Significance of metastatic lymph nodes ratio in overall survival for patients with resected Non–Small-Cell Lung Cancer

Significance of metastatic lymph nodes ratio in overall survival for patients with resected Non–Small-Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Significance of metastatic lymph nodes ratio in overall survival for patients with resected Non–Small-Cell Lung Cancer Xiaoping Lin, Jianfeng Yao, Baoshan Huang, Tebin Chen, Rongfu Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2617566/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: TNM stage is widely applied to classify lung cancer and the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough especially for the N status. In this study, we aim to build a convenient survival prediction model that incorporated the current items of lymph node status. Methods: We collected data of resectable NSCLC(IA-IIIB) patients from Surveillance, Epidemiology, and End Results (SEER) database (2006-2015). X-tile program was applied to calculate the optimal threshold of metastatic lymph nodes ratio (MLNR). Then, independent prognostic factors were determined by multivariable cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the concordance index(C-index ) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph nodes number (ELNs) were presented in subgroups. Results: 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph nodes ratio, histology type, adjuvant treatment, and AJCC 8 th T stage were deemed as independent prognostic parameters after multivariable cox regression analysis. Nomogram was built using those variables and its efficiency in predicting patients’ survival was better than the conventional AJCC stage system after evaluation. Our new model has a significant higher concordance index(C-index) (training set,0.683 v 0.641, respectively; P<0.01; testing set, 0.676 v 0.638, respectively; p<0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observation in both cohorts. And then, after risk stratification, we found MLNR is more reliable than ELNs in predicting overall survival(OS). Conclusions: We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used TNM staging system and could benefits clinicians in treatment options and cancer control. non-small cell lung cancer tumor-node-metastasis lymph node Stage prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung cancer remains the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer(NSCLC) accounting for approximately 85% of all diagnosed patients[ 1 ]. Although the widespread application of advanced imaging techniques has witnessed a dramatic increase in early detection, for early-stage NSCLC patients who undergo surgical excision, the 5-year survival rates are only 30%~75%[ 2 ]. Histologically confirmed lymph node(LN) metastases are important prognostic factors and also essential components of the widely used tumor, node, metastasis (TNM) stage classification. However, patients in the same stage often show different survival time[ 3 , 4 ]. In the updated eighth edition of the TNM classification of NSCLC, LN stages are the same as those in the previous edition and based solely on the anatomical location of LN involvement[ 5 ]. Several previous studies have pointed out that the count of positive lymph nodes and examined lymph nodes can reveal the effectiveness of lymphadenectomy then oncological outcomes[ 6 , 7 ]. Other researchers also found the ratio of metastatic lymph nodes ratio (MLNR) is a prognostic factor in many tumors, including esophageal carcinoma[ 8 ] and medullary thyroid cancer[ 9 ]. Nomogram is a reliable tool to quantify risk which can reduce statistical predictive models into a single numerical estimate of the probability of an event[ 10 ]. By creating precise prediction, that is to say, combining independent prognostic factors such as patients’ age, histology type, and other potential prognostic factors, it can provide essential information to therapy regime option and cancer control. However, nomograms for predicting long-term survival outcomes for NSCLC patients after surgery are scarce. In this study, we are going to explore the ability of examined lymph nodes number (ELNs) and MLNR to predict prognosis for NSCLC patients after surgery employing a cohort