Validation for revision of the stage IIIA(T1N2) in the forthcoming ninth edition of the TNM classification for lung cancer

Validation for revision of the stage IIIA(T1N2) in the forthcoming ninth edition of the TNM classification for lung cancer | 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 Validation for revision of the stage IIIA(T1N2) in the forthcoming ninth edition of the TNM classification for lung cancer Tong Wu, Jingsheng Cai, Yun Li, Kezhong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4727507/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2025 Read the published version in BMC Cancer → Version 1 posted 4 You are reading this latest preprint version Abstract Objectives The 9th edition of the lung cancer tumor-node-metastasis (TNM) staging system downgrades certain non-small cell lung cancer (NSCLC) patients from stage IIIA (T1N2) to IIB. This study aimed to externally validate this stage adjustment. Methods Consecutive resected stage IIB and IIIA NSCLC patients were included. Subgrouping was done based on lymph node involvements: IIB N2a1 (single-station N2 without N1 involvement), IIB N2a2 (single-station N2 with N1 involvements) and IIB N0-1. Overall survival (OS) and disease-free survival (DFS) were compared using the Kaplan-Meier method, with propensity score matching (PSM) employed to mitigate potential biases. COX regression models were utilized to assess prognostic differences. Results 224 stage IIB and 227 stage IIIA cases was included. There were 38, 66 and 120 patients in the IIB N2a1, IIB N2a2 and IIB N0-1 subgroups, respectively. Univariate COX analysis indicated comparable prognoses between the stage IIB N0-1 and IIB N2a1 patients, whereas stage IIB N2a2 patients exhibited poorer outcomes. Upon combining the stage IIB N2a1 and IIB N0-1 subgroups, multivariate COX analysis demonstrated a significantly worse prognosis for stage IIB N2a2 patients compared to those with stage IIB N2a1/0–1 tumors (OS, P = 0.035; DFS, P = 0.021). Further comparisons between stage IIB N2a2 and IIIA patients, following PSM analysis, indicated similar survivals (OS: P = 0.390; DFS: P = 0.210). Conclusion The prognosis of stage IIB N2a2 patients was worse than that of remaining stage IIB patients but comparable to that of stage IIIA patients. We proposed that stage IIB N2a2 patients should be maintained as stage IIIA. non-small cell lung cancer stage IIB stage IIIA prognosis The 9th edition of the lung cancer TNM staging system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Lung cancer is the most malignant tumor with the highest incidence and mortality in the world [ 1 ] . Non-small cell lung cancer (NSCLC) constitutes approximately 85% of cases [ 2 ] . The lung cancer tumor-node-metastasis (TNM) staging manual, proposed by the International Association for the Study of Lung Cancer, serves as the cornerstone for the standardized treatment of patients with NSCLC [ 3 , 4 ] . In 2024, the 9th edition of TNM staging manual for lung cancer [ 3 ] was released, maintaining the existing T staging without alterations. However, notable updates were made to N staging, introducing N2 sub-categories (N2a: single-station N2 metastasis; N2b: multi-station N2 metastasis) [ 3 ] . Additionally, the M staging was further refined, with M1c now subdivided into M1c1 (multiple extra-thoracic metastases in one organ) and M1c2 (multiple extra-thoracic metastases in multiple organs) [ 3 , 5 ] . From the TNM staging standpoint, T1N1, formerly assigned to stage IIB, has been reclassified to stage IIA. Conversely, T1N2, previously designated as stage IIIA, has been subdivided into T1N2a (stage IIB) and T1N2b (stage IIIA) [ 3 ] . The revisions introduced in the latest staging edition necessitate external validation to confirm their accuracy and generalizability. Within this manuscript, we direct our attention to a specific subgroup of patients transitioning from stage IIIA to IIB (T1N2a). Previous studies suggested that skip N2 metastasis is associated with improved survivals in N2 lung cancer [ 6 – 9 ] . Therefore, we further separated stage IIB patients into three subgroups: stage IIB N2a1 (single-station N2 without N1 involvement), stage IIB N2a2 (single-station N2 with N1 involvements) and stage IIB N0-1. In this study, we set out to investigate potential prognostic differences among stage IIB NSCLC patients and further externally validate the stage IIB classification in the 9th edition of lung cancer TNM staging manual. MATERIALS AND METHODS Ethical statement This study was approved by the Ethics Committee of Peking University People's Hospital. Given the anonymized patient data and retrospective design, written informed consent was not required for this study. Study Population The clinical records of 7,931 patients diagnosed with pulmonary malignant tumors and admitted to the Department of Thoracic Surgery at Peking University People's Hospital between 1999 and 2018 (PKUPHTOI dataset) were systematically reviewed. This well-managed dataset has been used before [10-13] . Inclusion criteria were as follows: (1) Diagnosis of NSCLC; (2) Underwent surgical resection; (3) stages IIB and IIIA (the 9th edition of lung cancer TNM staging manual). Exclusion criteria comprised: (1) Age < 18 years; (2) Presence of N3 disease; (3) Presence of M1 disease; (4) R1/R2 resection; (5) Sublobar resection; (6) Non-systematic lymph node dissection; (7) Primary lung cancer; (8) Receipt of neoadjuvant therapy; (9) History of previous malignancies; (10) Unavailability of clinicopathological data. A total of 2,051 eligible patients were finally included, consisting of 224 individuals with stage IIB and 227 with stage IIIA disease. Figure 1 illustrates the patient selection process. Based on the lymph node metastasis status, the stage IIB patients were further divided into three categories: IIB N2a1, IIB N2a2, and IIB N0-1. Data Collection The clinicopathological dataset encompassed various parameters, including age, gender (male/female), smoking history (yes/no), family tumor history (yes/no), preoperative comorbidities (yes/no), body mass index (BMI), American Society of Anesthesiologist (ASA) classification (grades 1/2/3/4), surgical approach (thoracoscopy/thoracotomy), extent of surgical resection (lobectomy, bilobectomy, and pneumonectomy), and postoperative complications. Additionally, histopathological characteristics such as histology (adenocarcinoma, squamous cell carcinoma and other), along with factors like visceral pleural invasion (VPI) (yes/no), lymphovascular invasion (LVI) (yes/no), occurrence of postoperative complications (yes/no), and receipt of postoperative adjuvant therapy (yes/no). Patient outcomes, including mortality and time to recurrence, were also recorded. Complete data analysis was performed in this study. Surgical procedure To ensure the absolute accuracy of N staging, we exclusively included patients who underwent radical surgical resections and systemic mediastinal lymph node dissection. The standard protocol of surgery was similar to the one previously described by Xu et.al [14] . Systemic lymphadenectomy was defined as mediastinal lymph node dissection of at least 3 stations, including station 7 (the subcarinal lymph node), from station 4L, 5, 6, 7, 8 and 9 for the left-side NSCLCs and station 2R, 3A, 4R, 7, 8 and 9 for the right-side NSCLCs. In addition, at least 6 lymph nodes were harvested. As for N1 station lymph nodes, in routine, the station 10, 11 and 12 were dissected intraoperatively. The station 13 and 14 lymph nodes were dissected by resident doctors from the excised specimen, but this procedure was not mandatory. Follow-up The routine follow-up strategies for patients in our center have been previously reported [12] . Specifically，follow-up data were acquired through comprehensive review of medical records, direct patient consultations, and telephonic interviews. Our center adheres to a rigorous postoperative follow-up protocol, involving assessments every three months during the initial two years post-surgery, biannually for the subsequent three to five years, and annually thereafter. Each follow-up session includes thorough physical examinations, monitoring of serum tumor markers, and chest computed tomography scans. Additional diagnostic modalities such as brain magnetic resonance imaging and bone scans are conducted as warranted by clinical indications. The primary endpoints of this study were overall survival (OS) and disease-free survival (DFS). OS was defined as the period from the date of diagnosis to all-cause death or the date of last follow-up. DFS was defined as the period from the date of diagnosis to the date of disease recurrence, death or the last follow-up. Statistical Analysis Statistical analysis was conducted using IBM SPSS Statistics (version 27.0.1, IBM Corp, Armonk, NY, USA) and R version 4.3.1 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org). Categorical variables were presented as frequencies and percentages and compared using Pearson's chi-square test or Fisher's exact test. The Shapiro-Wilk test was utilized to assess whether data follows a normal distribution. Non-normally distributed continuous variables were described using medians and ranges and compared using the Mann-Whitney U test. Survival rates were assessed using the Kaplan-Meier