Clinical features and prognostic factors of IV combined small cell lung cancer: A Propensity Score Matching Analysis

Clinical features and prognostic factors of IV combined small cell lung cancer: A Propensity Score Matching Analysis | 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 Article Clinical features and prognostic factors of IV combined small cell lung cancer: A Propensity Score Matching Analysis shanshan cai, Weichang Yang, Zhouhua Li, Xiaotian Huang, Jinbo Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3939208/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Nowadays, the characteristics and treatment of combined small-cell lung carcinoma (CSCLC) remain controversial. This study aimed to analyze the feature of clinical characteristics, survival outcomes and treatment modalities among IV CSCLC, IV SCLC and IV NSCLC, to provide more evidence for the study of IV CSCLC Methods All CSCLC, SCLC and NSCLC patient data were obtained from the SEER database (2010–2020). Pearson's χ2 test was used to compare the differences in clinical characteristics. Propensity score matching (PSM) was utilized to balance the bias of the variables between patients. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify prognostic factors. KM analysis was used to calculate survival. Results A total of 493 patients with IV CSCLC, 35503 patients with SCLC, 122807 patients with IV NSCLC were included in this study. The demographic characteristics and tumor characteristics of three groups were different. Before PSM, there were significant difference in OS and CSS among IV CSCLC, IV SCLC and IV NSCLC, After PSM, there was significant difference in OS and CSS between the IV CSCLC and IV NSCLC. Risk/protective factors for OS and CSS were different in three groups. Chemotherapy, radiotherapy, surgery all can improve survival time of IV CSCLC. Chemotherapy combine surgery can significantly improve OS and CSS in patients with IV CSCLC (10.0 months and 16.0 months), chemotherapy alone was also a good choice for some IV CSCLC patients who have already lost the opportunity for surgery at the time of first diagnosis. Conclusions These results indicated that the prognosis and clinical characteristics IV CSCLC, IV SCLC and IV NSCLC were significant difference. Surgery combined chemotherapy was the best treatment in patients with IV CSCLC and chemotherapy alone was a good choice for patients who have lost the indication of surgery. Biological sciences/Cancer/Lung cancer/Non small cell lung cancer Biological sciences/Cancer/Lung cancer/Small cell lung cancer Biological sciences/Cancer/Cancer therapy combined small cell lung cancer small cell lung cancer non-small cell lung cancer advanced propensity score matching analysis SEER database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Lung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide, with an estimated 2 million new cases and 1·76 million deaths per year 1 . Combined small-cell lung carcinoma (CSCLC) is a rare tissue type of lung cancer, the initial definition of CSCLC dates back to 1999 2 , the histological classification of the World Health Organization (WHO) classifies SCLC into SCLC and Combined SCLC (CSCLC), which accounts for about 5–20% of total SCLC cases 3,4 . CSCLC is a relatively rare subtype of SCLC, which refers to a subtype that has both SCLC histology and any subtype of non-small cell lung cancer (NSCLC) 5 . CSCLC is closely related to genotypic and immunophenotypic of NSCLC and SCLC. Epidermal growth factor receptor (EGFR) mutations are present in NSCLC and related to tumor response to EGFR tyrosine kinase inhibitors (TKIs), indicating that EGFR constitutes a potential biomarker. Previous studies reported that EGFR mutations occur in less than 5% of SCLC cases, while a rate reaching 15–20% can be found in CSCLC 6–8 .Wagner et al 9 first assessed 7 CSCLC cases for genotypic and immunophenotypic associations determining whether NSCLC constituents displayed features specific to SCLC. In this study, several biomarkers were utilized, further indicating that a common clonal precursor with closer relationship with SCLC than NSCLC exists, CSCLCs are closer to SCLC than NSCLC. It is very meaningful to compare prognosis and clinical features due to the closely association of genotypes and immunophenotypes. However, because of the rarity of the CSCLC, little research has been done on the prognosis and treatment of patients with subtypes of CSCLC and comparison with SCLC and NSCLC, especially IV stage. Only several studies compared survival and prognosis of CSCLC and SCLC 10–13 , but conflicting results have been reported by different studies. As for the study about comparison of CSCLC and NSCLC is even rarer, and no relevant studies on the IV stage have been conducted so far. Most cases of CSCLC belonged to advanced stages when they were firstly diagnosed. Zhang et al. reported almost 90% of CSCLC were diagnosed as stage III and IV in their cohort 14 . Thus, the study of IV CSCLC is of great clinical interest The Surveillance, Epidemiology, and End Results (SEER) database is unique in the number of cases, especially for IV CSCLC patients. In this study, we obtained stage IV CSCLC, IV SCLC and IV NSCLC data from SEER database and performed 1:1 propensity score matching (PSM) analysis to compare the clinical characteristics, prognostic factors and treatment modalities of three group. Materials and Methods Data collection SEER is a United States cancer patient-based database that collects data on approximately 30% of all cancer patients with the goal of reducing the burden of cancer ( https://seer.cancer.gov/ ). CSCLC, SCLC and NSCLC data were downloaded from SEER Research Data, 17 Registries, Nov 2022 Sub (2000–2020). Inclusion criteria: ( 1 ) all subjects were diagnosed in 2010–2020; ( 3 )Site and Morphology. CS Schema-AJCC 6th Edition: Lung; ( 4 ) histology code (ICD-O-3 Hist/behav): 8045/3, 8002/3, 8041/3, 8042/3, 8043/3, 8044/3, 8012/3, 8070/3, 8071/3, 8072/3, 8073/3, 8074/3, 8075/3, 8076/3 and 8140/3. ( 5 ) The tumor stage was IV. Exclusion criteria: ( 1 ) Follow-up data unknown and missing; ( 2 ) Incomplete clinical data and other relevant information. The variables collected included demographic characteristics of patients: age, gender, race, marital status. Tumor characteristics: laterality, T stage, N stage, brain metastasis, bone metastasis, liver metastasis, lung metastasis, primary site. Treatment: Surgery, Radiation and chemotherapy. Survival data: Survival months, overall survival (OS) and cancer-specific survival (CSS). To facilitate statistical analysis, we reclassified some variables: age (≥ 65,< 65), marital status (married, divorced, others), T stage (T0,T1,T2,T3,T4,TX), N stage (N0,N1,N2,N3,NX), laterality (left, right, others), primary site (main bronchus, upper lobe, middle lobe, lower lobe, others). OS and CSS were the primary endpoints in this study. Patients diagnosed in 2016–2017 were reclassified to T stage and N stage according to the "2016 SEER Manual Section V: Stage of Disease at Diagnosis" document. Definition of Treatment options: ( 1 ) Radiotherapy: Yes: patients were treated with radiotherapy as first course of treatment. No: patients were not treated with radiotherapy as first course of treatment. ( 2 ) Chemotherapy: Yes: patients were treated with chemotherapy. No: patients were not treated with chemotherapy. ( 3 ) Surgery: Yes: patients were treated with surgery. No: patients were not treated with surgery. The flow chart of patient screening is shown in Fig. 1. Propensity score matching To reduce the effect of selection bias, propensity score matching (PSM) was applied to CSCLC, SCLC and NSCLC groups in this study. The matching ratio for stage IV CSCLC and SCLC groups was 1:1 and the caliper value was set to 0.02 through the “nearest” method (Fig. 2, A), the same as the matching ratio (1:1) and caliper value (0.02) for stage IV CSCLC and NSCLC groups (Fig. 2, B). The variables used for matching were as follows: age, gender, race, marital status, T stage, N stage, laterality, primary site, brain metastasis, bone metastasis, liver metastasis, lung metastasis, surgery, radiotherapy, chemotherapy. Statistical Methods All statistical analyses were performed with SPSS 23.0(SPSS Inc., Chicago, IL, USA) and R version 4.2.1. P-value < 0.05 was considered statistically significant. The "MatchIt" “tableone” package of R was used to perform PSM analyses and Standardized mean difference (SMD). Kaplan-Meier (KM) analysis was used to compare the prognosis of different groups and treatment modalities. Pearson's χ2 test was utilized to compare the baseline characteristics of the stage IV CSCLC, IV SCLC and IV NSCLC groups. Univariable and multivariate Cox proportional hazard models were used to identify risk factors for OS and CSS in the three groups. Statements The authors declare that all methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Second Affiliated Hospital of Nanchang University. The information in this database did not involve sensitive content and identifying information of patients, therefore, Ethics committee approval and patients consent are not required for the use of such information. The following information was supplied regarding data availability: The raw data is available from the SEER database ( https://seer.cancer.gov ), informed consent was waived by Second Affiliated Hospital of Nanchang University. Result Epidemiology for IV CSCLC, IV SCLC and IV NSCLC The semi-logarithmic line chart was used to describe the number of cases per year from 2000 to 2020 per year in the three groups. It can be seen in the figure that the number of patients in NSCLC was the highest, followed by SCLC, and the number of patients in CSCLC was the lowest. The number of patients in NSCLC kept increasing from 2004 to 2017, but was generally stable. However, the incidence of CSCLC and SCLC did not fluctuate significantly (Fig. 3,A). Stacked Bar Chart was used to perform total number of patients, IV stage patients and the percentage of stage IV patients in three groups. The percentage of stage IV patients in SCLC was the highest, followed by CSCLC, and the number of patients in NSCLC was the lowest (Fig. 3,B). Basic characteristics of patients for IV CSCLC, IV SCLC and IV NSCLC before PSM There were stage IV CSCLC (n = 493), stage IV SCLC (n = 35503) and stage IV NSCLC (n= 122807) groups in this study. Demographics and clinicopathologic characteristics of patients are shown in Table 1. Age of ≥ 65 (67.3%), married (50.7%), upper lobe (49.1%), right (53.1%), T4 (44.8%), N2 (44.4%) and N3 (24.5%) were common in IV CSCLC patients. Gender, race, primary site, T stage, N stage, liver metastasis, lung metastasis, surgery, and radiotherapy were significantly different between stage IV CSCLC and stage IV SCLC group (p < 0.05). Race, primary site, N stage, bone metastasis, liver metastasis, lung metastasis and chemotherapy were significantly different in stage IV CSCLC and NSCLC group (p < 0.05). Lung metastasis (25.6% vs 19.9%) was more common and liver metastases (31.8% vs 47.7%) was less common in IV CSCLC than in SCLC. Liver metastasis(31.8%VS 17.2%)was more common, lung metastasis (25.6% vs 31.4%) and bone metastases (34.3% vs 40.3%) were less common in IV CSCLC than in NSCLC. More IV CSCLC patients chose chemotherapy (64.1% vs 52.1%) than IV NSCLC patients. More IV CSCLC patients chose surgery (5.1% vs 0.9%) and radiotherapy (43.2% vs 37.3%) than IV SCLC patients. Basic characteristics of patients for IV CSCLC, IV SCLC and IV NSCLC after PSM Random 1:1 nearest-neighbor PSM without replacement to balance all baseline covariates between CSCLC, SCLC and NCSCLC. 