administration, Writing – Review & Editing. Acknowledgement The authors gratefully acknowledge the staff of the Department of Oncology and Department of Nuclear Medicine at the General Hospital of Western Theater Command for their assistance with data collection. Data Availability The data are available from the corresponding author on reasonable request. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians . 2024;74:229-263. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. Journal of the National Cancer Center . 2024;4:47-53. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. Journal of Thoracic Oncology . 2022;17:362-387. Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Annals of Global Health . 2019;85:1-11. Relli V, Trerotola M, Guerra E, Alberti S. Abandoning the Notion of Non-Small Cell Lung Cancer. Trends in Molecular Medicine . 2019;25:585-594. Li, H., Yuan, S., Wu, H., et al. Combination therapy using low-dose anlotinib and immune checkpoint inhibitors for extensive-stage small cell lung cancer. Cancer innovation, 3(6), e155. Zhou Q, Chen M, Jiang O, et al. Sugemalimab versus placebo after concurrent or sequential chemoradiotherapy in patients with locally advanced, unresectable, stage III non-small-cell lung cancer in China (GEMSTONE-301): interim results of a randomised, double-blind, multicentre, phase 3 trial. The Lancet Oncology . 2022;23:209-219. Xu Z, Zou Z, Hao X, et al. Adjuvant and neo-adjuvant immunotherapy in resectable non-small cell lung cancer (NSCLC): Current status and perspectives. Cancer innovation, 2(1), 65–78. Nestle U, De Ruysscher D, Ricardi U, et al. ESTRO ACROP guidelines for target volume definition in the treatment of locally advanced non-small cell lung cancer. Radiotherapy and Oncology . 2018;127:1-5. Kawase A, Yoshida J, Ishii G, et al. Differences between squamous cell carcinoma and adenocarcinoma of the lung: are adenocarcinoma and squamous cell carcinoma prognostically equal? Japanese Journal of Clinical Oncology . 2012;42:189-195. Deng HY, Zeng M, Li G, et al. Lung Adenocarcinoma has a Higher Risk of Lymph Node Metastasis than Squamous Cell Carcinoma: A Propensity Score-Matched Analysis. World Journal of Surgery . 2019;43:955-962. Giraud P, Antoine M, Larrouy A, et al. Evaluation of microscopic tumor extension in non-small-cell lung cancer for three-dimensional conformal radiotherapy planning. International Journal of Radiation Oncology, Biology, Physics . 2000;48:1015-1024. Sulman EP, Komaki R, Klopp AH, et al. Exclusion of elective nodal irradiation is associated with minimal elective nodal failure in non-small cell lung cancer. Radiation Oncology . 2009;4:5. Rosenzweig KE, Sim SE, Mychalczak B, et al. Elective nodal irradiation in the treatment of non-small-cell lung cancer with three-dimensional conformal radiation therapy. International Journal of Radiation Oncology, Biology, Physics . 2001;50:681-685. Rosenzweig KE, Sura S, Jackson A, Yorke E. Involved-field radiation therapy for inoperable non small-cell lung cancer. Journal of Clinical Oncology . 2007;25:5557-5561. Nestle U, Le Pechoux C, De Ruysscher D. Evolving target volume concepts in locally advanced non small cell lung cancer. Translational Lung Cancer Research . 2021;10:1999-2010. Konert T, Vogel W, MacManus MP, et al. PET/CT imaging for target volume delineation in curative intent radiotherapy of non small cell lung cancer: IAEA consensus report 2014. Radiotherapy and Oncology . 2015;116:27-34. Sun F, Chen Z, Zhou D, et al. Regularity and correlation analysis of regional lymph node metastasis in nonoperative patients with non-small cell lung cancer based on positron emission tomography/computed tomography images. Radiation Oncology . 2024;19:137. Xu N, Wang M, Zhu Z, et al. Integrated positron emission tomography and computed tomography in preoperative lymph node staging of non-small cell lung cancer. Chinese Medical Journal . 2014;127:607-613. Wang YX, Li BS, Huang W, et al. Pattern of lymph node metastases and its implication in radiotherapeutic clinical target volume in patients with non-small-cell lung cancer: a study of 2062 cases. The British journal of radiology. 2015;88(1056):20140288. Nestle U, De Ruysscher D, Ricardi U, et al. ESTRO ACROP guidelines for target volume definition in the treatment of locally advanced non-small cell lung cancer. Radiother Oncol. 2018;127(1):1-5. Additional Declarations No competing interests reported. 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14:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8318884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8318884/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101273301,"identity":"85d37df1-c9a9-43dc-b76c-276c5b346e83","added_by":"auto","created_at":"2026-01-28 03:04:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80787,"visible":true,"origin":"","legend":"\u003cp\u003ea) Option 1: CTV including the whole pathologically affected lymph node station. \u0026nbsp;b) Option 2: Geometric expansion of nodal GTV to CTV in analogy to the primary tumor (5-8 mm) [21].\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8318884/v1/3bc060a61fb2ae21fb06be9e.jpg"},{"id":101397689,"identity":"58d54495-7497-4c14-9bbc-751ded5d0846","added_by":"auto","created_at":"2026-01-29 09:35:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8318884/v1/24bc7135-357b-43cc-bf20-84aa7b52bdab.pdf"},{"id":101297166,"identity":"ccd125ee-a42a-41af-8a60-9208eb31a68d","added_by":"auto","created_at":"2026-01-28 09:25:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1544326,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiguresandtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8318884/v1/859a0880fd43c49693678818.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Histology-Specific Radiotherapy Target Volume Delineation for NSCLC Based on Distinct Lymph Node Metastasis Patterns of Adenocarcinoma and Squamous Cell Carcinoma","fulltext":[{"header":"1. Background","content":"\u003cp\u003eLung cancer remains a leading global public health burden, accounting for over 1.8\u0026nbsp;million deaths worldwide in 2022, representing 18.7% of all cancer-related mortality[1]. In China, the incidence of lung cancer constitutes 22.0% of all malignancies, with a mortality rate as high as 28.5%\u0026mdash;significantly exceeding the global average[2]. Lung adenocarcinoma (LUAD, of bronchial glandular origin) and lung squamous cell carcinoma (LUSC, of bronchial epithelial origin) are the two predominant subtypes of non-small-cell lung cancer (NSCLC), accounting for approximately 50% and 20\u0026ndash;30% of cases, respectively[3, 4]. As is well-known, these two histological subtypes exhibit distinct pathological origins, clinicopathological features, and tumor biological behaviors[5].\u003c/p\u003e \u003cp\u003eDespite the advent of novel anticancer agents[6], concurrent or sequential chemoradiotherapy remains an irreplaceable treatment modality for locally advanced unresectable NSCLC[7,8]. The 2018 ESTRO ACROP consensus emphasized the indispensable role of radiotherapy and provided recommendations for target volume delineation[9]. Defining the clinical target volume (CTV) for lymph nodes requires a thorough understanding of LNM patterns; however, significant controversy persists regarding the biological LNM characteristics specific to LUAD and LUSC[10,11].