method and the log-rank test. Bonferroni's adjustment was applied in comparisons involving multiple subgroups in the 1:1 analysis. To mitigate bias arising from disparate baseline characteristics, one-to-one propensity score matching (PSM) was performed utilizing the R package "MatchIt" (method=nearest, replace=FALSE). Univariate and multivariate Cox proportional hazards regression analysis (forced enter method) was employed to explore the prognostic factors, with hazard ratios (HR) and 95% confidence intervals (CI) serving as statistical indicators to ascertain independent prognostic factors. The proportional hazards assumption was checked using the Schoenfeld residuals. A two-tailed P value < 0.05 was considered statistically significant. RESULTS Clinicopathological characteristics Based on the aforementioned inclusion and exclusion criteria, a total of 2,051 eligible patients were identified, among which 224 were classified as stage IIB and 227 were classified as stage IIIA (9 th edition of lung cancer TNM staging manual). There were 38 patients in subgroup IIB N2a1, 66 in subgroup IIB N2a2, and 120 in subgroup IIB N0-1. The clinicopathological characteristics of the stage IIB patients are summarized in Table 1. The median age was 63 years (range: 32-86 years), with a predominance of male patients (72.8%). Patients in the IIB N2a2 subgroup exhibited a higher incidence of adenocarcinoma histology ( P <0.001), VPI ( P =0.02), LVI ( P =0.001), and postoperative complications ( P =0.007) compared to the other two subgroups. Additionally, a higher proportion of patients in the IIB N2a2 subgroup received postoperative adjuvant therapy ( P =0.004). Patients in stage IIB N2a2 and IIIA group were matched using PSM method, resulting in 66 well-matched pairs. The clinicopathological characteristics between stage IIB N2a2 and IIIA patients were well balanced after PSM (Table 2). Prognosis analysis Pairwise comparisons of survival among groups IIB N2a1, IIB N2a2, and IIB N0-1 using Kaplan-Meier and Bonferroni correction showed that group IIB N2a2 patients had the worst OS (5-years OS rate: 73.0% vs. 59.5% vs. 62.7%, P =0.078, Figure 2A; 5-years DFS rate: 65.5% vs. 35.5% vs. 56.6%, P =0.007, Figure 2B), while there was no difference in either OS or DFS between stage IIB N2a1 and IIB N0-1 patients (5-years OS rate: P =0.439; 5-years DFS rate: P =0.398). These results were further supported by univariate results (OS HR: IIB N2a1 vs. IIB N2a2 vs. IIB N0-1=1 vs. 1.840 vs.1.236, P =0.082, Figure 2C; DFS HR: IIB N2a1 vs. IIB N2a2 vs. IIB N0-1=1 vs. 2.142 vs.1.272, P =0.009, Figure 2D). Based on the aforementioned findings, we further combined IIB N2a1 and IIB N0-1 into one group (IIB N2a1/0-1), again compared with IIB N2a2, showing a significant difference in both DFS and OS between these two groups (5-year OS rate: 65.2% vs 59.5%, P =0.03, Figure 3A; 5-year DFS rate: 58.8% vs 35.5%, P =0.002, Figure 3B). The results of univariate Cox analysis show that stage IIB classification (IIB N2a1/0-1 vs. IIB N2a2) was a potential prognostic factor for both OS and DFS (OS HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.560, P =0.032, Table S1; DFS HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.780, P =0.003; Table S2). The multivariate Cox analysis further confirmed that stage IIB classification was an independent prognostic factors for both OS and DFS (OS: HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.556, P =0.035, Figure 3C, Table S1; DFS:HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.626, P =0.021; Figure 3D, Table S2). Prognostic comparisons between Stage IIB N2a2 and IIIA patients The survival outcomes of stage IIB N2a2 and stage IIIA patients were compared. Before PSM, there was no significant disparity in survivals between these two groups (5-year OS rate: 59.5% vs. 54.2%, P =0.727, Figure 4A; 5-year DFS: 35.5% vs. 43.0%, P =0.482, Figure 4B). After PSM, the survival rates between the two groups remained statistically similar (5-year OS rate: 59.5% vs. 56.4%, P =0.390, Figure 4C; 5-year DFS: 35.5% vs. 46.2%, P =0.210 Figure 4D). Univariate Cox analysis further indicated no significant differences in OS and DFS between the two groups (OS HR: IIB N2a2 vs. IIIA =1 vs. 0.812, P =0.391, Figure 5A, Table S3; DFS HR: IIB N2a2 vs. IIIA =1 vs. 0.757, P =0.212, Figure 5B, Table S4). DISCUSSION Our research team possesses a wealth of experience in the refinement of lung cancer TNM staging, having actively contributed to the advancement of previous iterations [13, 15-21] . We have previously observed variations in prognosis among NSCLC patients within the same TNM stage [16-19] . In this study, we aimed to assess prognostic differences among NSCLC patients in different stage IIB subgroups based on the 9 th edition TNM staging criteria. Our findings revealed that the prognosis of stage IIB N2a2 (single-station N2 with N1 involvements) patients were significantly inferior to that of other stage IIB patients but comparable to that of stage IIIA patients. Consequently, we proposed that part of stage IIB (N2a2) patients should be maintained as stage IIIA. However, further validation is warranted to corroborate our conclusion. As of now, there has been no research exploring and validating the rationality of downstaging certain patients, such as T1N2a patients, in the 9 th edition TNM staging criteria. Our study specifically focuses on this patient subset and utilizes our center's well-maintained data to validate this new staging adjustment. Following rigorous statistical analyses including multivariate Cox analysis and PSM, our study innovatively proposed that patients with T1N2a1 diseases should indeed be downstaged to stage IIB, consistent with the 9 th edition TNM staging criteria. However, patients with T1N2a2 diseases should remain classified as stage IIIA. Our research holds significant clinical implications, as accurate TNM staging constitutes the fundamental cornerstone for guiding subsequent patient management strategies. Our findings raise the possibility that certain NSCLC patients classified as stage IIB according to the 9 th edition TNM staging criteria may be underestimated. The imprecise staging might ultimately have detrimental effects on their treatment and surveillance. For instance, in clinical practice, when physicians encounter patients with N2a2 involvements, according to the 9 th edition of the TNM classification for Lung Cancer, they classify these patients as stage IIB. Relative to the previous classification as stage IIIA, clinicians might incline toward recommending less aggressive treatment modalities and less intensive surveillance strategies to these patients. However, such approaches may unfavorably impact the prognosis of these individuals. In previous studies, several clinical series have reported the favorable prognosis of skip metastasis (N2a1) [6-9] . Therefore, it is necessary to further investigate the prognosis of patients with skip metastasis and sequential metastasis (N2a2) among those diagnosed with stage IIB (N2a). Our results were consistent with prior studies, demonstrating that patients with N2a1 exhibit markedly superior prognoses compared to those with N2a2. The prognostic outcomes of N2a1 patients resembled those of remaining stage IIB patients, which underscored the legitimacy of downstaging such individuals from stage IIIA to stage IIB. For the observed phenomenon of better prognosis in N2a1 patients compared to poorer prognosis in N2a2 patients, our interpretations were as follows: (1) patients with N2a2 status typically harbored a greater tumor burden, a factor previously associated with poorer prognosis in existing literature [6, 9, 22] ; (2) due to incomplete lymph node dissection or limitations in pathological diagnostic techniques, N2a2 patients may indeed present with multi-station N2 lymph node involvements. However, given our stringent inclusion criteria, which exclusively included patients undergoing systematic lymph node dissection, coupled with the esteemed reputation of our pathology department as one of the premier medical pathology centers in mainland China, the likelihood of this scenario is considered minimal; (3) the occurrence of skip metastasis (N2a1) is attributed to several factors. One of the main reasons is the anatomical connectivity within the lymphatic system [23, 24] . Abundant lymphatic vessels and networks beneath the pleura provide a direct pathway for tumor cells to bypass the intrapulmonary and hilar lymph nodes, draining directly into the ipsilateral mediastinal lymph nodes. In theory, without these direct pathways, tumors may only metastasize to the hilar lymph nodes, failing to extend to the mediastinal lymph nodes. Therefore, N2a1 and N1 patients might have similar prognosis. Our study had several limitations that warrant consideration. Firstly, it was a single-center retrospective study, inherently susceptible to bias. Secondly, the sample size within the stage IIB subgroups was relatively small, potentially limiting the statistical power of our results. Future investigations with larger sample sizes are warranted to address this limitation. Lastly, it remains uncertain whether the conclusions drawn from our exploration of pathological staging in this article are equally applicable to clinical staging. Our