486 patients and 493 patients in CSCLC group were selected separately after twice PSM. The stage IV SCLC group (n = 486) and stage IV NSCLC group (n = 493) were selected for further analysis after PSM. The baseline features after PSM were well-balanced (p ≥ 0.05 and SMD<0.1, Table 2 , Fig. 2). KM analysis for IV CSCLC, IV SCLC and IV NSCLC KM analysis was used to compare OS or CSS in the stage IV CSCLC, SCLC, and NSCLC groups (Fig. 4). For the cohorts before PSM, the mOS and mCSS of IV CSCLC were both 6.0 months, IV SCLC patients presented a mOS of 5.00 months and a mCSS of 6 months. The mOS and mCSS of NSCLC patients were 6 months and 7 months. There were statistically significant difference in OS (p = 0.013,) and CSS (p = 0.014) between stage IV CSCLC and stage IV SCLC groups (Fig. 4, A,B), So did the OS (p<0.001) and CSS (p<0.001) between stage IV CSCLC and stage IV NSCLC group (Fig. 4, E,F). After PSM, the mOS and mCSS of CSCLC patients were both 6.00 months, and the SCLC were both 5.00 months, which were still poorer than the mOS and mCSS of the CSCLC patients (5.00 VS 6.00 months, Fig. 4, C,D). The mOS and mCSS of NSCLC were same as before PSM (6 months and 7 months), the mCSS of NSCLC were longer than CSCLC (7 months VS 6 months). There were statistically significant difference in OS (p = 0.003) and CSS (p = 0.006) between stage IV CSCLC and stage IV NSCLC groups (Fig. 4, G,H). Univariable Cox analysis for IV CSCLC, IV SCLC and IV NSCLC Before PSM, the results of univariate Cox analysis showed that age, race, N stage, bone metastasis, liver metastasis, surgery, radiotherapy, chemotherapy were significantly associated with OS and CSS in three groups (p<0.05, Table S1 , Fig. 5). After PSM, age, liver metastasis, N stage, radiotherapy, chemotherapy were significantly associated with OS in three groups; besides, race, bone metastasis, surgery were significantly associated with OS in stage IV CSCLC patients (p<0.05, Table S2, Fig. 6, A,C,E). Chemotherapy and radiotherapy were common associated with CSS in the three groups (p<0.05). Race and T stage were correlated with CSS of IV SCLC and IV CSCLC, age, N stage, liver metastasis and surgery were common associated with CSS of IV NSCLC and CSCLC (p<0.05, Table S2, Fig. 6, B,D,F ). Multivariate Cox analysis for IV CSCLC, IV SCLC and IV NSCLC The results of independent influencing factors of three groups were described by forest plot (Fig. 5 and Fig. 6) and the correlation of influencing factors among three groups were described by Venn diagram (Fig. 7).Before PSM, race, primary site, N stage, bone metastasis, liver metastasis, surgery, chemotherapy and radiotherapy were common independent risk/ protective factors for both CSS and OS of three groups, besides age was also the independent risk factor for OS of three groups. Gender, married status, T stage, brain metastasis, lung metastasis were common independent risk/ protective factors for OS and CSS of SCLC and NSCLC(P<0.05, Table 3 , Fig. 5 and Fig. 7A,B ). The results of multivariate Cox analysis with stage IV CSCLC after PSM were similar with the results before PSM: bone metastasis and liver metastasis were independent risk factor, surgery, chemotherapy and radiotherapy were the independent protective factors for both CSS and OS, and age was also the independent risk factor for OS. Liver metastasis was the common independent risk factor for OS of three groups. Chemotherapy was the protective factors for CSS and OS of three groups (p<0.05). Surgery was the independent protective factor for CSS and OS of CSCLC and NSCLC. Radiotherapy was the common protective factor for CSS of CSCLC and SCLC (P<0.05, Table 4 , Fig. 6 and Fig. 7C, D). Prognosis of each treatment modality in IV CSCLC patients To assess the prognostic impact of each treatment modality on patients with IV CSCLC, we compared treatment outcomes with radiotherapy, chemotherapy and surgery. The mOS of surgery was 10.0 months (Fig. 8A), radiotherapy was 7.0 months (Fig. 8C), chemotherapy was 8.0 months (Fig. 8E), The KM analysis showed that radiotherapy, chemotherapy and surgery could improve the survival probability of IV CSCLC patients (p < 0.05). The mCSS of surgery, radiotherapy and chemotherapy were 10.0 months, 7.0 months and 9.0 months (Fig. 8B,D,F). Evaluation of different treatment modalities of IV CSCLC, IV SCLC and IV NSCLC To identify the effect of treatment modalities on OS and CSS for three groups, patients were divided into eight groups according to treatment modalities before PSM: Control: patients were not treated with radiotherapy, chemotherapy or surgery since being diagnosed. Surgery: patients were treated with surgery alone. Chemotherapy: patients were treated with chemotherapy alone. Radiotherapy: patients were treated with radiotherapy alone. Chemoradiotherapy: patients were both treated with radiotherapy and chemotherapy. Surgery + chemotherapy: patients were treated with surgery and chemotherapy. Surgery + radiotherapy: patients were treated with surgery and radiotherapy (data missing in CSCLC group). Surgery + chemoradiotherapy: patients were treated with surgery, chemotherapy and radiotherapy. Characteristics of CSCLC, SCLC, NSCLC were shown in Table S3 to Table S5. KM analysis was used to compare the difference in survival probability between patients with different treatment modalities (Fig. 9) and mOS/mCSS in different groups were shown in Table 4 . The mOS and mCSS of surgery + chemotherapy group were 10 months and 16 months respectively, with a better survival probability than other groups in IV CSCLC (p < 0.001). Besides, chemotherapy alone had similar OS and CSS with surgery + chemotherapy, chemoradiotherapy, and surgery + chemoradiotherapy groups. The mOS and mCSS of Surgery + chemoradiotherapy group were 13 months and 14 months in IV SCLC, which had a higher probability of survival than the other treatments (p<0.001). In IV NSCLC group, Surgery + chemotherapy group had better mOS (29 months)and mCSS (35 months)than other treatment modalities (p < 0.001). Discussion CSCLC is a rare histological type of lung cancer. In this study, we retrieved the data of CSCLC patients from the SEER database in the past two decades (2194 cases), accounting for 2.2% of the total number of lung cancer patients (100,753 cases). The number of annual incidence has been maintained at around 100 cases, which is consistent with the incidence rate of CSCLC in previous studies ranging from 1–14%. The highest detection rate of 14.3% was reported by Fushimi et al, the study found that the detection rate by autopsy was significantly higher than that by biopsy or other cytological methods 15 , the main detection method in the SEER database is not autopsy, so the detection rate may be low. The proportion of IV CSCLC patients is 38.33%, which is between that of IV NSCLC (35.22%) and IV SCLC (49.44%). Since some studies has been confirmed that CSCLC has histological features in common with NSCLC and SCLC 3,6–8,16 , it is necessary to further explore the differences in clinical features, prognostic factors, and treatment methods among the three groups. Our study suggests that most advanced CSCLC cases are elderly patients, with a higher incidence in the upper lobe and right lung. There are significant differences in clinical characteristics between advanced CSCLC, NSCLC, and SCLC, which are reflected in demographic and tumor features. The probability of liver and lung metastasis in IV CSCLC is between that of IV NSCLC and IV SCLC. This may be due to better treatment outcomes in IV CSCLC, as CSCLC patients are more willing to undergo surgery and chemoradiotherapy compared to the other two groups. However, despite there being no significant difference in survival time (OS and CSS) between IV CSCLC and IV SCLC after PSM, the Kaplan-Meier curve trend and before PSM results suggest that the survival of IV CSCLC is slightly better than that of IV SCLC. Additionally, both before and after PSM, the survival of IV CSCLC is shorter than that of IV NSCLC (P < 0.05), which is consistent with previous studies of CSCLC in I-IV stages 5,17 . For this situation, SCLC is considered to have the worst prognosis and the highest degree of malignancy among all lung cancer tissue types, especially in the advanced stage 18 , it has also been proposed that CSCLC is more closely related to SCLC than to NSCLC 19 , the SCLC component is a negative prognostic factor in CSCLC, and there may be a correlation between the proportion of SCLC component and prognosis 20 , due to CSCLC is mixed with some components of NSCLC, the survival time will be in the middle of the SCLC and NSCLC. Subsequently, univariate and multivariate COX analysis were performed on the three groups. Bone metastasis and liver metastasis were identified as independent risk factors for prognosis in all three groups. However, brain metastasis and lung metastasis were identified as independent risk factors for SCLC and NSCLC, but not for CSCLC. In the study by Zhang et al 10 , it was believed that liver and lung metastases were not prognostic factors for CSCLC patients. However, that study only included patients with CSCLC in stages I-IV, so it can be speculated that liver metastasis is only an independent risk factor in advanced CSCLC patients. For SCLC and NSCLC, liver, brain, bone, and lung metastases are all independent risk factors. Previous studies have suggested that SCLC patients with liver metastases had the unfavorable prognosis 21 , In our study, liver metastasis in advanced CSCLC may also indicate poor prognosis. We speculate that the survival time of IV CSCLC is between CSCLC and NSCLC, it may be that the probability of liver metastasis in advanced CSCLC is lower than that in SCLC, so the prognosis of IV CSCLC is slightly better than that in IV SCLC, while the probability of liver metastasis in advanced CSCLC is higher than that in NSCLC, so the prognosis is worse than that in NSCLC. Bone metastasis is also an important risk factor for the prognosis of CSCLC, which can affect CSS and OS of CSCLC. Therefore, our study suggests that liver and bone metastasis in patients with advanced CSCLC may indicate poor prognosis. Before PSM, all treatment methods (radiotherapy, chemotherapy, surgery) were independent protective factors for all three groups of patients. After PSM, the three treatment methods were independent protective factors for IV CSCLC, but only chemotherapy was an independent protective factor for OS and CSS for all three groups. The independent protective factors for NSCLC were surgery and chemotherapy, while for SCLC were radiotherapy and chemotherapy. This may be the small number of cases included in the NSCLC and SCLC groups after PSM, or because the SCLC patients included were generally at a later stage and unable to undergo surgery. However, it can be considered that chemotherapy was an important protective factor for all three groups of patients. Surgery and radiotherapy may be dependent on the patient's condition for advanced NSCLC and SCLC. As all three treatment methods were independent protective factors for CSS and OS in CSCLC, we performed separate KM analyses for each treatment method. We found that all three treatment methods significantly prolong survival time and have statistical significance. Therefore, CSCLC patients are more willing to receive surgery and chemoradiotherapy compared to the other two groups. Among the three treatment methods, surgery has the longest survival time, followed by chemotherapy. It is worth noting that age, primary site, and N staging may also affect the prognosis of CSCLC. Therefore, for patients with advanced CSCLC, it is also important to pay particular attention to the age of patients, primary site, and the N staging. Due to the rare histological type of CSCLC among lung cancers, there is limited real-world data, so there are no detailed guidelines for the treatment of CSCLC 22,23 , especially for advanced CSCLC. We conducted separate KM analysis for each treatment modality in patients with advanced CSCLC, SCLC, and NSCLC. In this study, the combination of surgery and chemotherapy was found to have the best efficacy in patients with advanced CSCLC, consistent with the latest ESMO clinical practice guidelines: for patients suspected of having CSCLC, surgical treatment may be considered 22 . However, for some advanced patients who have already lost the opportunity for surgery at the time of first diagnosis, our study suggests that chemotherapy alone is also a good choice, with the median survival time second only to combined treatment. For patients with advanced SCLC and NSCLC, combined treatment with surgery, radiotherapy, and chemotherapy is more effective. Since CSCLC has components of both SCLC and NSCLC, can we draw lessons from the treatment protocols of NSCLC or SCLC? A study by Luo et al. found that CSCLC patients who received the classic chemotherapy regimen for SCLC (etoposide and cisplatin) presented a survival benefit 24 , this study retrospectively compared the efficacy of the NIP (navelbine + ifosfamide + cisplatin) and EP (etoposide + cisplatin) regimens as first-line treatment options for III-IV stage CSCLC, the results suggest that the EP regimen may provide better survival benefits compared to the NIP regimen. However, some studies have mentioned that NSCLC is less sensitive to the EP regimen, and CSCLC contains NSCLC components, so the clinical benefits of using the EP regimen for CSCLC may not be as effective as for SCLC 14 . EGFR-TKIs are widely used in NSCLC with EGFR mutations, but there have been no randomized clinical trials to evaluate their efficacy in the treatment of CSCLC, only small case series studies have suggested that EGFR-TKIs may be helpful in the treatment of CSCLC and SCLC, but the efficacy of EGFR-TKIs in the treatment of CSCLC or SCLC may not be as good as in NSCLC 25,26 . In summary, for patients with advanced CSCLC, if there are indications for surgery, the combination of surgery and chemotherapy should be given priority. If the opportunity for surgery has been lost, the chemotherapy regimen for SCLC can be referred to. There are some limitations to our study. Firstly, the SEER database does not provide information on some patient characteristics such as smoking, past medical history, specific chemotherapy and radiotherapy regimens, immunotherapy and targeted therapy, which may affect our results. Secondly, no further molecular level analysis and comparison were performed among the three groups. Thirdly, due to the limitations of diagnostic techniques and the low incidence and high mortality of CSCLC, we were unable to provide information on IV stage CSCLC patients from our own database. Future prospective studies are needed to further explore the relationship between CSCLC and NSCLC, SCLC. Conclusion In summary, the clinical features of IV CSCLC differ from those of IV SCLC and IV NSCLC, we found that the prognosis of IV CSCLC patients was significantly worse than that of IV NSCLC patients, but better than IV SCLC patients. Additionally, surgery combined chemotherapy was the best efficacy in patients with IV CSCLC and chemotherapy regimen can referred to IV SCLC. Declarations Authors contribution Xiaoqun Ye designed the conception and methodology. Zhouhua Li, Xiaotian Huang, Jinbo Li and Hongdan Luo performed the data analysis. Shanshan Cai and Weichang Yang wrote the original draft. All authors critically revised the manuscript for intellectual content. All authors approved the final version of the manuscript. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 81660493), the Natural Science Foundation of Jiangxi Province (Grant No.20202ACBL206019) and Jiangxi Province Graduate Innovation Special Fund (YC2023-B083). Data availability The following information was supplied regarding data availability: The raw data is available from the SEER database (https://seer.cancer.gov), informed consent was waived by Second Affiliated Hospital of Nanchang University. Disclosures Shanshan Cai, Weichang Yang, Zhouhua Li, Xiaotian Huang, Jinbo Li, Hongdan Luo, Dr. Xiaoqun Ye have no potential conflicts of interest or financial ties to disclose. References Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet (London, England) 2021;398:535-54. Wagenaar SS. 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Tables Characteristics CSCLC , N = 493 1 SCLC , N = 35503 1 p-value 2 NSCLC , N = 122807 1 p-value 2 Age.years. 0.065 0.381 ＜65 161 (32.7%) 13,024 (36.7%) 42542 (34.6%) ≥65 332 (67.3%) 22,479 (63.3%) 80265 (65.4%) Gender 0.003 0.057 male 289 (58.6%) 18,409 (51.9%) 66604 (54.2%) female 204 (41.4%) 17,094 (48.1%) 56203 (45.8%) Race 0.009 0.002 Black 57 (11.6%) 2,981 (8.4%) 14930 (12.2%) White 405 (82.2%) 30,976 (87.2%) 95575 (77.8%) Asian or Pacific Islander 26 (5.3%) 1,288 (3.6%) 11610 (9.5%) American Indian/Alaska Native 5 (1.0%) 258 (0.7%) 692 (0.6%) Married.status 0.472 0.256 Married 250 (50.7%) 17,082 (48.1%) 61977 (50.5) Divorced 70 (14.2%) 5,070 (14.3%) 14754 (12.0) Others 173 (35.1%) 13,351 (37.6%) 46076 (37.5) Primary.Site 0.005 0.010 Main bronchus 36 (7.3%) 3,916 (11.0%) 5158 (4.2%) Upper lobe 242 (49.1%) 15,480 (43.6%) 59390 (48.4%) Middle lobe 14 (2.8%) 1,249 (3.5%) 4940 (4.0%) Lower lobe 112 (22.7%) 7,112 (20.0%) 31467 (35.6%) Others 89 (18.1%) 7,746 (21.8%) 21852 (17.8%) Laterality 0.831 0.655 Left 196 (39.8%) 14,100 (39.7%) 47460 (38.6%) Right 262 (53.1%) 18,625 (52.5%) 67296 (54.8%) Others 35 (7.1%) 2,778 (7.8%) 8105 (6.6%) T.stage 0.011 0.254 T0 5 (1.0%) 461 (1.3%) 1101 (0.9%) T1 41 (8.3%) 3,200 (9.0%) 13771 (11.2%) T2 121 (24.5%) 7,204 (20.3%) 28899 (23.5%) T3 55 (11.2%) 3,320 (9.4%) 13246 (10.8%) T4 221 (44.8%) 15,924 (44.9%) 50519 (41.1%) TX 50 (10.1%) 5,394 (15.2%) 15271 (12.5%) N.stage <0.001 0.010 N0 95 (19.3%) 4,379 (12.3%) 30248 (24.6) N1 33 (6.7%) 2,305 (6.5%) 9475 (7.7%) N2 219 (44.4%) 17,845 (50.3%) 49169 (40.0%) N3 121 (24.5%) 8,828 (24.9%) 25870 (21.1%) NX 25 (5.1%) 2,146 (6.0%) 8045 (6.6%) Bone.Metastasis 0.520 0.008 Yes 169 (34.3%) 12,667 (35.7%) 49431 (40.3%) No 324 (65.7%) 22,836 (64.3%) 73376 (59.7%) Brain.Metastasis 0.082 0.287 Yes 140 (28.4%) 8,870 (25.0%) 32155 (26.2%) No 353 (71.6%) 26,633 (75.0%) 90652 (73.8%) Liver.Metastasis <0.001 ＜ 0.001 Yes 157 (31.8%) 16,940 (47.7%) 21077 (17.2%) No 336 (68.2%) 18,563 (52.3%) 101730 (82.8%) Lung.Metastasis 0.002 0.006 Yes 126 (25.6%) 7,076 (19.9%) 38568 (31.4%) No 367 (74.4%) 28,427 (80.1%) 84239 (68.6%) Surgery <0.001 0.068 Yes 25 (5.1%) 329 (0.9%) 4250 (3.5%) No 468 (94.9%) 35,174 (99.1%) 118557 (96.5%) Radiotherapy 0.008 0.887 Yes 213 (43.2%) 13,259 (37.3%) 52543 (42.8%) No 280 (56.8%) 22,244 (62.7%) 70264 (57.2%) Chemotherapy 0.202 ＜ 0.001 Yes 316 (64.1%) 23,723 (66.8%) 64004 (52.1%) No 177 (35.9%) 11,780 (33.2%) 58803 (47.9%) 1 n (%) 2 Pearson's Chi-squared test; Fisher's exact test Table 2: Demographics and clinicopathologic characteristics of patients with CSCLC ,SCLC and NSCLC after PSM Characteristics CSCLC, N = 486 SCLC, N = 486 p-value 2 CSCLC, N = 493 NSCLC, N = 493 p-value 2 Age.years. 0.334 0.733 <65 160 (32.9) 146 (30.0) 161 (32.7) 156 (31.6) ≥ 65 326 (67.1) 340 (70.0) 332 (67.3) 337 (68.4) Gender 0.745 0.747 male 283 (58.2) 288 (59.3) 289 (58.6) 284 (57.6) female 203 (41.8) 198 (40.7) 204 (41.4) 209 (42.4) Race 0.861 0.687 Black 56 (11.5) 49 (10.1) 57 (11.6) 50 (10.1) White 400 (82.3) 410 (84.4) 405 (82.2) 418 (84.8) Asian or Pacific Islander 25 (5.1) 23 (4.7) 25 (5.1) 19 (3.9) American Indian/Alaska Native 5 (1.0) 4 (0.8) 6 (1.2) 6 (1.2) Married.status 0.846 0.402 Married 246 (50.6) 255 (52.5) 250 (50.7) 269 (54.6) Divorced 70 (14.4) 67 (13.8) 70 (14.2) 59 (12.0) Others 170 (35.0) 164 (33.7) 173 (35.1) 165 (33.5) Primary.Site 0.533 0.573 Main bronchus 36 (7.4) 30 (6.2) 35 (7.1) 27 (5.5) Upper lobe 237 (48.8) 259 (53.3) 242 (49.1) 246 (49.9) Middle lobe 14 (2.9) 14 (2.9) 14 (2.8) 10 (2.0) Lower lobe 110 (22.6) 92 (18.9) 112 (22.7) 106 (21.5) Others 89 (18.3) 91 (18.7) 90 (18.3) 104 (21.1) Laterality 0.637 0.987 Left 195 (40.1) 202 (41.6) 196 (39.8) 194 (39.4) Right 256 (52.7) 256 (52.7) 261 (52.9) 262 (53.1) Others 35 (7.2) 28 (5.8) 36 (7.3) 37 (7.5) T.stage 0.894 0.980 T0 5 (1.0) 4 (0.8) 5 (1.0) 4 (0.8) T1 41 (8.4) 37 (7.6) 41 (8.3) 44 (8.9) T2 119 (24.5) 117 (24.1) 121 (24.5) 122 (24.7) T3 53 (10.9) 65 (13.4) 55 (11.2) 48 (9.7) T4 218 (44.9) 216 (44.4) 221 (44.8) 223 (45.2) TX 50 (10.3) 47 (9.7) 50 (10.1) 52 (10.5) N.stage 0.774 0.966 N0 91 (18.7) 96 (19.8) 95 (19.3) 100 (20.3) N1 31 (6.4) 30 (6.2) 33 (6.7) 37 (7.5) N2 218 (44.9) 232 (47.7) 219 (44.4) 217 (44.0) N3 121 (24.9) 107 (22.0) 121 (24.5) 114 (23.1) NX 25 (5.1) 21 (4.3) 25 (5.1) 25 (5.1) Bone.Metastasis 0.687 0.947 Yes 167 (34.4) 173 (35.6) 169 (34.3) 170 (34.5) No 319 (65.6) 313 (64.4) 324 (65.7) 323 (65.5) Brain.Metastasis 0.830 0.833 Yes 138 (28.4) 135 (27.8) 140 (28.4) 143 (29.0) No 348 (71.6) 351 (72.2) 353 (71.6) 350 (71.0) Liver.Metastasis 0.945 0.838 Yes 157 (32.3) 156 (32.1) 157 (31.8) 160 (32.5) No 329 (67.7) 330 (67.9) 336 (68.2) 333 (67.5) Lung.Metastasis 0.941 0.942 Yes 124 (25.5) 123 (25.3) 126 (25.6) 125 (25.4) No 362 (74.5) 363 (74.7) 367 (74.4) 368 (74.6) Surgery 0.727 0.275 Yes 18 (3.7) 16 (3.3) 25 (5.1) 18 (3.7) No 468 (96.3) 470 (96.7) 468 (94.9) 475 (96.3) Radiotherapy 0.516 0.521 Yes 210 (43.2) 200 (41.2) 213 (43.2) 223 (45.2) No 276 (56.8) 286 (58.8) 280 (56.8) 270 (54.8) Chemotherapy 0.687 0.894 Yes 312 (64.2) 318 (65.4) 316 (64.1) 318 (64.5) No 174 (35.8) 168 (34.6) 177 (35.9) 175 (35.5) 1 n (%) 2 Pearson's Chi-squared test; Fisher's exact test Table 4: Multivariable Cox analysis of OS and CSS in IV CSCLC, SCLC and NSCLC after PSM OS after PSM CSS after PSM CSCLC SCLC NSCLC CSCLC SCLC NSCLC Characteristic HR(95CI) pvalue HR(95CI) pvalue HR(95CI) pvalue HR(95Cl) pvalue HR(95CI) pvalue HR(95CI) pvalue Age.years. <65 — — — — — — ≥ 65 1.24 (1.00, 1.53) 0.050 1.75(1.39, 2.20) < 0.001 1.58(1.27, 1.98) < 0.001 1.21(0.97, 1.51) 0.095 1.25(0.70, 2.25) 0.449 1.56(1.24, 1.96) < 0.001 Gender male — — — — — — female 0.83 (0.67, 1.02) 0.075 0.87(0.71, 1.07) 0.189 0.84(0.69, 1.03) 0.098 0.85(0.68, 1.06) 0.148 0.84(0.47, 1.49) 0.547 0.86(0.70, 1.07) 0.183 Race Black — — — — — — White 1.28 (0.94, 1.74) 0.120 1.08(0.78, 1.51) 0.642 0.99(0.71, 1.38) 0.949 1.36(0.98, 1.88) 0.069 1.03(0.44, 2.44) 0.944 1.02(0.72, 1.45) 0.905 Asian or Pacific Islander 1.79 (1.07, 3.01) 0.028 0.92(0.53, 1.60) 0.770 0.83(0.33, 2.08) 0.697 2.00(1.17,3.43) 0.012 2.03(0.53, 7.80) 0.305 0.9(0.36, 2.28) 0.827 American Indian/Alaska Native 0.87 (0.33, 2.26) 0.774 1.32(0.45, 3.85) 0.612 0.48(0.25, 0.94) 0.032 1.02(0.39, 2.66) 0.974 5.37(1.16,24.91) 0.032 0.49(0.24, 0.99) 0.045 Married.status Married — — — — — — Divorced 1.03 (0.76, 1.39) 0.870 1.12(0.83, 1.51) 0.459 1.44(1.04, 1.98) 0.026 1.09(0.80, 1.49) 0.584 2.50(1.16, 5.39) 0.020 1.42(1.02, 1.98) 0.036 Others 0.90 (0.72, 1.12) 0.347 1.11(0.88, 1.39) 0.387 1.11(0.89, 1.40) 0.353 0.93(0.74, 1.18) 0.549 1.72(0.91, 3.25) 0.096 1.01(0.79, 1.28) 0.944 Primary.Site Main bronchus — — — — — — Upper lobe 0.77 (0.52, 1.13) 0.183 1.35(0.89, 2.07) 0.162 0.55(0.36, 0.84) 0.006 0.75(0.50, 1.11) 0.150 1.11(0.32, 3.89) 0.866 0.53(0.34, 0.82) 0.004 Middle lobe 0.72 (0.37, 1.38) 0.319 1.33(0.65, 2.72) 0.427 0.59(0.26,1.3) 0.223 0.66(0.33, 1.31) 0.234 2.83(0.47, 17.14) 0.258 0.64(0.28, 1.49) 0.302 Lower lobe 0.71 (0.47, 1.08) 0.115 1.31(0.82, 2.08) 0.258 0.57(0.36, 0.89) 0.015 0.69(0.45, 1.06) 0.092 2.45(0.65, 9.22) 0.184 0.51(0.32, 0.81) 0.005 Others 0.59 (0.37, 0.95) 0.028 1.15(0.71, 1.86) 0.576 0.75(0.46, 1.22) 0.242 0.61(0.38, 0.99) 0.045 2.65(0.68,10.32) 0.160 0.76(0.46, 1.25) 0.282 Laterality Left — — — — — — Right 1.14 (0.93, 1.40) 0.214 0.97(0.79, 1.20) 0.800 0.98(0.79, 1.22) 0.873 1.09(0.88, 1.35) 0.449 1.06(0.59, 1.91) 0.854 0.97(0.78, 1.22) 0.820 Others 1.24 (0.78, 1.97) 0.362 1.15(0.67, 1.98) 0.620 1.04(0.66, 1.63) 0.869 1.00(0.61, 1.65) 0.998 0.87(0.19, 4.02) 0.857 0.99(0.62, 1.58) 0.964 T.stage T0 — — — — — — T1 0.70 (0.26, 1.88) 0.483 1.81(0.41, 8.08) 0.435 0.83(0.24, 2.85) 0.771 0.60(0.22, 1.61) 0.307 0.87(0.10, 7.43) 0.898 0.83(0.24, 2.86) 0.768 T2 0.65 (0.25, 1.70) 0.380 2.3(0.53, 9.95) 0.263 1.14(0.34, 3.79) 0.831 0.56(0.22, 1.47) 0.242 0.17(0.02, 1.42) 0.103 1.13(0.34, 3.78) 0.842 T3 0.67 (0.25, 1.75) 0.410 2.06(0.47, 9.08) 0.338 0.85(0.25, 2.92) 0.799 0.52(0.20, 1.39) 0.195 1.16(0.15, 9.20) 0.891 0.83(0.24, 2.86) 0.766 T4 0.72 (0.28, 1.84) 0.489 2.19(0.51, 9.39) 0.292 1.20(0.37, 3.92) 0.766 0.63(0.25, 1.63) 0.345 0.28(0.04, 2.11) 0.216 1.23(0.37, 4.05) 0.731 TX 0.78 (0.30,2.06) 0.620 2.11(0.49, 9.01) 0.314 0.75(0.22, 2.54) 0.641 0.67(0.25, 1.78) 0.421 0.33(0.05, 2.36) 0.268 0.74(0.22, 2.53) 0.630 N.stage N0 — — — — — — N1 1.30(0.81, 2.08) 0.275 0.74(0.46, 1.18) 0.205 0.89(0.58,1.30) 0.616 1.23(0.75, 2.03) 0.411 2.48(0.85, 7.27) 0.097 0.89(0.56, 1.41) 0.621 N2 1.26(0.96, 1.67) 0.100 0.94(0.72, 1.23) 0.664 1.48(1.12, 1.96) 0.006 1.31(0.98, 1.76) 0.067 2.01(0.93, 4.33) 0.076 1.49(1.11, 2.00) 0.007 N3 1.58(1.16, 2.16) 0.004 1.03(0.75, 1.41) 0.852 1.13(0.83, 1.55) 0.437 1.62(1.17, 2.25) 0.004 1.72(0.71, 4.18) 0.234 1.15(0.83, 1.60) 0.402 NX 2.03(1.24, 3.33) 0.005 1.34(0.78, 2.29) 0.287 1.70(1.02, 2.84) 0.041 1.90(1.12, 3.23) 0.018 0.99(0.13, 7.43) 0.989 1.57(0.91, 2.71) 0.102 Bone.Metastasis No — — — — — — Yes 1.31(1.06, 1.63) 0.013 1.13(0.91, 1.40) 0.262 1.21(0.98, 1.49) 0.080 1.34(1.07, 1.67) 0.010 0.85(0.48, 1.51) 0.579 1.22(0.98, 1.52) 0.079 Brain.Metastasis No — — — — — — Yes 1.26(0.97, 1.62) 0.083 1.36(1.07, 1.72) 0.010 1.32(1.03, 1.68) 0.030 1.23(0.94, 1.61) 0.127 1.25(0.66, 2.38) 0.500 1.37(1.06, 1.78) 0.016 Liver.Metastasis No — — — — — — Yes 1.46(1.17, 1.81) < 0.001 1.75(1.40, 2.19) < 0.001 1.37(1.11, 1.70) 0.004 1.42(1.13, 1.79) 0.003 1.69(0.91, 3.14) 0.098 1.51(1.21, 1.88) < 0.001 Lung.Metastasis No — — — — — — Yes 1.12(0.89, 1.42) 0.331 1.16(0.92, 1.46) 0.206 1.08(0.85, 1.37) 0.532 1.04(0.82, 1.34) 0.730 1.26(0.69, 2.30) 0.449 1.03(0.80, 1.33) 0.789 Surgery No — — — — — — Yes 0.44(0.25, 0.75) 0.003 0.89(0.49, 1.59) 0.682 0.35(0.19, 0.64) < 0.001 0.42(0.23, 0.74) 0.003 1.64(0.45, 6.01) 0.455 0.37(0.20, 0.68) 0.002 Radiotherapy No — — — — — — Yes 0.68(0.54, 0.86) < 0.001 0.71(0.57, 0.89) 0.003 0.88(0.70, 1.10) 0.251 0.69(0.54, 0.87) 0.002 0.7(0.38, 1.27) 0.238 0.88(0.69, 1.11) 0.268 Chemotherapy No — — — — — — Yes (0.22, 0.35) < 0.001 0.31(0.24, 0.38) < 0.001 0.32(0.25, 0.39) < 0.001 0.31(0.24, 0.39) < 0.001 0.44(0.23, 0.82) 0.010 0.31(0.25, 0.40) < 0.001 Table 5 : The mOS and mCSS of different therapy methods in IV CSCLC, SCLC and IV SCLC Therapy method Group CSCLC SCLC NSCLC mOS (months) mCSS (months) mOS (months) mCSS (months) mOS (months) mCSS (months) Control 1 1 0 1 2 3 Surgery 7 7 1 2 14 20 Chemotherapy 8 8 7 7 12 13 Radiotherapy 3 3 2 2 3 3 Chemoradiotherapy 8 9 10 10 10 11 Surgery + chemotherapy 10 16 9 9 29 35 Surgery + radiotherapy NA NA 2 2 7 8 Surgery + chemoradiotherapy 9 9 13 14 20 21 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table3.docx Supplementarytable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoqun","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-02-08 07:59:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3939208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3939208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51776039,"identity":"fea6ba03-5c9a-4ef7-a8a2-07dc5a0da246","added_by":"auto","created_at":"2024-02-28 20:46:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163393,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1Flowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/2844881d1c919614bf33b8c0.jpg"},{"id":51776578,"identity":"30b80a67-26e0-4b60-81dc-352afdb2e469","added_by":"auto","created_at":"2024-02-28 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6","display":"","copyAsset":false,"role":"figure","size":882887,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure6afterpsm.