from population-based Surveillance, Epidemiology, and End Results (SEER) program. By comparing it with conventional TNM staging criteria, we show that nomograms can confer better survival prediction. Methods Data source and collection All data were collected from the SEER database. It is supported by the Surveillance Research Program which provides national leadership in the science of cancer surveillance as well as analytical tools and methodological expertise in collecting, analyzing, interpreting, and disseminating reliable population-based statistics[ 11 ]. NSCLC cases between 2006 and 2015 in the SEER public access database and their corresponding details were identified with the use of SEER*Stat version 8.1.5 software. Patients were uniformly reviewed and staged according to the eighth edition of the TNM classification. The extent of surgery was analyzed by regional nodes positive (1988+) and RX Summ-Surg Prim Site (1998+)[ 12 ]. Only patients diagnosed with NSCLC who underwent radical resection were enrolled in the study. Patients were eliminated when some of the criteria meet: a) patients with local advanced or metastatic disease (TNM stage IIIB or stage IV). b) more than one primary tumor c) missing information on extracted variables. d) fewer than one examined LN were eligible.) Demographic characters (age, gender), the number of metastatic lymph nodes, the number of examined lymph nodes, American Joint Committee on Cancer (AJCC) TNM staging system (8th edition), and oncological outcomes were identified from the SEER database. We deemed endpoint of NSCLC patients as the time from histological diagnosis to the date of death from lung cancer. Statistical analysis All statistical analysis was performed with R software ( www.r-project.org ) version 3.6.1 and RStudio ( www.rstudio.com ) version 1.1.456. Categorical variables were shown as frequency and percentage. Continuous variables were transformed into categorical variables based on recognized cutoff values (for age) or median number (examined lymph nodes lymph node station). The X-tile program was applied to calculate the optimal threshold for MLNR and risk levels with maximum specificity and sensitivity. The variables with P < 0.05 from the univariate analysis were chosen for the next step to built the Cox proportional hazards regression model to find risk factors linked to the prognosis of NSCLC. The nomogram prediction model was constructed based on chosen prognostic factors by multivariate cox regression analysis from the training set. Accuracy of nomograms was evaluated by Harrell’s concordance index (C-index) calculated through rcorrcens function in Hmisc package version 4.3-1. The predicted survival rates were compared with actual survival rates determined using a Kaplan-Meier analysis, and calibrations were generated in the training set and testing set trough calibrate function in rms package version 5.1-4. Bootstraps were used for these analyses at 1000 reiterations. Kaplan-Meier survival curves were constructed for chosen variables through ggsurvplot function included in package survminer version 0.4.6 and were compared with cox’s regression through coxph function included in package survival version 3.1-8. A comparison of the C-index of two different models was based on methods previously described[ 13 ]. Tests were two-sided and P < 0.05 was considered as statistically significant in all analyses mentioned above. Results Clinical characteristics of patients During 2006–2015, a total of 456090 NSCLC patients were collected. Patients without lymphadenectomy or clear ELNs and MLNR, recorded staging information, stage IIIB or IV disease, not a first tumor and distant metastases were excluded. According to the inclusion criteria, 40853 patients with a history of surgical treatment at the primary site were finally identified. Based on the eighth TNM staging system, the proportion of patients at T1, T2, T3 and T4 stages was 42.24%(n = 17255), 33.39% (n = 13653), 14.27% (n = 5837) and 10.06%(n = 4108), respectively. Most patients (n = 22767,55.729%) were of an adenocarcinoma histological type. The median ELNs were 8 (1 ~ 90), and the median MLNR was 0(range from 0 ~ 1). Among 4351 patients who accept adjuvant therapy, 1081 patients received