database lacks detailed records of clinical lymph node metastasis. Therefore, more detailed data are needed to validate our findings. CONCLUSION In conclusion, our study indicated that the prognosis of stage IIB (N2a2) patients was inferior to that of other stage IIB patients but comparable to that of stage IIIA patients. Thus, we proposed retaining stage IIB (N2a2) classification within the original stage IIIA designation. However, our conclusions warranted further validations. Abbreviations TNM, tumor-node-metastasis NSCLC, non-small cell lung cancer OS, overall survival DFS, disease-free survival PSM, propensity score matching N2a1, single-station N2 without N1 involvement; N2a2, single-station N2 with N1 involvement; BMI, body mass index; ASA, American society of anesthesiologist physical status classification system; VPI, visceral pleural invasion; LVI, lymphovascular invasion; HR, hazard ratio; CI, confidence interval; Declarations Conflict of interest statement: The authors declare no conflict of interest. Funding: This work was supported by Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002); CAMS Innovation Fund for Medical Sciences (CIFMS, 2022-I2M-C&T-B-120) and the National Natural Science Foundation of China (No.92059203) Author contribution statement: (I) Conception and design: Ke-Zhong Chen and Jing-Sheng Cai (II) Administrative support: Ke-Zhong Chen and Yun Li (III) Provision of study materials or patients: Tong Wu and Jing-Sheng Cai (IV) Collection and assembly of data: Tong Wu and Jing-Sheng Cai (V) Data analysis and interpretation: Tong Wu and Jing-Sheng Cai (VI) Manuscript writing: Tong Wu and Jing-Sheng Cai (VII) Final approval of manuscript: All authors Data availability statements The data underlying this article will be shared on reasonable request to the corresponding author. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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Tables Table 1 The baseline characteristics of the included stage IIB NSCLC patients Characteristic IIB N2a1 (n = 38) IIB N2a2 (n = 66) IIB N0-1 (n = 120) P Sex 0.124 Male 26 (68.4%) 43 (65.2%) 94 (78.3%) Female 12 (31.6%) 23 (34.8%) 26 (21.7%) Age, years 0.103 a Median (range) 60.5 (37–86) 61 (37–81) 64.5 (32–80) Smoking 0.083 No 21(55.3%) 33(50.0%) 45(37.5%) Yes 17(44.7%) 33(50.0%) 75(62.5%) Family tumor history 0.110 Without 38 (100%) 60 (90.9%) 107 (89.2%) With 0 (0.0%) 6 (9.1%) 13 (10.8%) Preoperative comorbidity 0.239 Without 14 (36.8%) 23 (34.8%) 56 (46.7%) With 24 (63.2%) 43 (65.2%) 64 (53.3%) BMI 0.328 b =24 24 (63.2%) 35 (53.0%) 53 (44.2%) ASA grade 0.573 b 1 5 (13.2%) 13 (19.7%) 26 (21.7%) 2 30 (78.9%) 52 (78.8%) 87 (72.5%) 3 3 (7.9%) 1 (1.5%) 6 (5.0%) 4 0 (0.0%) 0 (0.0%) 1 (0.8%) Surgical type <0.001 Thoracoscope 32 (84.2%) 58 (87.9%) 73 (60.8%) Thoracotomy 6 (15.8%) 8 (12.1%) 47 (39.2%) Surgical extent 0.146 b Lobectomy 34 (89.5%) 60 (90.9%) 95 (79.2%) Bi-lobectomy 3 (7.9%) 5 (7.6%) 13 (10.8%) Pneumonectomy 1 (2.6%) 1 (1.5%) 12 (10.0%) Histology <0.001 Adenocarcinoma 28 (73.7%) 54 (81.8%) 44 (36.7%) Squamous 9 (23.7%) 8 (12.1%) 64 (53.3%) Other 1 (2.6%) 4 (6.1%) 12 (10.0%) VPI 0.020 Without 30 (78.9%) 34 (51.5%) 77 (64.2%) With 8 (21.1%) 32 (48.5%) 43 (35.8%) LVI 0.001 Without 27 (71.1%) 36 (54.5%) 96 (80.0%) With 11 (28.9%) 30 (45.5%) 24 (20.0%) Postoperative complications 0.007 b Without 35 (92.1%) 60 (90.9%) 119 (99.2%) With 3 (7.9%) 6 (9.1%) 1 (0.8%) Adjuvant therapy 0.004 Not performed 17 (44.7%) 19 (28.8%) 65 (54.2%) Performed 21 (55.3%) 47 (71.2%) 55 (45.8%) a Kruskal-Wallis H test b Fisher's exact test N2a1, single-station N2 without N1 involvement; N2a2, single-station N2 with N1 involvement; BMI, body mass index; ASA, American society of anesthesiologist physical status classification system; VPI, visceral pleural invasion; LVI, lymphovascular invasion Table 2 The baseline characteristics of the stage IIB (N2a2) and stage IIIA NSCLC patients before and after PSM Characteristic Before PSM After PSM IIB N2a2 (n = 66) IIIA (n = 227) P IIB N2a2 (n = 66) IIIA (n = 66) P Sex 0.408 0.854 Male 43 (65.2%) 160 (70.5%) 43 (65.2%) 44 (66.7%) Female 23 (34.8%) 67 (29.5%) 23 (34.8%) 22 (33.3%) Age, years 0.401 a 0.577 a Median (range) 61 (37–81) 61 (34–81) 61 (37–81) 62 (34–81) Smoking 0.240 0.862 No 33 (50.0%) 95 (41.9%) 33 (50.0%) 32 (48.5%) Yes 33 (50.0%) 132 (58.1%) 33 (50.0%) 34 (51.5%) Family tumor history 0.581 0.753 Without 60 (90.9%) 213 (93.8%) 60 (90.9%) 61 (92.4%) With 6 (9.1%) 14 (6.2%) 6 (9.1%) 5 (7.6%) Preoperative comorbidity 0.204 0.157 Without 23 (34.8%) 99 (43.6%) 23 (34.8%) 31 (47.0%) With 43 (65.2%) 128 (56.4%) 43 (65.2%) 35 (53.0%) BMI 0.406 0.501 b =24 35 (53.0%) 100 (44.1%) 35 (53.0%) 29 (43.9%) ASA grade 0.605 0.306 b 1 13 (19.7%) 47 (20.7%) 13 (19.7%) 13 (19.7%) 2 52 (78.8%) 171 (75.3%) 52 (78.8%) 48 (72.7%) 3 1 (1.5%) 9 (4.0%) 1 (1.5%) 5 (7.6%) 4 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Surgical type 0.005 1.000 Thoracoscope 58 (87.9%) 161 (70.9%) 58 (87.9%) 58 (87.9%) Thoracotomy 8 (12.1%) 66 (29.1%) 8 (12.1%) 8 (12.1%) Surgical extent 0.069 0.266 b Lobectomy 60 (90.9%) 192 (84.6%) 60 (90.9%) 60 (90.9%) Bi-lobectomy 5 (7.6%) 12 (5.3%) 5 (7.6%) 2 (3.0%) Pneumonectomy 1 (1.5%) 23 (10.1%) 1 (1.5%) 4 (6.1%) Histology 0.003 0.118 b Adenocarcinoma 54 (81.8%) 138 (60.8%) 54 (81.8%) 49 (74.2%) Squamous 8 (12.1%) 77 (33.9%) 8 (12.1%) 16 (24.2%) Other 4 (6.1%) 12 (5.3%) 4 (6.1%) 1 (1.5%) VPI 0.250 0.159 Without 34 (51.5%) 135 (59.5%) 34 (51.5%) 42 (63.6%) With 32 (48.5%) 92 (40.5%) 32 (48.5%) 24 (36.4%) LVI 0.694 0.164 Without 36 (54.5%) 130 (57.3%) 36 (54.5%) 28 (42.4%) With 30 (45.5%) 97 (42.7%) 30 (45.5%) 38 (57.6%) Postoperative complications 0.968 0.572 No 60 (90.9%) 206 (90.7%) 60 (90.9%) 58 (87.9%) Yes 6 (9.1%) 21 (9.3%) 6 (9.1%) 8 (12.1%) Adjuvant therapy 0.006 0.554 No 19 (28.8%) 109 (48.0%) 19 (28.8%) 16 (24.2%) Yes 47 (71.2%) 118 (52.0%) 47 (71.2%) 50 (75.8%) a Mann-Whitney U test b Fisher's exact test N2a2, single-station N2 with N1 involvement; BMI, body mass index; ASA, American society of anesthesiologist physical status classification system; VPI, visceral pleural invasion; LVI, lymphovascular invasion Additional Declarations No competing interests reported. Supplementary Files Supplements.docx Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Editor assigned by journal 12 Jul, 2024 Submission checks completed at journal 12 Jul, 2024 First submitted to journal 11 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4727507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328093638,"identity":"cb7cc2de-88a1-40ab-992b-490424a38369","order_by":0,"name":"Tong Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBAC+/sHGx8kGPyTY2NvIFbPDeZmgwcVB4z5eA4QrYW9TfLBmQOJ8yQSiNTBOLuxQSKx7U5im+TjjTcYamyiCWphljnYYJDY9sy4TTqt2ILhWFpuAyEtbAyJDQmJbcyybdI5ZhKMDYcJa+EBajkA1MLYJnmGSC0SEomNDQlnDiu2SfAQqcWA52AzQ0JFmjEbD9AvCcT4xYC9/fnPHwY2cvLthzfe+FBjQ1gLinaiowZJC6k6RsEoGAWjYGQAAPukQytWiYJSAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tong","middleName":"","lastName":"Wu","suffix":""},{"id":328093642,"identity":"102e8633-6dd6-47d4-b24e-290255fbe4f6","order_by":1,"name":"Jingsheng Cai","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingsheng","middleName":"","lastName":"Cai","suffix":""},{"id":328093644,"identity":"ca0a6498-5ee9-4015-b91c-01226237595e","order_by":2,"name":"Yun Li","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Li","suffix":""},{"id":328093647,"identity":"397d37c9-162e-4e10-9bde-cae62536a597","order_by":3,"name":"Kezhong Chen","email":"","orcid":"","institution":"Peking University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kezhong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-12 02:56:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4727507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4727507/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-024-13364-6","type":"published","date":"2025-03-12T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62219494,"identity":"3e444550-9de1-4c4e-8f29-5b740142ae6e","added_by":"auto","created_at":"2024-08-11 12:14:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":391086,"visible":true,"origin":"","legend":"\u003cp\u003eThe patient selection flow chart. NSCLC, non-small cell lung cancer; TNM, tumor-node-metastasis classification system; AIS, adenocarcinoma in situ.