\u003c/p\u003e \u003cp\u003eInvolved-field irradiation (IFI) is currently recommended for locally advanced inoperable NSCLC instead of elective nodal irradiation (ENI); nonetheless, discrepancies remain regarding optimal CTV delineation for lymph nodes. Previous studies and major NSCLC CTV delineation guidelines recommend an 8 mm expansion margin for LUAD and 6 mm for LUSC to cover 95% of microscopic extension[12]. This recommendation acknowledges the difference in gross tumor microscopic extension between the two subtypes but fails to address disparities in regional nodal metastasis patterns or provide corresponding CTV margin recommendations. Furthermore, the core evidence supporting involved-field radiation therapy (IFRT) stems from retrospective studies reporting elective nodal failure (ENF) rates below 10%\u0026mdash;defined as recurrence in initially uninvolved lymph nodes without local failure[13\u0026ndash;15]. Critically, these studies pooled all NSCLC subtypes without stratifying ENF rates by histology (LUAD vs. LUSC) or providing subtype-specific analyses. Consequently, the risk of out-field failure in LUAD versus LUSC patients treated with IFRT remains unclear. More importantly, no prospective randomized head-to-head trials have compared the efficacy and safety of ENI versus IFRT in locally advanced unresectable NSCLC, particularly stratified by histological subtype.\u003c/p\u003e \u003cp\u003e Positron emission tomography-computed tomography (PET-CT) offers superior diagnostic accuracy and is strongly recommended by the ESTRO ACROP and IAEA guidelines as a core technique for target volume delineation in locally advanced NSCLC radiotherapy[16,17]. It is widely recognized as the most precise imaging modality for investigating regional LNM patterns in NSCLC, especially in unresectable locally advanced disease. Our team previously explored regional LNM patterns in locally advanced NSCLC using PET-CT imaging[18]; however, PET-CT inherently has false-positive and false-negative limitations, particularly in cases complicated by tuberculous infections, granulomatous benign lesions, or chronic inflammatory conditions[19]. Postoperative pathological data can serve as a confirmatory tool to improve diagnostic accuracy but is limited by the fact that patients undergoing radical surgery typically present at earlier stages[20]. Additionally, surgical pathology lacks comprehensive regional LNM information (e.g., contralateral mediastinal or supraclavicular nodes are rarely dissected intraoperatively), failing to fully reflect inherent LNM patterns\u0026mdash;especially in locally advanced unresectable NSCLC. Therefore, a comprehensive systematic analysis integrating high-precision imaging and pathological data is essential to elucidate the inherent regional LNM patterns in NSCLC.\u003c/p\u003e \u003cp\u003eIn this study, we further investigated differences in regional LNM patterns between LUAD and LUSC by integrating PET-CT imaging data and postoperative pathological findings. Our objectives were to: (1) identify consistent NSCLC LNM patterns validated by both modalities; (2) systematically characterize subtype-specific differences and inter-nodal metastatic correlations; and (3) provide evidence-based guidance for precise radiotherapy target delineation.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient Eligibility\u003c/h2\u003e \u003cp\u003eWe retrospectively enrolled 422 pathologically confirmed NSCLC patients (281 LUAD, 141 LUSC) who underwent PET-CT examinations at the General Hospital of Western Theater Command of PLA between January 2018 and August 2024. Postoperative pathology data were collected from 305 additional NSCLC patients (236 LUAD, 69 LUSC) who underwent radical surgical resection at the same institution between January 2017 and August 2024. Exclusion criteria included: (1) N0 stage disease; (2) prior antitumor therapy; (3) incomplete clinical data; (4) mixed NSCLC subtypes; and (5) secondary lung malignancies. All cases were staged according to the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) 8th edition TNM staging system. This study was approved by the Ethics Committee of the General Hospital of Western Theater Command of PLA (Approval No. : EC5-ky004), with a waiver of informed consent due to its retrospective nature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Imaging Examination and LNM Confirmation\u003c/h2\u003e \u003cp\u003e Lymph node mapping followed the 2009 International Association for the Study of Lung Cancer (IASLC) nodal classification. The PET-CT examination protocol and image interpretation criteria were consistent with our previously published methodology[18]. A lymph node was defined as metastatic on PET-CT if it met all the following criteria: (1) short-axis diameter\u0026thinsp;\u0026ge;\u0026thinsp;1.0 cm; (2) maximum standardized uptake value (SUVmax)\u0026thinsp;\u0026ge;\u0026thinsp;2.5; and (3) absence of plaque-like calcification. Lymph nodes enlarged on CT without significant radiotracer uptake were not considered metastatic.\u003c/p\u003e \u003cp\u003eFor postoperative pathological specimens, the Ki-67 proliferation index was assessed by immunohistochemistry to quantify tumor cell nuclear positivity. Pathologists microscopically evaluated and documented vascular invasion, neural invasion, pleural involvement, and spread through air spaces (STAS). The maximum diameter of the primary tumor was measured intraoperatively and confirmed pathologically, then categorized according to T-stage criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were organized using Microsoft Excel and analyzed with IBM SPSS Statistics\u0026reg; 27 software. Categorical variables were presented as frequencies (percentages), with inter-group comparisons performed using the chi-square test or Fisher\u0026rsquo;s exact test (when expected frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5). Continuous variables conforming to a normal distribution (e.g., age, maximum tumor diameter) were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; non-normal variables were presented as median (interquartile range). Inter-group comparisons of continuous variables were performed using the independent samples t-test or Mann-Whitney U test, as appropriate.\u003c/p\u003e \u003cp\u003eBinary logistic regression models were employed for multivariate analysis to explore correlations between metastatic lymph node levels. The Hosmer-Lemeshow test was used to assess logistic regression model goodness-of-fit, with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating satisfactory model calibration. All tests were two-sided, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Patient Characteristics\u003c/h2\u003e \u003cp\u003eThe PET-CT cohort included 422 NSCLC patients (281 LUAD [66.6%], 141 LUSC [33.4%]). Baseline characteristics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed in age distribution or primary tumor location between subtypes. LUSC was associated with larger maximum tumor diameter (5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53 cm vs. 3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03 cm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and lower distant metastasis rate (33.3% vs. 64.