radiotherapy, 2225 patients received chemotherapy and 1045 received both. Detailed information on demographic features and clinicopathological characteristics are presented in Table 1 . Table 1 Clinical characteristics of training and testing set of AJCC 8th staging IA to IIIB NSCLC patients Characteristics Total [n (%)] Training Cohort [n (%)] Validation Cohort [n (%)] P Value Age ≤ 60 10909(26.7) 7799(26.7) 3110(26.6) 0.949 60–70 14758(36.1) 10549(36.2) 4209(36.1) ༞ 70 15186(37.1) 10833(37.1) 4353(37.3) Sex Female 21170(51.8) 15112(51.8) 6058(51.9) 0.842 Male 19683(48.1) 14069(48.2) 5614(48.1) Race White 33903(82.98) 24255(83.1) 9648(82.7) 0.405 Black 3729(9.12) 2629(9) 1100(9.4) Other 3221(7.88) 2297(7.9) 924(7.9) Assistant Treat Neither 22767(55.729) 20070(68.8) 8018(68.7) 0.985 Radiotherapy 1081(2.64) 711(2.4) 279(2.4) Chemotherapy 2225(5.44) 5499(18.8) 2214(19) Both 1045(2.55) 2901(9.9) 1161(9.9) Pathology Type ADC 1134(2.77) 16283(55.8) 6484(55.6) 0.254 ADSC 1969(4.81%) 736(2.5) 345(3) BAC 10632(26.02) 1578(5.4) 647(5.5) LCC 30628(74.97) 753(2.6) 292(2.5) NSCLC (unspecified) 6643(16.26) 815(2.8) 319(2.7) Other 3582(8.76) 1428(4.9) 541(4.6) SCC 22356(54.72) 7588(26) 3044(26.1) MLNR Median(range) 8(0–90) 8(0–90) 8(0–90) 0.432 ELNs median(range) 0(0–1) 0 (0–1) 0(0–1) 0.586 AJCC 8th T T1a 1687(4.13) 1199(4.1) 488(4.2) 0.957 T1b 8744(21.41) 6255(21.4) 2489(21.3) T1c 6824(16.70) 4862(16.7) 1962(16.8) T2a 10567(25.86) 7583(26) 2984(25.6) T2b 3086(7.53) 2185(7.5) 901(7.7) T3 5837(14.28) 4159(14.3) 1678(14.4) T4 4108(10.06) 2938(10.1) 1170(10) AJCC: The American Joint Committee for Cancer; CI: confidence interval; HR: hazardous ratio; ELNs: examined lymph nodes number; MLNR: metastatic lymph nodes ratio; ADC: adenocarcinoma; ADSC: adenosquamous carcinoma; BAC: bronchi alveolar carcinoma; LCC: large cell carcinoma; SCC: squamous cell carcinoma; NSCLC (unspecified): non-small cell lung carcinoma (unspecified but not listed above); Other: other malignant tumors categories other than above and small cell lung cancer in SEER database. Cut-off points for metastatic lymph nodes ratio Patient cohorts were made up of a training set and a testing set. Those two groups were randomly generated with 29181 (71%) in the former set and 11672(29%) in the later one. The discrepancy of clinical and oncological characteristics between those two parts was very small with p > 0.05 (shown in Table 2 ). The optimal cut‑off value of MLNR(0, 0.31) in the training set was calculated using X‑tile(P-value < .001, Fig. 1 ). After enrolled all the variables into univariate and multivariate analysis, factors like MLNR (P < .001), age (P < .001), T stage (P < .001), pathology type (P < .001), treatment(p < .001) were deemed as independent prognostic factors. Table 2 Univariate and Multivariate Cox regression analyses of OS of AJCC 8th staging IA to IIIB NSCLC patients. Variables N (40853) Univariate Cox Multivariate Cox HR 95% CI P Value HR 95% CI P Value Age ≤ 60 10909(26.7%) 1 - - 1 - - 60–70 14758(36.1%) 1.275 1.222–1.329 < 0.001 1.328 1.273–1.386 < 0.001 ༞ 70 15186(37.1%) 1.779 1.710–1.852 < 0.001 1.924 1.847–2.006 < 0.001 Sex Female 21170(51.8%) 1 - - 1 - - Male 19683(48.1%) 1.522 1.476–1.569 < 0.001 1.361 1.319–1.404 0.052 Race White 33903(82.98%) 1 - - Black 3729(9.12%) 0.965 0.915–1.018 0.189 Other 3221(7.88%) 0.773 0.726–0.823 < 0.001 Treatment Neither 28088(68.75%) 1 - - 1 - - Radiotherapy 990(2.4233%) 2.397 2.215–2.595 < 0.001 1.54 1.421–1.67 < 0.001 Chemotherapy 7713(18.88%) 1.290 1.241–1.341 < 0.001 0.82 0.784–0.857 < 0.001 Both 4062(9.943%) 2.002 1.915–2.094 < 0.001 1.047 0.991–1.106 0.103 Pathology Type ADC 22767(55.729%) 1 - - 1 - - ADSC 1081(2.64%) 1.534 1.408–1.671 < 0.001 1.291 1.185–1.406 < 0.001 BAC 2225(5.44%) 0.638 0.592–0.689 < 0.001 0.756 0.704–0.816 < 0.001 LCC 1045(2.55%) 1.625 1.495–1.767 < 0.001 1.589 1.461–1.728 < 0.001 NSCLC (unspecified) 1134(2.77%) 1.456 1.344–1.578 < 0.001 1.284 1.184–1.392 < 0.001 Other 1969(4.81%) 0.876 0.808–0.948 0.001 0.909 0.839–0.985 0.02 SCC 10632(26.02%) 1.461 1.411–1.512 < 0.001 1.228 1.185–1.273 < 0.001 MLNR 0 30628(74.97%) 1 - - 1 - - 0-0.31 6643(16.26%) 1.858 1.789–1.930 < 0.001 1.752 1.678–1.829 < 0.001 ≥ 0.31 3582(8.76%) 2.698 2.581–2.820 < 0.001 2.52 2.399–2.648 < 0.001 ELNs ≥ 8 