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/81ac6e32ccd821c1e3bd0c6a.jpg"},{"id":51776046,"identity":"676a9dcb-6581-4174-b03d-ce43ac254bef","added_by":"auto","created_at":"2024-02-28 20:46:22","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":442982,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/c5a626cf91fe34005946812d.jpg"},{"id":51776042,"identity":"7e56649f-a9f9-45ba-b5ce-c16fa05813e8","added_by":"auto","created_at":"2024-02-28 20:46:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":440282,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/d33b0e1b9be1bbfffff9a24c.jpg"},{"id":51776579,"identity":"37c80d53-aaf4-41d8-919b-8080ad8f165b","added_by":"auto","created_at":"2024-02-28 20:54:21","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":559508,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/8e3d2738adc42114aac36bcf.jpg"},{"id":60785883,"identity":"3a900663-335e-498f-a830-570dca1f2616","added_by":"auto","created_at":"2024-07-22 05:24:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6628960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/7f58cccc-2d59-4b2e-a35f-63b907a26da8.pdf"},{"id":51776037,"identity":"5721afa6-e605-4840-a131-7e18342a594d","added_by":"auto","created_at":"2024-02-28 20:46:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37618,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/cedfc62e7dd226f74442951d.docx"},{"id":51776038,"identity":"a3d125e1-de96-4b7e-920e-e82b767ff8b7","added_by":"auto","created_at":"2024-02-28 20:46:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":97582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-3939208/v1/185777726eadf8e56d25a69d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical features and prognostic factors of IV combined small cell lung cancer: A Propensity Score Matching Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide, with an estimated 2\u0026nbsp;million new cases and 1\u0026middot;76\u0026nbsp;million deaths per year\u003csup\u003e1\u003c/sup\u003e. Combined small-cell lung carcinoma (CSCLC) is a rare tissue type of lung cancer, the initial definition of CSCLC dates back to 1999\u003csup\u003e2\u003c/sup\u003e, the histological classification of the World Health Organization (WHO) classifies SCLC into SCLC and Combined SCLC (CSCLC), which accounts for about 5\u0026ndash;20% of total SCLC cases\u003csup\u003e3,4\u003c/sup\u003e. CSCLC is a relatively rare subtype of SCLC, which refers to a subtype that has both SCLC histology and any subtype of non-small cell lung cancer (NSCLC)\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCSCLC is closely related to genotypic and immunophenotypic of NSCLC and SCLC. Epidermal growth factor receptor (EGFR) mutations are present in NSCLC and related to tumor response to EGFR tyrosine kinase inhibitors (TKIs), indicating that EGFR constitutes a potential biomarker. Previous studies reported that EGFR mutations occur in less than 5% of SCLC cases, while a rate reaching 15\u0026ndash;20% can be found in CSCLC\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e.Wagner et al\u003csup\u003e9\u003c/sup\u003e first assessed 7 CSCLC cases for genotypic and immunophenotypic associations determining whether NSCLC constituents displayed features specific to SCLC. In this study, several biomarkers were utilized, further indicating that a common clonal precursor with closer relationship with SCLC than NSCLC exists, CSCLCs are closer to SCLC than NSCLC.\u003c/p\u003e \u003cp\u003eIt is very meaningful to compare prognosis and clinical features due to the closely association of genotypes and immunophenotypes. However, because of the rarity of the CSCLC, little research has been done on the prognosis and treatment of patients with subtypes of CSCLC and comparison with SCLC and NSCLC, especially IV stage. Only several studies compared survival and prognosis of CSCLC and SCLC\u003csup\u003e10\u0026ndash;13\u003c/sup\u003e, but conflicting results have been reported by different studies. As for the study about comparison of CSCLC and NSCLC is even rarer, and no relevant studies on the IV stage have been conducted so far.\u003c/p\u003e \u003cp\u003eMost cases of CSCLC belonged to advanced stages when they were firstly diagnosed. Zhang et al. reported almost 90% of CSCLC were diagnosed as stage III and IV in their cohort\u003csup\u003e14\u003c/sup\u003e. Thus, the study of IV CSCLC is of great clinical interest The Surveillance, Epidemiology, and End Results (SEER) database is unique in the number of cases, especially for IV CSCLC patients. In this study, we obtained stage IV CSCLC, IV SCLC and IV NSCLC data from SEER database and performed 1:1 propensity score matching (PSM) analysis to compare the clinical characteristics, prognostic factors and treatment modalities of three group.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eSEER is a United States cancer patient-based database that collects data on approximately 30% of all cancer patients with the goal of reducing the burden of cancer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/\u003c/span\u003e\u003c/span\u003e). CSCLC, SCLC and NSCLC data were downloaded from SEER Research Data, 17 Registries, Nov 2022 Sub (2000\u0026ndash;2020). Inclusion criteria: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) all subjects were diagnosed in 2010\u0026ndash;2020; (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)Site and Morphology. CS Schema-AJCC 6th Edition: Lung; (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) histology code (ICD-O-3 Hist/behav): 8045/3, 8002/3, 8041/3, 8042/3, 8043/3, 8044/3, 8012/3, 8070/3, 8071/3, 8072/3, 8073/3, 8074/3, 8075/3, 8076/3 and 8140/3. (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) The tumor stage was IV. Exclusion criteria: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Follow-up data unknown and missing; (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) Incomplete clinical data and other relevant information.\u003c/p\u003e\n\u003cp\u003eThe variables collected included demographic characteristics of patients: age, gender, race, marital status. Tumor characteristics: laterality, T stage, N stage, brain metastasis, bone metastasis, liver metastasis, lung metastasis, primary site. Treatment: Surgery, Radiation and chemotherapy. Survival data: Survival months, overall survival (OS) and cancer-specific survival (CSS). To facilitate statistical analysis, we reclassified some variables: age (\u0026ge;\u0026thinsp;65,\u0026lt; 65), marital status (married, divorced, others), T stage (T0,T1,T2,T3,T4,TX), N stage (N0,N1,N2,N3,NX), laterality (left, right, others), primary site (main bronchus, upper lobe, middle lobe, lower lobe, others). OS and CSS were the primary endpoints in this study. Patients diagnosed in 2016\u0026ndash;2017 were reclassified to T stage and N stage according to the \"2016 SEER Manual Section V: Stage of Disease at Diagnosis\" document. Definition of Treatment options: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Radiotherapy: Yes: patients were treated with radiotherapy as first course of treatment. No: patients were not treated with radiotherapy as first course of treatment. (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) Chemotherapy: Yes: patients were treated with chemotherapy. No: patients were not treated with chemotherapy. (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) Surgery: Yes: patients were treated with surgery. No: patients were not treated with surgery. The flow chart of patient screening is shown in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003ePropensity score matching\u003c/h2\u003e\n\u003cp\u003eTo reduce the effect of selection bias, propensity score matching (PSM) was applied to CSCLC, SCLC and NSCLC groups in this study. The matching ratio for stage IV CSCLC and SCLC groups was 1:1 and the caliper value was set to 0.02 through the \u0026ldquo;nearest\u0026rdquo; method (Fig.\u0026nbsp;2, A), the same as the matching ratio (1:1) and caliper value (0.02) for stage IV CSCLC and NSCLC groups (Fig.\u0026nbsp;2, B). The variables used for matching were as follows: age, gender, race, marital status, T stage, N stage, laterality, primary site, brain metastasis, bone metastasis, liver metastasis, lung metastasis, surgery, radiotherapy, chemotherapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Methods\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed with SPSS 23.0(SPSS Inc., Chicago, IL, USA) and R version 4.2.1. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The \"MatchIt\" \u0026ldquo;tableone\u0026rdquo; package of R was used to perform PSM analyses and Standardized mean difference (SMD). Kaplan-Meier (KM) analysis was used to compare the prognosis of different groups and treatment modalities. Pearson's \u0026chi;2 test was utilized to compare the baseline characteristics of the stage IV CSCLC, IV SCLC and IV NSCLC groups. Univariable and multivariate Cox proportional hazard models were used to identify risk factors for OS and CSS in the three groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eStatements\u003c/h2\u003e\n\u003cp\u003eThe authors declare that all methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were approved by the Second Affiliated Hospital of Nanchang University.\u003c/p\u003e\n\u003cp\u003eThe information in this database did not involve sensitive content and identifying information of patients, therefore, Ethics committee approval and patients consent are not required for the use of such information.\u003c/p\u003e\n\u003cp\u003eThe following information was supplied regarding data availability: The raw data is available from the SEER database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov\u003c/span\u003e\u003c/span\u003e), informed consent was waived by Second Affiliated Hospital of Nanchang University.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eEpidemiology for IV CSCLC, IV SCLC and IV NSCLC\u003c/h2\u003e\n\u003cp\u003eThe semi-logarithmic line chart was used to describe the number of cases per year from 2000 to 2020 per year in the three groups. It can be seen in the figure that the number of patients in NSCLC was the highest, followed by SCLC, and the number of patients in CSCLC was the lowest. The number of patients in NSCLC kept increasing from 2004 to 2017, but was generally stable. However, the incidence of CSCLC and SCLC did not fluctuate significantly (Fig.\u0026nbsp;3,A).\u003c/p\u003e\n\u003cp\u003eStacked Bar Chart was used to perform total number of patients, IV stage patients and the percentage of stage IV patients in three groups. The percentage of stage IV patients in SCLC was the highest, followed by CSCLC, and the number of patients in NSCLC was the lowest (Fig.\u0026nbsp;3,B).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eBasic characteristics of patients for IV CSCLC, IV SCLC and IV NSCLC before PSM\u003c/h2\u003e\n\u003cp\u003eThere were stage IV CSCLC (n\u0026thinsp;=\u0026thinsp;493), stage IV SCLC (n\u0026thinsp;=\u0026thinsp;35503) and stage IV NSCLC (n=\u0026thinsp;122807) groups in this study. Demographics and clinicopathologic characteristics of patients are shown in Table\u0026nbsp;1. Age of \u0026ge;\u0026thinsp;65 (67.3%), married (50.7%), upper lobe (49.1%), right (53.1%), T4 (44.8%), N2 (44.4%) and N3 (24.5%) were common in IV CSCLC patients. Gender, race, primary site, T stage, N stage, liver metastasis, lung metastasis, surgery, and radiotherapy were significantly different between stage IV CSCLC and stage IV SCLC group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Race, primary site, N stage, bone metastasis, liver metastasis, lung metastasis and chemotherapy were significantly different in stage IV CSCLC and NSCLC group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eLung metastasis (25.6% vs 19.9%) was more common and liver metastases (31.8% vs 47.7%) was less common in IV CSCLC than in SCLC. Liver metastasis(31.8%VS 17.2%)was more common, lung metastasis (25.6% vs 31.4%) and bone metastases (34.3% vs 40.3%) were less common in IV CSCLC than in NSCLC. More IV CSCLC patients chose chemotherapy (64.1% vs 52.1%) than IV NSCLC patients. More IV CSCLC patients chose surgery (5.1% vs 0.9%) and radiotherapy (43.2% vs 37.3%) than IV SCLC patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eBasic characteristics of patients for IV CSCLC, IV SCLC and IV NSCLC after PSM\u003c/h2\u003e\n\u003cp\u003eRandom 1:1 nearest-neighbor PSM without replacement to balance all baseline covariates between CSCLC, SCLC and NCSCLC. 486 patients and 493 patients in CSCLC group were selected separately after twice PSM. The stage IV SCLC group (n\u0026thinsp;=\u0026thinsp;486) and stage IV NSCLC group (n\u0026thinsp;=\u0026thinsp;493) were selected for further analysis after PSM. The baseline features after PSM were well-balanced (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05 and SMD\u0026lt;0.1, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eKM analysis for IV CSCLC, IV SCLC and IV NSCLC\u003c/h2\u003e\n\u003cp\u003eKM analysis was used to compare OS or CSS in the stage IV CSCLC, SCLC, and NSCLC groups (Fig.\u0026nbsp;4). For the cohorts before PSM, the mOS and mCSS of IV CSCLC were both 6.0 months, IV SCLC patients presented a mOS of 5.00 months and a mCSS of 6 months. The mOS and mCSS of NSCLC patients were 6 months and 7 months. There were statistically significant difference in OS (p\u0026thinsp;=\u0026thinsp;0.013,) and CSS (p\u0026thinsp;=\u0026thinsp;0.014) between stage IV CSCLC and stage IV SCLC groups (Fig.\u0026nbsp;4, A,B), So did the OS (p\u0026lt;0.001) and CSS (p\u0026lt;0.001) between stage IV CSCLC and stage IV NSCLC group (Fig.\u0026nbsp;4, E,F).\u003c/p\u003e\n\u003cp\u003eAfter PSM, the mOS and mCSS of CSCLC patients were both 6.00 months, and the SCLC were both 5.00 months, which were still poorer than the mOS and mCSS of the CSCLC patients (5.00 VS 6.00 months, Fig.\u0026nbsp;4, C,D). The mOS and mCSS of NSCLC were same as before PSM (6 months and 7 months), the mCSS of NSCLC were longer than CSCLC (7 months VS 6 months). There were statistically significant difference in OS (p\u0026thinsp;=\u0026thinsp;0.003) and CSS (p\u0026thinsp;=\u0026thinsp;0.006) between stage IV CSCLC and stage IV NSCLC groups (Fig.\u0026nbsp;4, G,H).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eUnivariable Cox analysis for IV CSCLC, IV SCLC and IV NSCLC\u003c/h2\u003e\n\u003cp\u003eBefore PSM, the results of univariate Cox analysis showed that age, race, N stage, bone metastasis, liver metastasis, surgery, radiotherapy, chemotherapy were significantly associated with OS and CSS in three groups (p\u0026lt;0.05, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig.\u0026nbsp;5). After PSM, age, liver metastasis, N stage, radiotherapy, chemotherapy were significantly associated with OS in three groups; besides, race, bone metastasis, surgery were significantly associated with OS in stage IV CSCLC patients (p\u0026lt;0.05, Table S2, Fig.\u0026nbsp;6, A,C,E). Chemotherapy and radiotherapy were common associated with CSS in the three groups (p\u0026lt;0.05). Race and T stage were correlated with CSS of IV SCLC and IV CSCLC, age, N stage, liver metastasis and surgery were common associated with CSS of IV NSCLC and CSCLC (p\u0026lt;0.05, Table S2, Fig.\u0026nbsp;6, B,D,F ).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eMultivariate Cox analysis for IV CSCLC, IV SCLC and IV NSCLC\u003c/h2\u003e\n\u003cp\u003eThe results of independent influencing factors of three groups were described by forest plot (Fig.\u0026nbsp;5 and Fig.\u0026nbsp;6) and the correlation of influencing factors among three groups were described by Venn diagram (Fig.\u0026nbsp;7).Before PSM, race, primary site, N stage, bone metastasis, liver metastasis, surgery, chemotherapy and radiotherapy were common independent risk/ protective factors for both CSS and OS of three groups, besides age was also the independent risk factor for OS of three groups. Gender, married status, T stage, brain metastasis, lung metastasis were common independent risk/ protective factors for OS and CSS of SCLC and NSCLC(P\u0026lt;0.05, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;5 and Fig.\u0026nbsp;7A,B ).\u003c/p\u003e\n\u003cp\u003eThe results of multivariate Cox analysis with stage IV CSCLC after PSM were similar with the results before PSM: bone metastasis and liver metastasis were independent risk factor, surgery, chemotherapy and radiotherapy were the independent protective factors for both CSS and OS, and age was also the independent risk factor for OS. Liver metastasis was the common independent risk factor for OS of three groups. Chemotherapy was the protective factors for CSS and OS of three groups (p\u0026lt;0.05). Surgery was the independent protective factor for CSS and OS of CSCLC and NSCLC. Radiotherapy was the common protective factor for CSS of CSCLC and SCLC (P\u0026lt;0.05, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;6 and Fig.\u0026nbsp;7C, D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003ePrognosis of each treatment modality in IV CSCLC patients\u003c/h2\u003e\n\u003cp\u003eTo assess the prognostic impact of each treatment modality on patients with IV CSCLC, we compared treatment outcomes with radiotherapy, chemotherapy and surgery. The mOS of surgery was 10.0 months (Fig.\u0026nbsp;8A), radiotherapy was 7.0 months (Fig.\u0026nbsp;8C), chemotherapy was 8.0 months (Fig.\u0026nbsp;8E), The KM analysis showed that radiotherapy, chemotherapy and surgery could improve the survival probability of IV CSCLC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mCSS of surgery, radiotherapy and chemotherapy were 10.0 months, 7.0 months and 9.0 months (Fig.\u0026nbsp;8B,D,F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eEvaluation of different treatment modalities of IV CSCLC, IV SCLC and IV NSCLC\u003c/h2\u003e\n\u003cp\u003eTo identify the effect of treatment modalities on OS and CSS for three groups, patients were divided into eight groups according to treatment modalities before PSM: Control: patients were not treated with radiotherapy, chemotherapy or surgery since being diagnosed. Surgery: patients were treated with surgery alone. Chemotherapy: patients were treated with chemotherapy alone. Radiotherapy: patients were treated with radiotherapy alone. Chemoradiotherapy: patients were both treated with radiotherapy and chemotherapy. Surgery\u0026thinsp;+\u0026thinsp;chemotherapy: patients were treated with surgery and chemotherapy. Surgery\u0026thinsp;+\u0026thinsp;radiotherapy: patients were treated with surgery and radiotherapy (data missing in CSCLC group). Surgery\u0026thinsp;+\u0026thinsp;chemoradiotherapy: patients were treated with surgery, chemotherapy and radiotherapy. Characteristics of CSCLC, SCLC, NSCLC were shown in Table S3 to Table S5.\u003c/p\u003e\n\u003cp\u003eKM analysis was used to compare the difference in survival probability between patients with different treatment modalities (Fig.\u0026nbsp;9) and mOS/mCSS in different groups were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The mOS and mCSS of surgery\u0026thinsp;+\u0026thinsp;chemotherapy group were 10 months and 16 months respectively, with a better survival probability than other groups in IV CSCLC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Besides, chemotherapy alone had similar OS and CSS with surgery\u0026thinsp;+\u0026thinsp;chemotherapy, chemoradiotherapy, and surgery\u0026thinsp;+\u0026thinsp;chemoradiotherapy groups. The mOS and mCSS of Surgery\u0026thinsp;+\u0026thinsp;chemoradiotherapy group were 13 months and 14 months in IV SCLC, which had a higher probability of survival than the other treatments (p\u0026lt;0.001). In IV NSCLC group, Surgery\u0026thinsp;+\u0026thinsp;chemotherapy group had better mOS (29 months)and mCSS (35 months)than other treatment modalities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCSCLC is a rare histological type of lung cancer. In this study, we retrieved the data of CSCLC patients from the SEER database in the past two decades (2194 cases), accounting for 2.2% of the total number of lung cancer patients (100,753 cases). The number of annual incidence has been maintained at around 100 cases, which is consistent with the incidence rate of CSCLC in previous studies ranging from 1\u0026ndash;14%. The highest detection rate of 14.3% was reported by Fushimi et al, the study found that the detection rate by autopsy was significantly higher than that by biopsy or other cytological methods\u003csup\u003e15\u003c/sup\u003e, the main detection method in the SEER database is not autopsy, so the detection rate may be low. The proportion of IV CSCLC patients is 38.33%, which is between that of IV NSCLC (35.22%) and IV SCLC (49.44%). Since some studies has been confirmed that CSCLC has histological features in common with NSCLC and SCLC\u003csup\u003e3,6\u0026ndash;8,16\u003c/sup\u003e, it is necessary to further explore the differences in clinical features, prognostic factors, and treatment methods among the three groups.\u003c/p\u003e \u003cp\u003eOur study suggests that most advanced CSCLC cases are elderly patients, with a higher incidence in the upper lobe and right lung. There are significant differences in clinical characteristics between advanced CSCLC, NSCLC, and SCLC, which are reflected in demographic and tumor features. The probability of liver and lung metastasis in IV CSCLC is between that of IV NSCLC and IV SCLC. This may be due to better treatment outcomes in IV CSCLC, as CSCLC patients are more willing to undergo surgery and chemoradiotherapy compared to the other two groups. However, despite there being no significant difference in survival time (OS and CSS) between IV CSCLC and IV SCLC after PSM, the Kaplan-Meier curve trend and before PSM results suggest that the survival of IV CSCLC is slightly better than that of IV SCLC. Additionally, both before and after PSM, the survival of IV CSCLC is shorter than that of IV NSCLC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is consistent with previous studies of CSCLC in I-IV stages \u003csup\u003e5,17\u003c/sup\u003e. For this situation, SCLC is considered to have the worst prognosis and the highest degree of malignancy among all lung cancer tissue types, especially in the advanced stage\u003csup\u003e18\u003c/sup\u003e, it has also been proposed that CSCLC is more closely related to SCLC than to NSCLC\u003csup\u003e19\u003c/sup\u003e, the SCLC component is a negative prognostic factor in CSCLC, and there may be a correlation between the proportion of SCLC component and prognosis\u003csup\u003e20\u003c/sup\u003e, due to CSCLC is mixed with some components of NSCLC, the survival time will be in the middle of the SCLC and NSCLC.\u003c/p\u003e \u003cp\u003eSubsequently, univariate and multivariate COX analysis were performed on the three groups. Bone metastasis and liver metastasis were identified as independent risk factors for prognosis in all three groups. However, brain metastasis and lung metastasis were identified as independent risk factors for SCLC and NSCLC, but not for CSCLC. In the study by Zhang et al\u003csup\u003e10\u003c/sup\u003e, it was believed that liver and lung metastases were not prognostic factors for CSCLC patients. However, that study only included patients with CSCLC in stages I-IV, so it can be speculated that liver metastasis is only an independent risk factor in advanced CSCLC patients. For SCLC and NSCLC, liver, brain, bone, and lung metastases are all independent risk factors. Previous studies have suggested that SCLC patients with liver metastases had the unfavorable prognosis\u003csup\u003e21\u003c/sup\u003e, In our study, liver metastasis in advanced CSCLC may also indicate poor prognosis. We speculate that the survival time of IV CSCLC is between CSCLC and NSCLC, it may be that the probability of liver metastasis in advanced CSCLC is lower than that in SCLC, so the prognosis of IV CSCLC is slightly better than that in IV SCLC, while the probability of liver metastasis in advanced CSCLC is higher than that in NSCLC, so the prognosis is worse than that in NSCLC. Bone metastasis is also an important risk factor for the prognosis of CSCLC, which can affect CSS and OS of CSCLC. Therefore, our study suggests that liver and bone metastasis in patients with advanced CSCLC may indicate poor prognosis.\u003c/p\u003e \u003cp\u003eBefore PSM, all treatment methods (radiotherapy, chemotherapy, surgery) were independent protective factors for all three groups of patients. After PSM, the three treatment methods were independent protective factors for IV CSCLC, but only chemotherapy was an independent protective factor for OS and CSS for all three groups. The independent protective factors for NSCLC were surgery and chemotherapy, while for SCLC were radiotherapy and chemotherapy. This may be the small number of cases included in the NSCLC and SCLC groups after PSM, or because the SCLC patients included were generally at a later stage and unable to undergo surgery. However, it can be considered that chemotherapy was an important protective factor for all three groups of patients. Surgery and radiotherapy may be dependent on the patient's condition for advanced NSCLC and SCLC. As all three treatment methods were independent protective factors for CSS and OS in CSCLC, we performed separate KM analyses for each treatment method. We found that all three treatment methods significantly prolong survival time and have statistical significance. Therefore, CSCLC patients are more willing to receive surgery and chemoradiotherapy compared to the other two groups. Among the three treatment methods, surgery has the longest survival time, followed by chemotherapy. It is worth noting that age, primary site, and N staging may also affect the prognosis of CSCLC. Therefore, for patients with advanced CSCLC, it is also important to pay particular attention to the age of patients, primary site, and the N staging.\u003c/p\u003e \u003cp\u003eDue to the rare histological type of CSCLC among lung cancers, there is limited real-world data, so there are no detailed guidelines for the treatment of CSCLC \u003csup\u003e22,23\u003c/sup\u003e, especially for advanced CSCLC. We conducted separate KM analysis for each treatment modality in patients with advanced CSCLC, SCLC, and NSCLC. In this study, the combination of surgery and chemotherapy was found to have the best efficacy in patients with advanced CSCLC, consistent with the latest ESMO clinical practice guidelines: for patients suspected of having CSCLC, surgical treatment may be considered \u003csup\u003e22\u003c/sup\u003e. However, for some advanced patients who have already lost the opportunity for surgery at the time of first diagnosis, our study suggests that chemotherapy alone is also a good choice, with the median survival time second only to combined treatment. For patients with advanced SCLC and NSCLC, combined treatment with surgery, radiotherapy, and chemotherapy is more effective. Since CSCLC has components of both SCLC and NSCLC, can we draw lessons from the treatment protocols of NSCLC or SCLC? A study by Luo et al. found that CSCLC patients who received the classic chemotherapy regimen for SCLC (etoposide and cisplatin) presented a survival benefit\u003csup\u003e24\u003c/sup\u003e, this study retrospectively compared the efficacy of the NIP (navelbine\u0026thinsp;+\u0026thinsp;ifosfamide\u0026thinsp;+\u0026thinsp;cisplatin) and EP (etoposide\u0026thinsp;+\u0026thinsp;cisplatin) regimens as first-line treatment options for III-IV stage CSCLC, the results suggest that the EP regimen may provide better survival benefits compared to the NIP regimen. However, some studies have mentioned that NSCLC is less sensitive to the EP regimen, and CSCLC contains NSCLC components, so the clinical benefits of using the EP regimen for CSCLC may not be as effective as for SCLC\u003csup\u003e14\u003c/sup\u003e. EGFR-TKIs are widely used in NSCLC with EGFR mutations, but there have been no randomized clinical trials to evaluate their efficacy in the treatment of CSCLC, only small case series studies have suggested that EGFR-TKIs may be helpful in the treatment of CSCLC and SCLC, but the efficacy of EGFR-TKIs in the treatment of CSCLC or SCLC may not be as good as in NSCLC\u003csup\u003e25,26\u003c/sup\u003e. In summary, for patients with advanced CSCLC, if there are indications for surgery, the combination of surgery and chemotherapy should be given priority. If the opportunity for surgery has been lost, the chemotherapy regimen for SCLC can be referred to.