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared with LUAD. LUAD exhibited higher rates of lymph vascular invasion (62.3% vs. 47.8%, p\u0026thinsp;=\u0026thinsp;0.032) and spread through air spaces (STAS; 21.6% vs. 5.8%, p\u0026thinsp;=\u0026thinsp;0.003). The pathological cohort included 305 NSCLC patients (236 LUAD [77.4%], 69 LUSC [22.6%]). Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No significant differences were noted in median age or overall lobar distribution. Statistically significant differences are highlighted in bold.\u003c/p\u003e \n\u003cp\u003eTable 1. Clinical Baseline Characteristics of LUAD and LUSC Based on PET-CT\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003eLUAD, n=281(66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003eLUSC, n=141(33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eMedian age[year, M(P\u003csub\u003e25,\u003c/sub\u003e P\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e64(56,71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e65(58,71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e154(54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e125(88.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e127(45.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e16(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eLocation of primary tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eLeft Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e120(42.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e70(49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eSuperior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e78(27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e38(27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eInferior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e42(14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e32(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eRight lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e161(57.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e71(50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eSuperior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e79(28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e31(22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eMiddle lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e19(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e7(5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eInferior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e63(22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e33(23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eTumor type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003ePeripheral lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e238(84.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e53(37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eCentral lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e43(15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e88(62.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eMaximum diameter [cm,x\u0026plusmn;s]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e3.87\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.48\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e2.53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eLocal invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e166(59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e91(64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eVisceral pleura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e136(48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e64(45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eMediastinum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e29(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e21(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eSeparate nodules in same lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51(18.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e12(8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eSeparate nodules in different ipsilateral lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49(17.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e8(5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eOthers \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e6(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e13(9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eN classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e23(8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e18(12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e100(35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e54(38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4649%;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e158(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2508%;\"\u003e\n \u003cp\u003e69(48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0334%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003eLUAD, n=281(66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003eLUSC, n=141(33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e1(0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e34(12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e5(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e36(12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e11(7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e58(20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e25(20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e26(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e16(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e49(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e32(22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e77(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e52(36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e100(35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e96(68.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e181(64.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e45(31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e32(11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e10(7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72(25.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e16(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eM1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e77(27.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e19(13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eTNM Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅡB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e11(3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e18(4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅢA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e33(11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e59(14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅢB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e36(12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e70(16.