22356(54.72%) 1 - - - - - ༜ 8 18497(45.27%) 1.017 0.986–1.048 0.289 - AJCC 8th T T1a 1687(4.13%) 1 - - 1 - - T1b 8744(21.41%) 1.297 1.163–1.446 < 0.001 1.214 1.088–1.353 0.001 T1c 6824(16.70%) 1.791 1.607–1.996 < 0.001 1.534 1.376–1.71 < 0.001 T2a 10567(25.86%) 2.149 1.934–2.389 < 0.001 1.769 1.591–1.967 < 0.001 T2b 3086(7.53%) 2.775 2.479–3.108 < 0.001 2.116 1.887–2.371 < 0.001 T3 5837(14.28%) 3.121 2.803–3.476 < 0.001 2.389 2.142–2.664 < 0.001 T4 4108(10.06) 3.748 3.362–4.179 < 0.001 2.864 2.563–3.199 < 0.001 Construct a new model of survival prediction According to the outcome of multivariate analysis, patients’ age, MLNR, T stage, pathological type, and adjuvant treatment were identified as independent predictors. The above factors were applied to formulate a nomogram (Fig. 2 ). The final risk point was the sum of the score assigned to each independent parameter. Patients with higher age, lower MLNR, more tend to be large cell carcinoma histology type, radiotherapy only is more likely to get higher points which means poor prognosis. We can visually estimate the probability of death of patients individually through drawing a vertical line. The calibration plots showed a well-matched 1-, 3-, and 5-year OS between nomogram prediction and clinically observation whether in the training set or testing set (Fig. 3 A, B). Harrell’s C-index, a classical index used to evaluate model performance, in our model is still superior to the usual TNM category prediction. C-index of 0.683 (95% CI: 0.677–0.6878) and 0.676(95%CI: 0.648–0.665) was observed in those two patient cohorts. However, in the AJCC TNM stage system (8th), CI is 0.641(95% CI: 0.636–0.646) and 0.638(95% CI: 0.626–0.642) respectively (Table 3 ). Our new model is greater and this difference was statistically significant (P < 0.05). Table 3 C-index of nomogram and AJCC 8th Staging system. Category Nomogram AJCC 8th Stage P.Value (Nomogram vs AJCC 8th Stage) C-Index 95%CI P.Value C-Index 95%CI P.Value OS Training Set 0.6828 0.6777–0.6878 < 0.001 0.6413 0.6362–0.6464 < 0.001 < 0.001 Validation Set 0.6762 0.6485–0.6649 < 0.001 0.6348 0.6267–0.6429 < 0.001 0.020 Stratification of risk groups Based on the cut-off calculated via X-tile program ((Fig. 4 ), patients were grouped into three subgroups as a higher score means an incremental risk: low risk (score 0–13.9), moderate risk (score 14.0–21.9), and high risk (score 22.0–35.0). Significant distinction (p < 0.05) between Kaplan-Meier curves was observed in different groups (Fig. 5 A, B). Next, we stratified patients according to the risk level to identify how ELNs、MLNR and adjuvant therapy influence their prognosis. As for ELNs, we can see from Fig. 6 that, in the total(p = 0.29) and high risk(p = 0.55) group, there are no incremental benefits when the examined lymph node is higher. But for patients with low and moderate risk, it is essential to have more lymph nodes examined since it can improve their survival time greatly(p < 0.001). MLNR which considered the examined lymph node and positive lymph node presented as a very good predictor. Kaplan-Meier curves are significantly different among patients with different MLNR in the same risk stratification as we can see from Fig. 7 . All those above prove that our new model can provide efficient information about cancer control and management. Discussion Since the TNM staging system is not precise enough especially regarding lymph node status, we want to build a new predictive model to evaluate the survival of NSCLC patients. Although several researchers are trying to construct predictive models[ 14 , 15 ], a nomogram which takes full account of the lymph node for NSCLC operable patient is scarce. In our study, we established a model which combines the condition of examined and positive lymph node for surgically resected NSCLC. We obtained our data from the SEER database which includes cancer incidence and survival data from 18 population-based cancer registries throughout America, covering about 27.8% of the US population. Thus huge database empowers our model to find out potential efficient predictor factors that influence a patient’s survival. Patients in stage IV were out of consideration since for them surgery is not the first treatment option. In this retrospective