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/0d59a40c37e0932a4cbcfff8.png"},{"id":62218813,"identity":"a06a2eee-1a69-4f89-909e-38b8ee6b5700","added_by":"auto","created_at":"2024-08-11 12:06:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197982,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimates of survivals and univariate Cox analysis of stage IIB patients (N2a1 vs. N2a2 vs. N0-1). (A) overall survival curves; (B) disease-free survival curves; (C) forest plot: univariate Cox analysis of overall survival; and (D) forest plot: univariate Cox analysis of disease-free survival. N2a1, single-station N2 without N1 involvement; N2a2, single-station N2 with N1 involvement; HR: hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/2921f2290c305bd3c5623e38.png"},{"id":62218816,"identity":"0c8ec089-9de7-4ef5-8040-c2569b56203e","added_by":"auto","created_at":"2024-08-11 12:06:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154720,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimates of survivals and multivariate Cox analysis of stage IIB patients (N2a1/0-1 vs. N2a2). (A) overall survival curves; (B) disease-free survival curves; (C) forest plot: multivariate Cox analysis of overall survival; and (D) forest plot: multivariate Cox analysis of disease-free survival. N2a1, single-station N2 without N1 involvement; N2a2, single-station N2 with N1 involvement; HR: hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/b6e1c35d5436af06cde29714.png"},{"id":62218819,"identity":"cd1bdd7b-9fde-488f-9116-8f32920c4811","added_by":"auto","created_at":"2024-08-11 12:06:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":469798,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimates of survivals differences between stage IIB N2a2 and IIIA patients both before PSM and after PSM. (A) overall survival before PSM; (B) disease-free survival before PSM; (C) overall survival after PSM; and (D) disease-free survival after PSM. N2a2, single-station N2 with N1 involvement; PSM, propensity score matching.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/4989f67fca405dc03b090896.png"},{"id":62218818,"identity":"b0a5f666-9b2d-4fe4-b678-bd4acadaca94","added_by":"auto","created_at":"2024-08-11 12:06:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256199,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate Cox analysis of the IIB N2a2 and IIIA NSCLC patients after PSM. (A) overall survival; (B) disease-free survival. N2a2, single-station N2 with N1 involvement; PSM, propensity score matching; HR: hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/f5906b9f3aa7079c6c0296c2.png"},{"id":78689688,"identity":"a574ab4c-98bb-4636-969c-ac4eb8544059","added_by":"auto","created_at":"2025-03-17 16:12:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2424100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/0e4e39be-1b67-4c46-ac96-ac13f99a0841.pdf"},{"id":62218817,"identity":"1870f661-743d-4420-bea8-3969736a2942","added_by":"auto","created_at":"2024-08-11 12:06:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":58978,"visible":true,"origin":"","legend":"","description":"","filename":"Supplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-4727507/v1/e0b627df29a4ecf2002bb466.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation for revision of the stage IIIA(T1N2) in the forthcoming ninth edition of the TNM classification for lung cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer is the most malignant tumor with the highest incidence and mortality in the world\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Non-small cell lung cancer (NSCLC) constitutes approximately 85% of cases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The lung cancer tumor-node-metastasis (TNM) staging manual, proposed by the International Association for the Study of Lung Cancer, serves as the cornerstone for the standardized treatment of patients with NSCLC\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn 2024, the 9th edition of TNM staging manual for lung cancer\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e was released, maintaining the existing T staging without alterations. However, notable updates were made to N staging, introducing N2 sub-categories (N2a: single-station N2 metastasis; N2b: multi-station N2 metastasis)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Additionally, the M staging was further refined, with M1c now subdivided into M1c1 (multiple extra-thoracic metastases in one organ) and M1c2 (multiple extra-thoracic metastases in multiple organs)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. From the TNM staging standpoint, T1N1, formerly assigned to stage IIB, has been reclassified to stage IIA. Conversely, T1N2, previously designated as stage IIIA, has been subdivided into T1N2a (stage IIB) and T1N2b (stage IIIA)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe revisions introduced in the latest staging edition necessitate external validation to confirm their accuracy and generalizability. Within this manuscript, we direct our attention to a specific subgroup of patients transitioning from stage IIIA to IIB (T1N2a). Previous studies suggested that skip N2 metastasis is associated with improved survivals in N2 lung cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, we further separated stage IIB patients into three subgroups: stage IIB N2a1 (single-station N2 without N1 involvement), stage IIB N2a2 (single-station N2 with N1 involvements) and stage IIB N0-1. In this study, we set out to investigate potential prognostic differences among stage IIB NSCLC patients and further externally validate the stage IIB classification in the 9th edition of lung cancer TNM staging manual.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Peking University People\u0026apos;s Hospital. Given the anonymized patient data and retrospective design, written informed consent was not required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical records of 7,931 patients diagnosed with pulmonary malignant tumors and admitted to the Department of Thoracic Surgery at Peking University People\u0026apos;s Hospital between 1999 and 2018 (PKUPHTOI dataset) were systematically reviewed. This well-managed dataset has been used before\u003csup\u003e[10-13]\u003c/sup\u003e. Inclusion criteria were as follows: (1) Diagnosis of NSCLC; (2) Underwent surgical resection; (3) stages IIB and IIIA (the 9th edition of lung cancer TNM staging manual). Exclusion criteria comprised: (1) Age \u0026lt; 18 years; (2) Presence of N3 disease; (3) Presence of M1 disease; (4) R1/R2 resection; (5) Sublobar resection; (6) Non-systematic lymph node dissection; (7) Primary lung cancer; (8) Receipt of neoadjuvant therapy; (9) History of previous malignancies; (10) Unavailability of clinicopathological data. A total of 2,051 eligible patients were finally included, consisting of 224 individuals with stage IIB and 227 with stage IIIA disease. Figure 1 illustrates the patient selection process. Based on the lymph node metastasis status, the stage IIB patients were further divided into three categories: IIB N2a1, IIB N2a2, and IIB N0-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinicopathological dataset encompassed various parameters, including age, gender (male/female), smoking history (yes/no), family tumor history (yes/no), preoperative comorbidities (yes/no), body mass index (BMI), American Society of Anesthesiologist (ASA) classification (grades 1/2/3/4), surgical approach (thoracoscopy/thoracotomy), extent of surgical resection (lobectomy, bilobectomy, and pneumonectomy), and postoperative complications. Additionally, histopathological characteristics such as histology (adenocarcinoma, squamous cell carcinoma and other), along with factors like visceral pleural invasion (VPI) (yes/no), lymphovascular invasion (LVI) (yes/no), occurrence of postoperative complications (yes/no), and receipt of postoperative adjuvant therapy (yes/no). Patient outcomes, including mortality and time to recurrence, were also recorded. Complete data analysis was performed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgical procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the absolute accuracy of N staging, we exclusively included patients who underwent radical surgical resections and systemic mediastinal lymph node dissection. The standard protocol of surgery was similar to the one previously described by Xu et.al\u003csup\u003e[14]\u003c/sup\u003e. Systemic lymphadenectomy was defined as mediastinal lymph node dissection of at least 3 stations, including station 7 (the subcarinal lymph node), from station 4L, 5, 6, 7, 8 and 9 for the left-side NSCLCs and station 2R, 3A, 4R, 7, 8 and 9 for the right-side NSCLCs. In addition, at least 6 lymph nodes were harvested. As for N1 station lymph nodes, in routine, the station 10, 11 and 12 were dissected intraoperatively. The station 13 and 14 lymph nodes were dissected by resident doctors from the excised specimen, but this procedure was not mandatory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe routine follow-up strategies for patients in our center have been previously reported\u003csup\u003e[12]\u003c/sup\u003e. Specifically，follow-up data were acquired through comprehensive review of medical records, direct patient consultations, and telephonic interviews. Our center adheres to a rigorous postoperative follow-up protocol, involving assessments every three months during the initial two years post-surgery, biannually for the subsequent three to five years, and annually thereafter. Each follow-up session includes thorough physical examinations, monitoring of serum tumor markers, and chest computed tomography scans. Additional diagnostic modalities such as brain magnetic resonance imaging and bone scans are conducted as warranted by clinical indications. The primary endpoints of this study were overall survival (OS) and disease-free survival (DFS). OS was defined as the period from the date of diagnosis to all-cause death or the date of last follow-up. DFS was defined as the period from the date of diagnosis to the date of disease recurrence, death or the last follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using IBM SPSS Statistics (version 27.0.1, IBM Corp, Armonk, NY, USA) and R version 