\u003c/p\u003e \u003cp\u003eThere are some limitations to our study. Firstly, the SEER database does not provide information on some patient characteristics such as smoking, past medical history, specific chemotherapy and radiotherapy regimens, immunotherapy and targeted therapy, which may affect our results. Secondly, no further molecular level analysis and comparison were performed among the three groups. Thirdly, due to the limitations of diagnostic techniques and the low incidence and high mortality of CSCLC, we were unable to provide information on IV stage CSCLC patients from our own database. Future prospective studies are needed to further explore the relationship between CSCLC and NSCLC, SCLC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the clinical features of IV CSCLC differ from those of IV SCLC and IV NSCLC, we found that the prognosis of IV CSCLC patients was significantly worse than that of IV NSCLC patients, but better than IV SCLC patients. Additionally, surgery combined chemotherapy was the best efficacy in patients with IV CSCLC and chemotherapy regimen can referred to IV SCLC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoqun Ye designed the conception and methodology. Zhouhua Li, Xiaotian Huang, Jinbo Li and Hongdan Luo performed the data analysis. Shanshan Cai and Weichang Yang wrote the original draft. All authors critically revised the manuscript for intellectual content. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 81660493), the Natural Science Foundation of Jiangxi Province (Grant No.20202ACBL206019) and Jiangxi Province Graduate Innovation Special Fund (YC2023-B083).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following information was supplied regarding data availability: The raw data is available from the SEER database (https://seer.cancer.gov), informed consent was waived by Second Affiliated Hospital of Nanchang University.\u003c/p\u003e\n\u003cp\u003eDisclosures Shanshan Cai, Weichang Yang, Zhouhua Li, Xiaotian Huang, Jinbo Li, Hongdan Luo, Dr. Xiaoqun Ye have no potential conflicts of interest or financial ties to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. 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EGFR mutations in small-cell lung cancers in patients who have never smoked. The New England journal of medicine 2006;355:213-5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cimg 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\" alt=\"\" width=\"1175\" height=\"44\" /\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003eCSCLC\u003c/strong\u003e, N = 493\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003eSCLC\u003c/strong\u003e, N = 35503\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e, N = 122807\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge.years.\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e＜65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e161 (32.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e13,024 (36.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e42542 (34.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u0026ge;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e332 (67.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e22,479 (63.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e80265 (65.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e289 (58.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e18,409 (51.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e66604 (54.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e204 (41.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e17,094 (48.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e56203 (45.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e57 (11.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2,981 (8.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e14930 (12.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e405 (82.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e30,976 (87.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e95575 (77.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e26 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1,288 (3.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e11610 (9.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e258 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e692 (0.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarried.status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.256\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e250 (50.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e17,082 (48.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e61977 (50.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eDivorced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e70 (14.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5,070 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e14754 (12.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e173 (35.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e13,351 (37.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e46076 (37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary.Site\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eMain bronchus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e36 (7.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3,916 (11.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e5158 (4.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eUpper lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e242 (49.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e15,480 (43.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e59390 (48.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eMiddle lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e14 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1,249 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e4940 (4.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eLower lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e112 (22.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7,112 (20.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e31467 (35.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e89 (18.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7,746 (21.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e21852 (17.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eLaterality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.655\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eLeft\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e196 (39.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e14,100 (39.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e47460 (38.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eRight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e262 (53.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e18,625 (52.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e67296 (54.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e35 (7.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2,778 (7.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e8105 (6.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eT.stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eT0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5 (1.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e461 (1.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1101 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e41 (8.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3,200 (9.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e13771 (11.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e121 (24.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7,204 (20.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e28899 (23.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e55 (11.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3,320 (9.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e13246 (10.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e221 (44.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e15,924 (44.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e50519 (41.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eTX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e50 (10.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5,394 (15.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e15271 (12.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eN.stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e95 (19.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e4,379 (12.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e30248 (24.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e33 (6.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2,305 (6.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e9475 (7.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e219 (44.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e17,845 (50.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e49169 (40.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e121 (24.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e8,828 (24.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e25870 (21.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e25 (5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2,146 (6.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e8045 (6.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eBone.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e169 (34.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e12,667 (35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e49431 (40.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e324 (65.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e22,836 (64.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e73376 (59.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eBrain.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e140 (28.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e8,870 (25.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e32155 (26.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e353 (71.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e26,633 (75.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e90652 (73.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eLiver.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e157 (31.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e16,940 (47.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e21077 (17.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e336 (68.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e18,563 (52.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e101730 (82.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eLung.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e126 (25.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7,076 (19.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e38568 (31.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e367 (74.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e28,427 (80.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e84239 (68.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e25 (5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e329 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e4250 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e468 (94.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e35,174 (99.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e118557 (96.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.887\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e213 (43.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e13,259 (37.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e52543 (42.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e280 (56.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e22,244 (62.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e70264 (57.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e316 (64.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e23,723 (66.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e64004 (52.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"180\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e177 (35.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e11,780 (33.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e58803 (47.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"998\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%) \u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test; Fisher's exact test \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Demographics and clinicopathologic characteristics of patients with CSCLC ,SCLC and NSCLC after PSM\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCSCLC, N\u0026thinsp;=\u0026thinsp;486\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSCLC, N\u0026thinsp;=\u0026thinsp;486\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCSCLC, N\u0026thinsp;=\u0026thinsp;493\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNSCLC, N\u0026thinsp;=\u0026thinsp;493\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge.years.\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.733\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160 (32.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161 (32.