study, we concentrate on the lymph node status on the respect that this item is an important component in the conventional AJCC stage system. Yanling has pointed out in their research that current TNM classification only states the anatomic extent of lymph node metastasis. They suggest positive lymph node count should be included especially for stage III patients[ 16 ]. Examined lymph node count is also cannot be neglect as reported in Wenhua’s study. They analyzed data from a Chinese multi-institutional registry and US SEER database and found ELN count is associated with improved outcomes in NSCLC[ 6 ]. They recommend 16 ELNs as the cut point for the evaluation of the quality of postoperative LN examination or prognostic stratification for patients with declared node-negative disease[ 6 ]. What is the difference from our study and the referred above is that we aimed to construct a nomogram which considered the condition of the number of examined lymph node and metastatic lymph nodes together. Using multivariable models, we are going to find that MLNR can reflect more-accurate node staging than other indexes like ELNs. Marc reported that the number of LNs is subject to normally distributed interindividual variability, with no significant impact on OS[ 17 ]. Likewise, through univariable analysis by cox regression, ELNs have no association with the survival time of NSCLC patients(p = 0.289) in our study. After stratified all the patients based on nomogram points, we managed to formulate 3 different risk level groups. Using the cut-off value screened from X-tile, patients with MLNR higher than 0.31 shows better long-term survival despite their risk grade. Meanwhile, only moderate and low-risk patients can benefit from a greater examined lymph node. More lymph node examined means more exhaustive elimination of remnants and timely impart adjuvant chemotherapy. But for high-risk NSCLC patients, as we know, adjuvant chemotherapy is imperative as long as there are signs of LN metastasis[ 18 ]. It is speculated that for this group of NSCLC patient, an increased ELNs doesn’t present as better survival time. The Benchmark of ELNs we determined is 8 which is different from others[ 19 ]. LN number we recorded may not reflect the true situation since it’s not easy in separating each LN after dissected and crushed nodal tissues often mistaken as several completed ones. Therefore, recommending an ideal ELNs maybe is not suitable. We also find patients with different histological types show different prognoses. Horiana recently reported in their study that the addition of histological subtype enhances traditional TNM classification to provide a more accurate risk assessment of patients with early-stage NSCLC[ 20 ]. After taking into account competing risks, those with squamous histology have a higher risk of mortality than those with adenocarcinoma histology[ 20 ]. Although the statistic method is different, we also found squamous histology shows an inferior prognosis than adenocarcinoma histology. Besides, we included histological types more than this two histology. The highest risk is large cell carcinoma while bronchi alveolar carcinoma with the minimum risk. We think to specify this element in our nomogram will make it more precise and reliable. Our study still has some limitations due to its retrospective nature. And interpret of those data should be cautious since the SEER database lacking some confounding factors like smoking, postoperative complications and access to perform limited resection (open or VATS). The results we get may be influenced to some extent and prospective studies are needed to further test the efficiency of our model. Conclusion All in all, to evaluate the survival time of NSCLC patients, it is not enough to just consider the anatomic extent of lymph node metastases. Here we performed a population-based study to summary clinic characteristics of NSCLC patients after surgery. Older age, higher NLPR, histology type, adjuvant treatment, and AJCC 8th stage were independent risk factors of NSCLC. Then elements build a nomogram that was performed greater than the AJCC conventional stage system. Through risk stratification we also able to speculate that NLPR is more suitable than ELNs to predict the