4.3.1 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org). Categorical variables were presented as frequencies and percentages and compared using Pearson\u0026apos;s chi-square test or Fisher\u0026apos;s exact test. The Shapiro-Wilk test was utilized to assess whether data follows a normal distribution. Non-normally distributed continuous variables were described using medians and ranges and compared using the Mann-Whitney U test. Survival rates were assessed using the Kaplan-Meier method and the log-rank test. Bonferroni\u0026apos;s adjustment was applied in comparisons involving multiple subgroups in the 1:1 analysis. To mitigate bias arising from disparate baseline characteristics, one-to-one propensity score matching (PSM) was performed utilizing the R package \u0026quot;MatchIt\u0026quot; (method=nearest, replace=FALSE). Univariate and multivariate Cox proportional hazards regression analysis (forced enter method) was employed to explore the prognostic factors, with hazard ratios (HR) and 95% confidence intervals (CI) serving as statistical indicators to ascertain independent prognostic factors. The proportional hazards assumption was checked using the Schoenfeld residuals. A two-tailed P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eClinicopathological characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned inclusion and exclusion criteria, a total of 2,051 eligible patients were identified, among which 224 were classified as stage IIB and 227 were classified as stage IIIA (9\u003csup\u003eth\u003c/sup\u003e edition of lung cancer TNM staging manual). There were 38 patients in subgroup IIB N2a1, 66 in subgroup IIB N2a2, and 120 in subgroup IIB N0-1. The clinicopathological characteristics of the stage IIB patients are summarized in Table 1. The median age was 63 years (range: 32-86 years), with a predominance of male patients (72.8%). Patients in the IIB N2a2 subgroup exhibited a higher incidence of adenocarcinoma histology (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), VPI (\u003cem\u003eP\u003c/em\u003e=0.02), LVI (\u003cem\u003eP\u003c/em\u003e=0.001), and postoperative complications (\u003cem\u003eP\u003c/em\u003e=0.007) compared to the other two subgroups. Additionally, a higher proportion of patients in the IIB N2a2 subgroup received postoperative adjuvant therapy (\u003cem\u003eP\u003c/em\u003e=0.004).\u003c/p\u003e\n\u003cp\u003ePatients in stage IIB N2a2 and IIIA group were matched using PSM method, resulting in 66 well-matched pairs. The clinicopathological characteristics between stage IIB N2a2 and IIIA patients were well balanced after PSM (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognosis analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise comparisons of survival among groups IIB N2a1, IIB N2a2, and IIB N0-1 using Kaplan-Meier and Bonferroni correction showed that group IIB N2a2 patients had the worst OS (5-years OS rate: 73.0% vs. 59.5% vs. 62.7%, \u003cem\u003eP\u003c/em\u003e=0.078, Figure 2A; 5-years DFS rate: 65.5% vs. 35.5% vs. 56.6%, \u003cem\u003eP\u003c/em\u003e=0.007, Figure 2B), while there was no difference in either OS or DFS between stage IIB N2a1 and IIB N0-1 patients (5-years OS rate: \u003cem\u003eP\u003c/em\u003e=0.439; 5-years DFS rate: \u003cem\u003eP\u003c/em\u003e=0.398). These results were further supported by univariate results (OS HR: IIB N2a1 vs. IIB N2a2 vs. IIB N0-1=1 vs. 1.840 vs.1.236, \u003cem\u003eP\u003c/em\u003e=0.082, Figure 2C; DFS HR: IIB N2a1 vs. IIB N2a2 vs. IIB N0-1=1 vs. 2.142 vs.1.272, \u003cem\u003eP\u003c/em\u003e=0.009, Figure 2D).\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned findings, we further combined IIB N2a1 and IIB N0-1 into one group (IIB N2a1/0-1), again compared with IIB N2a2, showing a significant difference in both DFS and OS between these two groups (5-year OS rate: 65.2% vs 59.5%, \u003cem\u003eP\u003c/em\u003e=0.03, Figure 3A; 5-year DFS rate: 58.8% vs 35.5%,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.002, Figure 3B). The results of univariate Cox analysis show that stage IIB classification (IIB N2a1/0-1 \u003cem\u003evs.\u003c/em\u003e IIB N2a2) was a potential prognostic factor for both OS and DFS (OS HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.560, \u003cem\u003eP\u003c/em\u003e=0.032, Table S1; DFS HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.780, \u003cem\u003eP\u003c/em\u003e=0.003; Table S2). The multivariate Cox analysis further confirmed that stage IIB classification was an independent prognostic factors for both OS and DFS (OS: HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.556, \u003cem\u003eP\u003c/em\u003e=0.035, Figure 3C, Table S1; DFS:HR: IIB N2a1/0-1 vs. IIB N2a2 =1 vs. 1.626, \u003cem\u003eP\u003c/em\u003e=0.021; Figure 3D, Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic comparisons between Stage IIB N2a2 and IIIA patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survival outcomes of stage IIB N2a2 and stage IIIA patients were compared. Before PSM, there was no significant disparity in survivals between these two groups (5-year OS rate: 59.5% vs. 54.2%, \u003cem\u003eP\u003c/em\u003e=0.727, Figure 4A; 5-year DFS: 35.5% vs. 43.0%, \u003cem\u003eP\u003c/em\u003e=0.482, Figure 4B). After PSM, the survival rates between the two groups remained statistically similar (5-year OS rate: 59.5% vs. 56.4%, \u003cem\u003eP\u003c/em\u003e=0.390, Figure 4C; 5-year DFS: 35.5% vs. 46.2%, \u003cem\u003eP\u003c/em\u003e=0.210 Figure 4D). Univariate Cox analysis further indicated no significant differences in OS and DFS between the two groups (OS HR: IIB N2a2 vs. IIIA =1 vs. 0.812, \u003cem\u003eP\u003c/em\u003e=0.391, Figure 5A, Table S3; DFS HR: IIB N2a2 vs. IIIA =1 vs. 0.757, \u003cem\u003eP\u003c/em\u003e=0.212, Figure 5B, Table S4).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur research team possesses a wealth of experience in the refinement of lung cancer TNM staging, having actively contributed to the advancement of previous iterations\u003csup\u003e[13, 15-21]\u003c/sup\u003e. We have previously observed variations in prognosis among NSCLC patients within the same TNM stage\u003csup\u003e[16-19]\u003c/sup\u003e. In this study, we aimed to assess prognostic differences among NSCLC patients in different stage IIB subgroups based on the 9\u003csup\u003eth\u003c/sup\u003e edition TNM staging criteria. Our findings revealed that the prognosis of stage IIB N2a2 (single-station N2 with N1 involvements) patients were significantly inferior to that of other stage IIB patients but comparable to that of stage IIIA patients. Consequently, we proposed that part of stage IIB (N2a2) patients should be maintained as stage IIIA. However, further validation is warranted to corroborate our conclusion.\u003c/p\u003e\n\u003cp\u003eAs of now, there has been no research exploring and validating the rationality of downstaging certain patients, such as T1N2a patients, in the 9\u003csup\u003eth\u003c/sup\u003e edition TNM staging criteria. Our study specifically focuses on this patient subset and utilizes our center\u0026apos;s well-maintained data to validate this new staging adjustment. Following rigorous statistical analyses including multivariate Cox analysis and PSM, our study innovatively proposed that patients with T1N2a1 diseases should indeed be downstaged to stage IIB, consistent with the 9\u003csup\u003eth\u003c/sup\u003e edition TNM staging criteria. However, patients with T1N2a2 diseases should remain classified as stage IIIA. Our research holds significant clinical implications, as accurate TNM staging constitutes the fundamental cornerstone for guiding subsequent patient management strategies. Our findings raise the possibility that certain NSCLC patients classified as stage IIB according to the 9\u003csup\u003eth\u003c/sup\u003e edition TNM staging criteria may be underestimated. The imprecise staging might ultimately have detrimental effects on their treatment and surveillance. For instance, in clinical practice, when physicians encounter patients with N2a2 involvements, according to the 9\u003csup\u003eth\u003c/sup\u003e edition of the TNM classification for Lung Cancer, they classify these patients as stage IIB. Relative to the previous classification as stage IIIA, clinicians might incline toward recommending less aggressive treatment modalities and less intensive surveillance strategies to these patients. However, such approaches may unfavorably impact the prognosis of these individuals.