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156 (31.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326 (67.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e340 (70.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e332 (67.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e337 (68.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.747\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e283 (58.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e288 (59.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e289 (58.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284 (57.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e203 (41.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198 (40.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e204 (41.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209 (42.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (11.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e400 (82.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e410 (84.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e405 (82.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e418 (84.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (3.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarried.status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.402\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e246 (50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255 (52.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e250 (50.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e269 (54.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDivorced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (14.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (14.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (12.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (35.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e164 (33.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165 (33.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary.Site\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.573\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMain bronchus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (7.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (5.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpper lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e237 (48.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e259 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e242 (49.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e246 (49.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (2.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLower lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (22.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (18.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (22.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (21.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (18.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91 (18.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90 (18.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (21.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLaterality\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeft\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195 (40.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (41.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196 (39.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194 (39.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256 (52.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256 (52.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e261 (52.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 (53.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (7.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (7.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT.stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.980\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (10.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (11.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e216 (44.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221 (44.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e223 (45.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (10.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN.stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91 (18.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (19.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95 (19.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (20.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (6.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (6.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (7.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e232 (47.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219 (44.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217 (44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (24.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114 (23.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBone.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e167 (34.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (35.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (34.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (34.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319 (65.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e313 (64.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e324 (65.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323 (65.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBrain.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (27.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143 (29.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e348 (71.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e351 (72.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e353 (71.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e350 (71.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLiver.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157 (32.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156 (32.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157 (31.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160 (32.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e329 (67.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330 (67.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e336 (68.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e333 (67.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLung.Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.942\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (25.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125 (25.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e362 (74.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e363 (74.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e367 (74.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e368 (74.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.275\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (3.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e468 (96.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e470 (96.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e468 (94.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e475 (96.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.521\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e210 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200 (41.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e223 (45.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e286 (58.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e280 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e270 (54.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.894\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e312 (64.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e318 (65.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e316 (64.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e318 (64.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174 (35.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168 (34.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e177 (35.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175 (35.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%) \u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test; Fisher's exact test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003eTable 4: Multivariable Cox analysis of OS and CSS in IV CSCLC, SCLC and NSCLC after PSM\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"12\" align=\"left\"\u003e\n\u003cp\u003eOS after PSM\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"18\" align=\"left\"\u003e\n\u003cp\u003eCSS after PSM\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eNSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eCSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHR(95CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHR(95CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHR(95CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eHR(95Cl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHR(95CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eHR(95CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAge.years.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.24 (1.00, 1.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.75(1.39, 2.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.58(1.27, 1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.21(0.97, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.25(0.70, 2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.56(1.24, 1.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.83 (0.67, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.87(0.71, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.84(0.69, 1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.098\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.85(0.68, 1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.84(0.47, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.547\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.86(0.70, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRace\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.28 (0.94, 1.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.08(0.78, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.642\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.99(0.71, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.949\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.36(0.98, 1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.03(0.44, 2.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.02(0.72, 1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.79 (1.07, 3.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.92(0.53, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.770\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.83(0.33, 2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.00(1.17,3.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.03(0.53, 7.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.9(0.36, 2.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.827\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.87 (0.33, 2.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.32(0.45, 3.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.48(0.25, 0.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.02(0.39, 2.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e5.37(1.16,24.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.49(0.24, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarried.status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDivorced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.03 (0.76, 1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.12(0.83, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.44(1.04, 1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.09(0.80, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.584\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.50(1.16, 5.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.42(1.02, 1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.90 (0.72, 1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.11(0.88, 1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.11(0.89, 1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.93(0.74, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.72(0.91, 3.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.01(0.79, 1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrimary.Site\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMain bronchus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUpper lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.77 (0.52, 1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.35(0.89, 2.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.55(0.36, 0.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.75(0.50, 1.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.11(0.32, 3.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.53(0.34, 0.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMiddle lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.72 (0.37, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.33(0.65, 2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.59(0.26,1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.66(0.33, 1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.83(0.47, 17.