survival of NSCLC patients especially for the high risk one. Abbreviations NSCLC non-small cell lung cancer TNM tumor-node-metastasis OS overall survival MLNR metastatic lymph nodes ratio C-index concordance index ELNs lymph nodes number LN lymph node SEER Surveillance, Epidemiology, and End Results AJCC American Joint Committee on Cancer CI confidence interval HR hazardous ratio ADC adenocarcinoma ADSC adenosquamous carcinoma BAC bronchi alveolar carcinoma LCC large cell carcinoma SCC squamous cell carcinoma Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by Grants from Natural Science Foundation of Fujian province(Grant No. 2019J01169) and Science and Technology Foundation of Quanzhou City (Grant No. 2022NS087) to Xiaoping Lin. Acknowledgments Not applicable. Author information Xiaoping Lin and Jianfeng Yaocontributed equally to this work. Authors and Affiliations Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China Xiaoping Lin Reproductive Medicine Centre,Quanzhou Maternity and Child Health Care Hospital, Quanzhou, 362000, China Jianfeng Yao Department of Clinical Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000,China Tebin Chen & Rongfu Huang Pediatrics, The Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, China Baoshan Huang Authors' contributions All authors contributed substantially to the development of the study. XL and JY designed the study, collected the data and wrote the main manuscript text. BH did the statistical and prepared table 1-3. TC did the statistical analysis and prepared figure 1-7. 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Impact of examined lymph node counts on survival of patients with stage IA non-small cell lung cancer undergoing sublobar resection. J Thorac Disease. 2018;10:6569–. Grosu HB, Manzanera A, Shivakumar S, Sun S, Noguras Gonzalez G, Ost DE. Survival disparities following surgery among patients with different histological types of non-small cell lung cancer. Lung Cancer. 2020;140:55–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2617566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":184565363,"identity":"b356e895-ec38-4bb4-bf1e-f424a757fbcf","order_by":0,"name":"Xiaoping Lin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Lin","suffix":""},{"id":184565366,"identity":"ff6b7248-acab-4f66-a47f-d0d82a697fd2","order_by":1,"name":"Jianfeng Yao","email":"","orcid":"","institution":"Quanzhou Maternity and Child Health Care Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Yao","suffix":""},{"id":184565369,"identity":"f793bfa9-f4bf-48a0-a721-a53215f55190","order_by":2,"name":"Baoshan Huang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Baoshan","middleName":"","lastName":"Huang","suffix":""},{"id":184565370,"identity":"ec4a2c5a-f387-4866-be5f-9449ffb4d660","order_by":3,"name":"Tebin Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tebin","middleName":"","lastName":"Chen","suffix":""},{"id":184565372,"identity":"25acd519-cb6c-4318-971a-4d0466c6805d","order_by":4,"name":"Rongfu Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3QMQrCMBSA4SdPgsOTbvKkoFeIdLBCwaskCJ09giLoIs6Kl/AIlYCT4AG6FLxA3LppnXTRZhTMtwXeT5IH4Hk/SEKjAE4SChCzzJZOCUrgNO11V0Ifd2vXBFITyQtFpiUckmFoBMcK9cyQNUDQDzrZ92S0mQhmJfR80T6YaQyD3V7VPOwcnLhbkl5glWwJlMxrE3zewnqJVBgS7omMCAmcE4xZqR6jkNWS2eUv2MhZ3Wl8MVdry6QfhDVJpRny68Cf597gzTrNeZ7n/a0HGc8/nFnWzCwAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rongfu","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2023-02-22 17:44:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2617566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2617566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34651338,"identity":"f809fcf0-7c0e-4765-bc75-969217aae73a","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132707,"visible":true,"origin":"","legend":"\u003cp\u003eX-tile analysis determining optimal MLNR cut-off values based on the overall survival. The optimal threshold is shown in blue(MLNR = 0), gray (MLNR，0 ≤ and \u0026lt;0.31) and violet (MLNR≥0.31) panels.