\u003c/p\u003e\n\u003cp\u003eIn previous studies, several clinical series have reported the favorable prognosis of skip metastasis (N2a1)\u003csup\u003e[6-9]\u003c/sup\u003e. Therefore, it is necessary to further investigate the prognosis of patients with skip metastasis and sequential metastasis (N2a2) among those diagnosed with stage IIB (N2a). Our results were consistent with prior studies, demonstrating that patients with N2a1 exhibit markedly superior prognoses compared to those with N2a2. The prognostic outcomes of N2a1 patients resembled those of remaining stage IIB patients, which underscored the legitimacy of downstaging such individuals from stage IIIA to stage IIB. For the observed phenomenon of better prognosis in N2a1 patients compared to poorer prognosis in N2a2 patients, our interpretations were as follows: (1) patients with N2a2 status typically harbored a greater tumor burden, a factor previously associated with poorer prognosis in existing literature\u003csup\u003e[6, 9, 22]\u003c/sup\u003e; (2) due to incomplete lymph node dissection or limitations in pathological diagnostic techniques, N2a2 patients may indeed present with multi-station N2 lymph node involvements. However, given our stringent inclusion criteria, which exclusively included patients undergoing systematic lymph node dissection, coupled with the esteemed reputation of our pathology department as one of the premier medical pathology centers in mainland China, the likelihood of this scenario is considered minimal; (3) the occurrence of skip metastasis (N2a1) is attributed to several factors. One of the main reasons is the anatomical connectivity within the lymphatic system\u003csup\u003e[23, 24]\u003c/sup\u003e. Abundant lymphatic vessels and networks beneath the pleura provide a direct pathway for tumor cells to bypass the intrapulmonary and hilar lymph nodes, draining directly into the ipsilateral mediastinal lymph nodes. In theory, without these direct pathways, tumors may only metastasize to the hilar lymph nodes, failing to extend to the mediastinal lymph nodes. Therefore, N2a1 and N1 patients might have similar prognosis.\u003c/p\u003e\n\u003cp\u003eOur study had several limitations that warrant consideration. Firstly, it was a single-center retrospective study, inherently susceptible to bias. Secondly, the sample size within the stage IIB subgroups was relatively small, potentially limiting the statistical power of our results. Future investigations with larger sample sizes are warranted to address this limitation. Lastly, it remains uncertain whether the conclusions drawn from our exploration of pathological staging in this article are equally applicable to clinical staging. Our database lacks detailed records of clinical lymph node metastasis. Therefore, more detailed data are needed to validate our findings.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, our study indicated that the prognosis of stage IIB (N2a2) patients was inferior to that of other stage IIB patients but comparable to that of stage IIIA patients. Thus, we proposed retaining stage IIB (N2a2) classification within the original stage IIIA designation. However, our conclusions warranted further validations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTNM, tumor-node-metastasis\u003c/p\u003e\n\u003cp\u003eNSCLC, non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eOS, overall survival\u003c/p\u003e\n\u003cp\u003eDFS, disease-free survival\u003c/p\u003e\n\u003cp\u003ePSM, propensity score matching\u003c/p\u003e\n\u003cp\u003eN2a1, single-station N2 without N1 involvement;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN2a2, single-station N2 with N1 involvement;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI, body mass index;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eASA, American society of anesthesiologist physical status classification system;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVPI, visceral pleural invasion;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLVI, lymphovascular invasion;\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio;\u003c/p\u003e\n\u003cp\u003eCI, confidence interval;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002); CAMS Innovation Fund for Medical Sciences (CIFMS, 2022-I2M-C\u0026amp;T-B-120) and the National Natural Science Foundation of China (No.92059203)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) Conception and design: Ke-Zhong Chen and Jing-Sheng Cai\u003c/p\u003e\n\u003cp\u003e(II) Administrative support: Ke-Zhong Chen and Yun Li\u003c/p\u003e\n\u003cp\u003e(III) Provision of study materials or patients: Tong Wu and Jing-Sheng Cai\u003c/p\u003e\n\u003cp\u003e(IV) Collection and assembly of data: Tong Wu and Jing-Sheng Cai\u003c/p\u003e\n\u003cp\u003e(V) Data analysis and interpretation: Tong Wu and Jing-Sheng Cai\u003c/p\u003e\n\u003cp\u003e(VI) Manuscript writing: Tong Wu and Jing-Sheng Cai\u003c/p\u003e\n\u003cp\u003e(VII) Final approval of manuscript: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholson AG, Tsao MS, Beasley MB, Borczuk AC, Brambilla E, Cooper WA, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol. 2022;17(3):362\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRami-Porta R, Nishimura KK, Giroux DJ, Detterbeck F, Cardillo G, Edwards JG et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. 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Chest. 2021;159(6):2458\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang BW, Wang WQ, Cai JS, Zhang SW. Investigations of the distant metastatic non-small cell lung cancer without local lymph node involvement: Real world data from a large database. Clin Respir J. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai JS, Yang F, Wang X. Reconsidering the T category for the T3 non-small cell lung cancer with additional tumor nodules in the same lobe: A population-based study. Front Oncol. 2023;13:1043386.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai JS, Wang X. Investigation of Early-Stage Non-Small Cell Lung Cancer Patients with Different T2 Descriptors: Real Word Data From a Large Database. Lung. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai J-S, Li Y, Yang F, Wang X. 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Semin Thorac Cardiovasc Surg; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyawaki T, Kenmotsu H, Doshita K, Kodama H, Nishioka N, Iida Y, et al. Clinical impact of tumour burden on the efficacy of PD-1/PD-L1 inhibitors plus chemotherapy in non-small-cell lung cancer. Cancer Med. 2023;12(2):1451\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiquet M, Hidden G, Debesse B. Direct Lymphatic Drainage of Lung Segments to the Mediastinal Nodes - an Anatomic Study on 260 Adults. J Thorac Cardiov Sur. 1989;97(4):623\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImai K, Minamiya Y, Saito H, Nakagawa T, Ito M, Ono T, et al. Detection of pleural lymph flow using indocyanine green fluorescence imaging in non-small cell lung cancer surgery: a preliminary study. Surg Today. 2013;43(3):249\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eThe baseline characteristics of the included stage IIB NSCLC patients\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharacteristic\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIB N2a1 (n\u0026thinsp;=\u0026thinsp;38)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIB N2a2 (n\u0026thinsp;=\u0026thinsp;66)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIB N0-1 (n\u0026thinsp;=\u0026thinsp;120)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSex\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.124\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e26 (68.4%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e94 (78.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (31.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e26 (21.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge, years\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.103\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian (range)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.5 (37\u0026ndash;86)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e61 (37\u0026ndash;81)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e64.5 (32\u0026ndash;80)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmoking\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.083\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e21(55.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e33(50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e45(37.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e17(44.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e33(50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e75(62.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFamily tumor history\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.110\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e38 (100%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e107 (89.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (10.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePreoperative comorbidity\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.239\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e14 (36.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e56 (46.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e24 (63.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e64 (53.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.328\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;18.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (2.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (3.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (4.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.5\u0026ndash;24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (34.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e29 (43.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e62 (51.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;=24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e24 (63.