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅢC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e20(7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e49(11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅣA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e104(37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e129(30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eⅣB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e77(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e97(23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eDistant metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e181(64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e47(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eBone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e128(45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e24(17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003ePleural dissemination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e49(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e15(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eLiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e24(8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e9(6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eContralateral lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37(13.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e7(5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eBrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20(7.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e2(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.9983%;\"\u003e\n \u003cp\u003eAdrenal gland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e28(9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.4757%;\"\u003e\n \u003cp\u003e7(5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0503%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003eTable 2. Clinical Baseline Characteristics of LUAD and LUSC Based on Pathological Data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003eLUAD n=236 (77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003eLUSC n=69(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eMedian age [year, M (P25, P75)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e56.5(25, 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e58(IQR, 53-63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e119(50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e62(89.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e117(49.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e7(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eLocation of primary tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eLeft Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e86(36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e29(42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eSuperior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e43(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e14(20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eInferior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e43(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e15(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eRight lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e150(63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e40(58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eSuperior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e70(29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e13(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eMiddle lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e23(9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e11(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eInferior lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e57(24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e16(23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eMaximum diameter [cm, M (P25, P75)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e2.50 (2.0, 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.20 (2.5, 3.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003ePathological characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eVisceral pleura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e158(66.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e32(46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eNeural invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e55(23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e10(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e147(62.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e33(47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eSpread through air spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61(21.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e4(5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eBronchial stump\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1(0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e2(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eKi-67 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eMedian score [M (P25, P75)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e20%(10%, 30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40%(30%, 60%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e2%-60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e3%-70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eN classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e59(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e29(42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e175(74.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e40(58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.1443%;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e2(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7423%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Concordance Between PET-CT and Pathological LNM Patterns\u003c/h2\u003e \u003cp\u003eOverall, PET-CT and postoperative pathological data exhibited high concordance in LNM distribution patterns for both LUAD and LUSC, confirming the reliability of PET-CT-derived LNM patterns. For LUAD across pulmonary lobes, the top five metastatic nodal levels identified by both modalities showed substantial overlap: left superior lobe (10L, 5, 4L, 6); left inferior lobe (10L, 7, 4L, 5); right upper lobe (10R, 4R, 2R, 7); right middle lobe (10R, 7, 4R, 2R); and right inferior lobe (7, 10R, 4R, 2R). For LUSC across pulmonary lobes, concordant top metastatic levels included: left superior lobe (10L, 4L, 5, 7); left inferior lobe (10L, 7, 4L, 5); right upper lobe (10R, 4R, 2R, 7); right middle lobe (10R, 4R, 2R); and right inferior lobe (10R, 7, 4R, 2R) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eComparative Analysis of Lobar LNM Distribution in LUAD vs. LUSC by Pulmonary Lobe Based on PET-CT and Postoperative Pathology Data\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUAD n\u0026thinsp;=\u0026thinsp;236 (77.4%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLUSC n\u0026thinsp;=\u0026thinsp;69(22.6%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age [year, M (P25, P75)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5(25, 75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(IQR, 53\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e62(89.9%)\u003c/b\u003e\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e117(49.