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.64(0.28, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLower lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.71 (0.47, 1.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.31(0.82, 2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.57(0.36, 0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.69(0.45, 1.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.45(0.65, 9.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.51(0.32, 0.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.59 (0.37, 0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.15(0.71, 1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.75(0.46, 1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.61(0.38, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.65(0.68,10.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.76(0.46, 1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.282\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLaterality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLeft\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.14 (0.93, 1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.97(0.79, 1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.98(0.79, 1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.09(0.88, 1.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.06(0.59, 1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.97(0.78, 1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.820\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.24 (0.78, 1.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.15(0.67, 1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.04(0.66, 1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.00(0.61, 1.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.87(0.19, 4.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.99(0.62, 1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT.stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.70 (0.26, 1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.81(0.41, 8.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.83(0.24, 2.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.60(0.22, 1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.87(0.10, 7.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.83(0.24, 2.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.768\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.65 (0.25, 1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.3(0.53, 9.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.14(0.34, 3.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.56(0.22, 1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.17(0.02, 1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.13(0.34, 3.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.842\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.67 (0.25, 1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.06(0.47, 9.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.338\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.85(0.25, 2.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.52(0.20, 1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.16(0.15, 9.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.83(0.24, 2.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.72 (0.28, 1.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.19(0.51, 9.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.20(0.37, 3.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.63(0.25, 1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.28(0.04, 2.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.23(0.37, 4.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.731\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.78 (0.30,2.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.11(0.49, 9.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.75(0.22, 2.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.67(0.25, 1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.33(0.05, 2.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.74(0.22, 2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN.stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.30(0.81, 2.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.74(0.46, 1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.89(0.58,1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.23(0.75, 2.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.411\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.48(0.85, 7.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.89(0.56, 1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.621\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.26(0.96, 1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.94(0.72, 1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.48(1.12, 1.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.31(0.98, 1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.01(0.93, 4.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.49(1.11, 2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.58(1.16, 2.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.03(0.75, 1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.13(0.83, 1.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.62(1.17, 2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.72(0.71, 4.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.15(0.83, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.402\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.03(1.24, 3.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.34(0.78, 2.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.70(1.02, 2.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.90(1.12, 3.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.99(0.13, 7.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.57(0.91, 2.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBone.Metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.31(1.06, 1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.13(0.91, 1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.21(0.98, 1.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.34(1.07, 1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.85(0.48, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.22(0.98, 1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.079\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBrain.Metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.26(0.97, 1.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.36(1.07, 1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.32(1.03, 1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.23(0.94, 1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.25(0.66, 2.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.37(1.06, 1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLiver.Metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.46(1.17, 1.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.75(1.40, 2.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.37(1.11, 1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.42(1.13, 1.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.69(0.91, 3.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.51(1.21, 1.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLung.Metastasis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.12(0.89, 1.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.16(0.92, 1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.08(0.85, 1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.04(0.82, 1.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.730\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.26(0.69, 2.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1.03(0.80, 1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.789\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSurgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.44(0.25, 0.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.89(0.49, 1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.35(0.19, 0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.42(0.23, 0.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.64(0.45, 6.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.37(0.20, 0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRadiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.68(0.54, 0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.71(0.57, 0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.88(0.70, 1.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.69(0.54, 0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.7(0.38, 1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.88(0.69, 1.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.268\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(0.22, 0.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.31(0.24, 0.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.32(0.25, 0.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.31(0.24, 0.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.44(0.23, 0.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.31(0.25, 0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 : The mOS and mCSS of different therapy methods in IV CSCLC, SCLC and IV SCLC\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTherapy method\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eGroup\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCSCLC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSCLC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNSCLC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emOS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emCSS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emOS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emCSS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emOS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003emCSS\u003c/p\u003e\n\u003cp\u003e(months)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemoradiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;chemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;radiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u0026thinsp;+\u0026thinsp;chemoradiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"combined small cell lung cancer, small cell lung cancer, non-small cell lung cancer, advanced, propensity score matching analysis, SEER database","lastPublishedDoi":"10.21203/rs.3.rs-3939208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3939208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNowadays, the characteristics and treatment of combined small-cell lung carcinoma (CSCLC) remain controversial. This study aimed to analyze the feature of clinical characteristics, survival outcomes and treatment modalities among IV CSCLC, IV SCLC and IV NSCLC, to provide more evidence for the study of IV CSCLC\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAll CSCLC, SCLC and NSCLC patient data were obtained from the SEER database (2010\u0026ndash;2020). Pearson's χ2 test was used to compare the differences in clinical characteristics. Propensity score matching (PSM) was utilized to balance the bias of the variables between patients. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify prognostic factors. KM analysis was used to calculate survival.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 493 patients with IV CSCLC, 35503 patients with SCLC, 122807 patients with IV NSCLC were included in this study. The demographic characteristics and tumor characteristics of three groups were different. Before PSM, there were significant difference in OS and CSS among IV CSCLC, IV SCLC and IV NSCLC, After PSM, there was significant difference in OS and CSS between the IV CSCLC and IV NSCLC. Risk/protective factors for OS and CSS were different in three groups. Chemotherapy, radiotherapy, surgery all can improve survival time of IV CSCLC. Chemotherapy combine surgery can significantly improve OS and CSS in patients with IV CSCLC (10.0 months and 16.0 months), chemotherapy alone was also a good choice for some IV CSCLC patients who have already lost the opportunity for surgery at the time of first diagnosis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results indicated that the prognosis and clinical characteristics IV CSCLC, IV SCLC and IV NSCLC were significant difference. Surgery combined chemotherapy was the best treatment in patients with IV CSCLC and chemotherapy alone was a good choice for patients who have lost the indication of surgery.\u003c/p\u003e","manuscriptTitle":"Clinical features and prognostic factors of IV combined small cell lung cancer: A Propensity Score Matching Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 20:46:16","doi":"10.21203/rs.3.rs-3939208/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66af0408-ac22-4882-be5e-3202aef56fce","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28960044,"name":"Biological sciences/Cancer/Lung cancer/Non small cell lung cancer"},{"id":28960045,"name":"Biological sciences/Cancer/Lung cancer/Small cell lung cancer"},{"id":28960046,"name":"Biological sciences/Cancer/Cancer therapy"}],"tags":[],"updatedAt":"2024-07-22T05:00:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 20:46:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3939208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3939208","identity":"rs-3939208","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}