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e35 (53.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e53 (44.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eASA grade\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.573\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (13.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (19.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e26 (21.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 (78.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e52 (78.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e87 (72.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 (7.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (5.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (0.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSurgical type\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThoracoscope\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (84.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e58 (87.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e73 (60.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThoracotomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (15.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e47 (39.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSurgical extent\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.146\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLobectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (89.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e95 (79.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBi-lobectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 (7.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (7.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (10.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePneumonectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (2.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (10.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHistology\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdenocarcinoma\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e28 (73.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e54 (81.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e44 (36.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSquamous\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9 (23.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e64 (53.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (2.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4 (6.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (10.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVPI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.020\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 (78.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (51.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e77 (64.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (21.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (48.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (35.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLVI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e27 (71.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e36 (54.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e96 (80.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 (28.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 (45.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e24 (20.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePostoperative complications\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.007\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e35 (92.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e119 (99.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 (7.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (0.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjuvant therapy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.004\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot performed\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e17 (44.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e19 (28.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e65 (54.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePerformed\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e21 (55.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e47 (71.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e55 (45.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea Kruskal-Wallis H test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eb Fisher's exact test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eN2a1, single-station N2 without N1 involvement; N2a2, single-station N2 with N1 involvement; BMI, body mass index; ASA, American society of anesthesiologist physical status classification system; VPI, visceral pleural invasion; LVI, lymphovascular invasion\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eThe baseline characteristics of the stage IIB (N2a2) and stage IIIA NSCLC patients before and after PSM\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharacteristic\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBefore PSM\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eAfter PSM\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIB N2a2 (n\u0026thinsp;=\u0026thinsp;66)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIIA (n\u0026thinsp;=\u0026thinsp;227)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIB N2a2 (n\u0026thinsp;=\u0026thinsp;66)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eIIIA (n\u0026thinsp;=\u0026thinsp;66)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSex\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.408\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.854\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e160 (70.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e44 (66.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e67 (29.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e22 (33.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge, years\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.401\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.577\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian (range)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e61 (37\u0026ndash;81)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e61 (34\u0026ndash;81)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e61 (37\u0026ndash;81)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e62 (34\u0026ndash;81)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmoking\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.240\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.862\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e95 (41.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (48.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e132 (58.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (51.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFamily tumor history\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.581\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.753\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e213 (93.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e61 (92.4%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e14 (6.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (7.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePreoperative comorbidity\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.204\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.157\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e99 (43.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (34.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e31 (47.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e128 (56.4%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e43 (65.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e35 (53.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.406\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.501\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;18.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (3.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 (4.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (3.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e4 (6.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.5\u0026ndash;24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e29 (43.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e116 (51.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e29 (43.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (50.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026gt;=24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e35 (53.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e100 (44.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e35 (53.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e29 (43.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eASA grade\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.605\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.306\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (19.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e47 (20.