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(10.1%)\u003c/p\u003e \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\u003eLocation of primary tumor\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(20.3%)\u003c/p\u003e \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\u003eInferior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(21.7%)\u003c/p\u003e \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\u003eRight lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150(63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(18.8%)\u003c/p\u003e \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\u003eMiddle lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(15.9%)\u003c/p\u003e \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\u003eInferior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(23.2%)\u003c/p\u003e \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\u003eMaximum diameter [cm, M (P25, P75)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50 (2.0, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.20 (2.5, 3.0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological characteristics\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\u003eVisceral pleura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e158(66.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(46.4%)\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\u003eNeural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e147(62.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpread through air spaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e61(21.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eBronchial stump\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67 score\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian score [M (P25, P75)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20%(10%, 30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e40%(30%, 60%)\u003c/b\u003e\u003c/p\u003e \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\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2%-60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3%-70%\u003c/p\u003e \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\u003eN classification\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(42.0%)\u003c/p\u003e \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\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e175(74.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(58.0%)\u003c/p\u003e \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\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subtype-Specific LNM Distribution Patterns\u003c/h2\u003e \u003cp\u003ePathological analysis of resectable NSCLC revealed distinct LNM distribution patterns between LUAD and LUSC (Figure S1, Table S1). Specifically, LUAD demonstrated a significantly higher metastasis rate at level 4R (29.2% vs. 13.0%, p\u0026thinsp;=\u0026thinsp;0.007) and a trend toward increased level 2R involvement (25.0% vs. 14.5%, p\u0026thinsp;=\u0026thinsp;0.067) compared with LUSC\u0026mdash;particularly in the right lung (Tables S3, S4). Univariate and multivariate analyses identified inter-nodal metastatic correlations in resected LUAD: 2R metastasis correlated with ipsilateral 4R; 4L correlated with 6; and level 7 correlated with level 9 (Table S5). Limitations of surgical pathology included infrequent dissection of supraclavicular lymph nodes (SCLN) and inability to assess nodes outside the surgical field\u0026mdash;gaps effectively addressed by PET-CT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePET-CT analysis further confirmed subtype-specific LNM distributions (Figure S2, Table S2). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, LUAD had higher SCLN metastasis rates (1L: 21.0% vs. 12.1%, p\u0026thinsp;=\u0026thinsp;0.024; 1R: 31.7% vs. 19.9%, p\u0026thinsp;=\u0026thinsp;0.011), while LUSC exhibited higher level 8 metastasis (29.8% vs. 19.9%, p\u0026thinsp;=\u0026thinsp;0.024). Multivariate analysis revealed that LUAD conferred 1.87-fold (1R, 95%CI: 1.15\u0026ndash;3.04) and 1.94-fold (1L, 95%CI: 1.08\u0026ndash;3.47) increased SCLN metastasis risks compared with LUSC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Overall LNM Rates Between LUAD and LUSC based on PET-CT data.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLNM Levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eThe number and rate of LNM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUAD (n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLUSC (n\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e59(21.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e17(12.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e89(31.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28(19.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109(38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116(41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60(42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175(62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96(34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155(55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e56(19.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e42(29.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112(39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64(45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168(59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82(58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-IPLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-IPLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30(21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\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\u003eSide-stratified analysis showed that left lung LUSC had higher level 8 metastasis (38.6% vs. 18.3%, p\u0026thinsp;=\u0026thinsp;0.002), while left lung LUAD had greater contralateral 1R involvement (24.2% vs. 10.0%, p\u0026thinsp;=\u0026thinsp;0.016). For right lung tumors, LUAD exhibited increased contralateral 1L (17.4% vs. 7.0%, p\u0026thinsp;=\u0026thinsp;0.038) and contralateral hilar 10L metastasis (10.6% vs. 2.8%, p\u0026thinsp;=\u0026thinsp;0.048) (Table S6). Lobar-specific analysis demonstrated that left inferior lobe LUSC had higher level 8 metastasis (50.0% vs. 26.2%, p\u0026thinsp;=\u0026thinsp;0.035), while right inferior lobe LUAD showed increased contralateral 1L (20.0% vs. 3.0%, p\u0026thinsp;=\u0026thinsp;0.020) and 10L metastasis (11.0% vs. 0%, p\u0026thinsp;=\u0026thinsp;0.047) (Tables S7, S8).\u003c/p\u003e \u003cp\u003eEmploying the same method as resected LUAD data, we also explored potential relationships among metastatic lymph node levels. Specifically, for the LUAD patients, significant correlations were confirmed between level 1L and 1R, 3a, 4L and 6, as well as correlation between 1R and 1L, 2L, 2R and 10R. Similarly, 2L was linked with 1R, 4L and 6, and 2R exhibited pronounced connections with 1R and 4R (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Level 3a correlated with 1L and 6 (See Table S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 High-Risk Factors for Site-Specific Metastasis\u003c/h2\u003e \u003cp\u003eGiven the identified propensity for SCLN metastasis in LUAD, we sought to characterize the high-risk patient to guide radiotherapy planning. LUAD patients with SCLN metastasis were compared with those without to identify high-risk features (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Multivariate analysis revealed that LUAD with mediastinal invasion (T3), ipsilateral multi-lobar separate nodules (T4), or LNM at levels 2L, 2R, 3a, or 6 had significantly higher SCLN metastasis risk (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparison of LUSC patients with and without level 8 metastasis identified key risk factors (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e): left inferior lobe origin (38.1% vs. 16.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and larger maximum tumor diameter (6.305\u0026thinsp;\u0026plusmn;\u0026thinsp;2.744 cm vs. 5.129\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36 cm, p\u0026thinsp;=\u0026thinsp;0.011) were independently associated with level 8 metastasis. Fisher\u0026rsquo;s exact test was used for location analysis due to expected frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5 in 20% of cells.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparasion of LUAD patients with/without supraclavicular lymph nodes metastasis\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLNM (+) n\u0026thinsp;=\u0026thinsp;110\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLNM (-) n\u0026thinsp;=\u0026thinsp;170\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal invasion\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 \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral pleura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(47.1%)\u003c/p\u003e \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\u003eMediastinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e15(13.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparate nodules in same lobe (T3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(11.2%)\u003c/p\u003e \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\u003eSeparate nodules in different ipsilateral lobe (T4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30(27.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \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\u003e1(0.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e29(26.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e76(69.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e36(32.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \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\u003e4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(30.6%)\u003c/p\u003e \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\u003e4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(49.4%)\u003c/p\u003e \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\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(28.8%)\u003c/p\u003e \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\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e48(43.6%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(44.1%)\u003c/p\u003e \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\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(13.5%)\u003c/p\u003e \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\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(8.8%)\u003c/p\u003e \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\u003e10L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(40%)\u003c/p\u003e \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\u003e10R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(50%)\u003c/p\u003e \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\u003eL-IPLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(14.7%)\u003c/p\u003e \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\u003eR-IPLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of LUSC patients with/without level 8 metastasis\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel 8 (+) n\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 8 (-) n\u0026thinsp;=\u0026thinsp;99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of primary tumor\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (43.4%)\u003c/p\u003e \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\u003eSuperior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (27.3%)\u003c/p\u003e \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\u003eInferior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16 (38.1%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (56.6%)\u003c/p\u003e \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\u003eSuperior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (7.1%)\u003c/p\u003e \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\u003eInferior lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (22.2%)\u003c/p\u003e \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\u003eMaximum diameter [cm, x\u0026thinsp;\u0026plusmn;\u0026thinsp;s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.305\u0026thinsp;\u0026plusmn;\u0026thinsp;2.744\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.129\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\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*: As the theoretical frequency was less than 5 in 20% of the data, the Fisher's exact test was employed for this analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBuilding on our prior work in \u003cem\u003eRadiation Oncology\u003c/em\u003e demonstrating significant LNM correlations in inoperable NSCLC[18], this study integrated PET-CT data from 422 treatment-naive patients and pathological data from 305 surgical cases to investigate lobar-specific LNM patterns and inter-nodal correlations between LUAD and LUSC. Our findings confirm that LUAD exhibits a more aggressive metastatic phenotype and underscore fundamental differences in LNM patterns between subtypes\u0026mdash;supporting histology-adapted CTV delineation to enhance radiotherapy precision.\u003c/p\u003e \u003cp\u003eLobar-specific analyses revealed distinct metastatic signatures: LUAD primarily involved levels 5/10L/4L/6/7 in the left lung and 10R/2R/4R/7 in the right lung, with the highest metastasis rates in the right middle/lower lobes (particularly the right inferior lobe). In contrast, LUSC predominantly metastasized to levels 10L/7/4L in the left lung and 7/10R/4R/2R in the right lung.\u003c/p\u003e \u003cp\u003eCTV delineation in NSCLC radiotherapy remains heavily experience-dependent due to the paucity of prospective evidence. To some extent, our findings address this critical gap by providing subtype-specific data to be expected to optimize definitive radiotherapy planning (though prospective validation is still needed). The ESTRO ACROP guidelines recommend IFRT for locally advanced inoperable NSCLC and offer two CTV delineation options: Option 1 (inclusion of entire involved nodal stations with \u0026ge;\u0026thinsp;5\u0026ndash;8 mm margin around GTV) and Option 2 (geometric GTV-to-CTV expansion [5\u0026ndash;8 mm] with smaller volume) [13\u0026ndash;15]. Consistent with prior evidence of low out-field recurrence with IFRT, we advocate for IFRT over ENI but propose subtype stratification: LUAD may benefit from Option 1 (larger volume) while LUSC is better suited for Option 2 (smaller volume).