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (19.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e13 (19.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e52 (78.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e171 (75.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e52 (78.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e48 (72.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9 (4.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (7.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0 (0.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSurgical type\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.005\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThoracoscope\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e58 (87.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e161 (70.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e58 (87.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e58 (87.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThoracotomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e66 (29.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSurgical extent\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.069\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.266\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLobectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e192 (84.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBi-lobectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (7.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (5.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 (7.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2 (3.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePneumonectomy\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e23 (10.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e4 (6.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHistology\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.003\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.118\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdenocarcinoma\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e54 (81.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e138 (60.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e54 (81.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e49 (74.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSquamous\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e77 (33.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e16 (24.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4 (6.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (5.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4 (6.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 (1.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVPI\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.250\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.159\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (51.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e135 (59.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (51.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e42 (63.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (48.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e92 (40.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e32 (48.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e24 (36.4%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLVI\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.694\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.164\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWithout\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e36 (54.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e130 (57.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e36 (54.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e28 (42.4%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWith\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 (45.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e97 (42.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e30 (45.5%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e38 (57.6%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePostoperative complications\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.968\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.572\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e206 (90.7%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e60 (90.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e58 (87.9%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e21 (9.3%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 (9.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e8 (12.1%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjuvant therapy\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003e0.006\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.554\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e19 (28.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e109 (48.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e19 (28.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e16 (24.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e47 (71.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e118 (52.0%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e47 (71.2%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e50 (75.8%)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ea Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eb Fisher's exact test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eN2a2, single-station N2 with N1 involvement; BMI, body mass index; ASA, American society of anesthesiologist physical status classification system; VPI, visceral pleural invasion; LVI, lymphovascular invasion\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"non-small cell lung cancer, stage IIB, stage IIIA, prognosis, The 9th edition of the lung cancer TNM staging system","lastPublishedDoi":"10.21203/rs.3.rs-4727507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4727507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe 9th edition of the lung cancer tumor-node-metastasis (TNM) staging system downgrades certain non-small cell lung cancer (NSCLC) patients from stage IIIA (T1N2) to IIB. This study aimed to externally validate this stage adjustment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eConsecutive resected stage IIB and IIIA NSCLC patients were included. Subgrouping was done based on lymph node involvements: IIB N2a1 (single-station N2 without N1 involvement), IIB N2a2 (single-station N2 with N1 involvements) and IIB N0-1. Overall survival (OS) and disease-free survival (DFS) were compared using the Kaplan-Meier method, with propensity score matching (PSM) employed to mitigate potential biases. COX regression models were utilized to assess prognostic differences.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e224 stage IIB and 227 stage IIIA cases was included. There were 38, 66 and 120 patients in the IIB N2a1, IIB N2a2 and IIB N0-1 subgroups, respectively. Univariate COX analysis indicated comparable prognoses between the stage IIB N0-1 and IIB N2a1 patients, whereas stage IIB N2a2 patients exhibited poorer outcomes. Upon combining the stage IIB N2a1 and IIB N0-1 subgroups, multivariate COX analysis demonstrated a significantly worse prognosis for stage IIB N2a2 patients compared to those with stage IIB N2a1/0\u0026ndash;1 tumors (OS, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; DFS, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Further comparisons between stage IIB N2a2 and IIIA patients, following PSM analysis, indicated similar survivals (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.390; DFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.210).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prognosis of stage IIB N2a2 patients was worse than that of remaining stage IIB patients but comparable to that of stage IIIA patients. We proposed that stage IIB N2a2 patients should be maintained as stage IIIA.\u003c/p\u003e","manuscriptTitle":"Validation for revision of the stage IIIA(T1N2) in the forthcoming ninth edition of the TNM classification for lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:06:31","doi":"10.21203/rs.3.rs-4727507/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-17T08:37:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-12T11:04:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T11:02:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-07-12T02:54:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47fb40da-18cc-4d21-91ad-52f9feff19ff","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:08:56+00:00","versionOfRecord":{"articleIdentity":"rs-4727507","link":"https://doi.org/10.1186/s12885-024-13364-6","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-03-12 15:58:43","publishedOnDateReadable":"March 12th, 2025"},"versionCreatedAt":"2024-08-11 12:06:31","video":"","vorDoi":"10.1186/s12885-024-13364-6","vorDoiUrl":"https://doi.org/10.1186/s12885-024-13364-6","workflowStages":[]},"version":"v1","identity":"rs-4727507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4727507","identity":"rs-4727507","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}