\u003c/p\u003e \u003cp\u003e The ESTRO ACROP guidelines also mention elective inclusion of hilar and/or neighboring nodal stations but do not define \"neighboring\" or specify inclusion criteria[21]. Our data partially clarify these ambiguities by identifying some subtype-specific high-risk stations such as SCLN for LUAD and level 8 for large-volume LUSC. Integrating these findings with guideline recommendations, we propose optimized CTVn delineation strategies for LUAD and LUSC (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Of course, future studies should specifically evaluate out-field failure rates by subtype and conduct prospective trials of subtype-adapted CTV delineation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its retrospective design means our CTVn recommendations require prospective validation. Second, factors including tumor differentiation grade, central/peripheral location, and EGFR mutation status\u0026mdash;potential modifiers of LNM patterns\u0026mdash;were not analyzed (e.g., poorly differentiated LUSC may mimic LUAD behavior). Third, single-center enrollment introduces potential selection bias despite large sample sizes. Fourth, PET-CT false positives/negatives and unevaluated molecular features (EGFR/PD-L1) may limit interpretation. Future multicenter prospective studies should integrate clinical, imaging, and pathological data to refine LNM prediction models, explore molecular/subtype modifiers, and leverage radiomics/artificial intelligence to enhance detection accuracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSuggestion for CTV delineation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUSC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESTRO ACROP guideline\u0026rsquo;s Option 1 (lymph node stations): (1) inclusion of the whole pathologically affected lymph node station (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) including at least a 8 mm margin around the GTV. (2) Inclusion of the hilum and uninvolved areas between involved stations. (3) Inclusion of the neighbouring lymph node stations should be considered l as below.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESTRO ACROP guideline\u0026rsquo;s Option 2 (geometric expansion): (1) geometric expansion of nodal GTV to CTV in analogy to the primary tumour (5\u0026ndash;8 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). (2) Inclusion of hilum station is optional. (3) Inclusion of uninvolved areas between involved stations (especially the hilum) is optional (4) Inclusion of the neighbouring lymph node stations should be considered as below.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: If the primary tumor directly invades the mediastinum, metastasizes to different lobes of the ipsilateral lung (T4) and metastasizes to lymph nodes in level of 2, 3a, and 6, it is recommended that CTVn include 1L/R on the same side or both sides.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: If the tumor is located in the left lower lobe especially for central-type lung cancer and tumor volume is large (\u0026ge;\u0026thinsp;6 cm in diameter), it is recommended that CTVn include level of 8.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"5. Conclusions","content":"\u003cp\u003eBy integrating PET-CT imaging and postoperative pathological data, this histology-stratified, lobar-specific analysis confirms distinct regional LNM patterns and inter-nodal correlations between LUAD and LUSC. The optimized CTVn delineation recommendations proposed for definitive concurrent chemoradiotherapy of LUAD and LUSC provide a evidence-based framework to enhance targeting precision and personalize radiotherapy planning.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"520\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Forms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\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: 29.0385%;\"\u003e\n \u003cp\u003eESTRO ACROP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eEuropean Society for Radiotherapy \u0026amp; Oncology Advisory Committee on Radiation Oncology Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eCTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eClinical target volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eGTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eGross tumor volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eInvolved-field irradiation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eENI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eElective nodal irradiation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eENF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eElective nodal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eIFRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eInvolved-field radiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eIAEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eInternational Atomic Energy Agency Guidelines\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003ePET-CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003ePositron emission tomography-computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eAJCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eUICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eUnion for\u0026nbsp;International Cancer Control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.0385%;\"\u003e\n \u003cp\u003eSTAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\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: 29.0385%;\"\u003e\n \u003cp\u003eSCLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.9615%;\"\u003e\n \u003cp\u003eSupraclavicular lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol, including the waiver of informed consent, was reviewed and approved by the Ethics Committee of the General Hospital of Western Theater Command of PLA (Approval No.: EC5-ky004). Due to the retrospective nature of this study, which involved the analysis of existing anonymized data, the requirement for obtaining individual informed consent was formally waived by the aforementioned Ethics Committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZheng Liu: Writing \u0026ndash; Original Draft, Validation, Methodology, Investigation, Software, Formal Analysis; Xiaomei Qian:Data curation, Investigation; Zhihui Li:Methodology; Zhiming Chen: Validation, Resources; Guangjie Wang: Project administration, Resources; Feifan Sun: Software; Hui Gao: Investigation; Jingjing Peng: Methodology; Xiaoli Huang: Validation; Jianqiong Feng: Resources; Min Du: Supervision; Jing Xian: Validation; Lingbo Bao: Validation; Hong Luo: Validation; Binglin Tang: Supervision; Yiyang Hu: Supervision; Yi Li: Supervision; Chao Wang: Validation, Supervision; Chaoyang Jiang: Resources, Validation; Daijun Zhou: Conceptualization, Validation, Supervision; Dong Li: Conceptualization, Resources, Validation, Supervision, Project administration, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the staff of the Department of Oncology and Department of Nuclear Medicine at the General Hospital of Western Theater Command for their assistance with data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e. 2024;74:229-263.\u003c/li\u003e\n\u003cli\u003eHan B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. \u003cem\u003eJournal of the National Cancer Center\u003c/em\u003e. 2024;4:47-53. \u003c/li\u003e\n\u003cli\u003eNicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. \u003cem\u003eJournal of Thoracic Oncology\u003c/em\u003e. 2022;17:362-387. \u003c/li\u003e\n\u003cli\u003eBarta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. \u003cem\u003eAnnals of Global Health\u003c/em\u003e. 2019;85:1-11. \u003c/li\u003e\n\u003cli\u003eRelli V, Trerotola M, Guerra E, Alberti S. Abandoning the Notion of Non-Small Cell Lung Cancer. \u003cem\u003eTrends in Molecular Medicine\u003c/em\u003e. 2019;25:585-594. \u003c/li\u003e\n\u003cli\u003eLi, H., Yuan, S., Wu, H., et al. Combination therapy using low-dose anlotinib and immune checkpoint inhibitors for extensive-stage small cell lung cancer. Cancer innovation, 3(6), e155.\u003c/li\u003e\n\u003cli\u003eZhou Q, Chen M, Jiang O, et al. Sugemalimab versus placebo after concurrent or sequential chemoradiotherapy in patients with locally advanced, unresectable, stage III non-small-cell lung cancer in China (GEMSTONE-301): interim results of a randomised, double-blind, multicentre, phase 3 trial. \u003cem\u003eThe Lancet Oncology\u003c/em\u003e. 2022;23:209-219. \u003c/li\u003e\n\u003cli\u003eXu Z, Zou Z, Hao X, et al. Adjuvant and neo-adjuvant immunotherapy in resectable non-small cell lung cancer (NSCLC): Current status and perspectives. Cancer innovation, 2(1), 65\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eNestle U, De Ruysscher D, Ricardi U, et al. ESTRO ACROP guidelines for target volume definition in the treatment of locally advanced non-small cell lung cancer. \u003cem\u003eRadiotherapy and Oncology\u003c/em\u003e. 2018;127:1-5.\u003c/li\u003e\n\u003cli\u003eKawase A, Yoshida J, Ishii G, et al. Differences between squamous cell carcinoma and adenocarcinoma of the lung: are adenocarcinoma and squamous cell carcinoma prognostically equal? \u003cem\u003eJapanese Journal of Clinical Oncology\u003c/em\u003e. 2012;42:189-195. \u003c/li\u003e\n\u003cli\u003eDeng HY, Zeng M, Li G, et al. Lung Adenocarcinoma has a Higher Risk of Lymph Node Metastasis than Squamous Cell Carcinoma: A Propensity Score-Matched Analysis. \u003cem\u003eWorld Journal of Surgery\u003c/em\u003e. 2019;43:955-962. \u003c/li\u003e\n\u003cli\u003eGiraud P, Antoine M, Larrouy A, et al. Evaluation of microscopic tumor extension in non-small-cell lung cancer for three-dimensional conformal radiotherapy planning. \u003cem\u003eInternational Journal of Radiation Oncology, Biology, Physics\u003c/em\u003e. 2000;48:1015-1024. \u003c/li\u003e\n\u003cli\u003eSulman EP, Komaki R, Klopp AH, et al. Exclusion of elective nodal irradiation is associated with minimal elective nodal failure in non-small cell lung cancer. \u003cem\u003eRadiation Oncology\u003c/em\u003e. 2009;4:5. \u003c/li\u003e\n\u003cli\u003eRosenzweig KE, Sim SE, Mychalczak B, et al. Elective nodal irradiation in the treatment of non-small-cell lung cancer with three-dimensional conformal radiation therapy. \u003cem\u003eInternational Journal of Radiation Oncology, Biology, Physics\u003c/em\u003e. 2001;50:681-685.\u003c/li\u003e\n\u003cli\u003eRosenzweig KE, Sura S, Jackson A, Yorke E. Involved-field radiation therapy for inoperable non small-cell lung cancer. \u003cem\u003eJournal of Clinical Oncology\u003c/em\u003e. 2007;25:5557-5561. \u003c/li\u003e\n\u003cli\u003eNestle U, Le Pechoux C, De Ruysscher D. Evolving target volume concepts in locally advanced non small cell lung cancer. \u003cem\u003eTranslational Lung Cancer Research\u003c/em\u003e. 2021;10:1999-2010. \u003c/li\u003e\n\u003cli\u003eKonert T, Vogel W, MacManus MP, et al. PET/CT imaging for target volume delineation in curative intent radiotherapy of non small cell lung cancer: IAEA consensus report 2014. \u003cem\u003eRadiotherapy and Oncology\u003c/em\u003e. 2015;116:27-34. \u003c/li\u003e\n\u003cli\u003eSun F, Chen Z, Zhou D, et al. Regularity and correlation analysis of regional lymph node metastasis in nonoperative patients with non-small cell lung cancer based on positron emission tomography/computed tomography images. \u003cem\u003eRadiation Oncology\u003c/em\u003e. 2024;19:137. \u003c/li\u003e\n\u003cli\u003eXu N, Wang M, Zhu Z, et al. Integrated positron emission tomography and computed tomography in preoperative lymph node staging of non-small cell lung cancer. \u003cem\u003eChinese Medical Journal\u003c/em\u003e. 2014;127:607-613. \u003c/li\u003e\n\u003cli\u003eWang YX, Li BS, Huang W, et al. Pattern of lymph node metastases and its implication in radiotherapeutic clinical target volume in patients with non-small-cell lung cancer: a study of 2062 cases. The British journal of radiology. 2015;88(1056):20140288. \u003c/li\u003e\n\u003cli\u003eNestle U, De Ruysscher D, Ricardi U, et al. ESTRO ACROP guidelines for target volume definition in the treatment of locally advanced non-small cell lung cancer. Radiother Oncol. 2018;127(1):1-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Lymph Node Metastasis Pattern, PET/CT, Pathology, Radiotherapy Target Volume","lastPublishedDoi":"10.21203/rs.3.rs-8318884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8318884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study sought to characterize distinct regional lymph node metastasis (LNM) patterns of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) using PET-CT imaging and postoperative pathology, and to provide evidence-based guidance for precise radiotherapy target volume delineation in NSCLC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 422 PET-CT scans (281 LUAD, 141 LUSC) and 305 surgical pathology reports (236 LUAD, 69 LUSC) from our institution. Inter-group comparisons were performed using chi-square or Fisher\u0026rsquo;s exact tests. Binary logistic regression models were utilized for multivariate analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePET-CT and pathological data exhibited high concordance in LNM distribution. Compared with LUSC, LUAD displayed more aggressive LNM behavior, with significantly higher metastasis rates to supraclavicular, contralateral mediastinal, and contralateral hilar nodes. In contrast, LUSC showed increased level 8 LNM, especially in left inferior lobe origin. Multivariate analysis revealed: LUAD with mediastinal invasion, ipsilateral multi-lobar nodules, or LNM at levels 2L/2R/3a/6 had higher 1L/1R metastasis risk; left inferior lobe origin or larger tumor diameter in LUSC hinted level 8 metastasis. Significant inter-nodal metastatic correlations were identified across different levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLUAD and LUSC exhibit distinct histology- and subsite-specific LNM patterns and inter-nodal metastatic correlations. Two optimized CTVn delineation recommendations for definitive concurrent chemoradiotherapy of LUAD and LUSC were proposed to enhance targeting precision.\u003c/p\u003e","manuscriptTitle":"Histology-Specific Radiotherapy Target Volume Delineation for NSCLC Based on Distinct Lymph Node Metastasis Patterns of Adenocarcinoma and Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:04:17","doi":"10.21203/rs.3.rs-8318884/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-22T09:56:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T20:25:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T11:37:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Radiation Oncology","date":"2025-12-12T10:37:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1890e8e0-1012-430a-8dd0-df47be32fb96","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T03:04:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 03:04:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8318884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8318884","identity":"rs-8318884","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}