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/baea7c151bbf03a8eedebd82.png"},{"id":34651337,"identity":"d1864274-e200-4d25-8369-2731b1ccb785","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112808,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival-predicting model for postoperative NSCLC patients at stage IA-IIIB. ADC, adenocarcinoma; ADSC, adenosquamous carcinoma; BAC, bronchioloalveolar carcinoma (fits adenocarcinoma in situ and minimally invasive adenocarcinoma); LC, large-cell carcinoma; MLNR, metastatic lymph nodes ratio; SC, squamous carcinoma.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/a60503d70563ce7e0a4799cc.png"},{"id":34651339,"identity":"1e70f243-598d-42df-af13-e1ee39a18ab4","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83545,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves for predicting patient survival at each time point in the (A) training set and (B) testing set. Nomogram-predicted overall survival (OS) is plotted on the x-axis; actual OS is plotted on the y-axis. A plot along the 45-degree line shows that predicted probabilities are well matched the actual outcomes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/7386aab24242d2f86690e15e.png"},{"id":34652991,"identity":"4f9eefab-0727-4a62-ada8-78affca86367","added_by":"auto","created_at":"2023-03-22 14:35:38","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161206,"visible":true,"origin":"","legend":"\u003cp\u003eX-tile analysis determining optimal cut-off values for risk stratification. The optimal threshold is shown in blue(low-risk group), gray (moderate-risk), and violet (high-risk group) panels.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/181cfdd09eebf95fca2890dd.jpeg"},{"id":34651341,"identity":"5d42441f-e5a4-40e2-8d33-6fff37ab8547","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":217859,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kaplan-Meier curves between subgroups in the training set (A) and the testing set (B).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/ed81c44bebabb26121f738b2.jpeg"},{"id":34651342,"identity":"b24dc6e7-3270-40ca-a533-cdae374391d9","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":390544,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier curves of OS for NSCLC patients after surgery. The prognostic value of examined lymph nodes number(ELNs) in total patient cohort (n = 40583, A), low-risk group (n = 15610, B), moderate-risk group (n = 21031, C), and high-risk group (n = 4212, D).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/be9aa9df666eb030d266e82d.jpeg"},{"id":34651343,"identity":"c243eeee-87c5-4b22-b881-75d6da2fb201","added_by":"auto","created_at":"2023-03-22 14:27:38","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":436633,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier curves of OS for NSCLC patients after surgery. The prognostic value of metastatic lymph nodes ratio(MLNR) in total patient cohort (n = 40583, A), low-risk group (n = 15610, B), moderate-risk group (n = 21031, C), and high-risk group (n = 4212, D).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/3e26cadb9adb380504a16694.jpeg"},{"id":36998447,"identity":"de510299-8a4a-4008-b1e3-1ce446da187c","added_by":"auto","created_at":"2023-05-14 06:59:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2617566/v1/c7680580-9ad0-45ad-b1a1-aa247f446bb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Significance of metastatic lymph nodes ratio in overall survival for patients with resected Non–Small-Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer(NSCLC) accounting for approximately 85% of all diagnosed patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the widespread application of advanced imaging techniques has witnessed a dramatic increase in early detection, for early-stage NSCLC patients who undergo surgical excision, the 5-year survival rates are only 30%~75%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistologically confirmed lymph node(LN) metastases are important prognostic factors and also essential components of the widely used tumor, node, metastasis (TNM) stage classification. However, patients in the same stage often show different survival time[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the updated eighth edition of the TNM classification of NSCLC, LN stages are the same as those in the previous edition and based solely on the anatomical location of LN involvement[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several previous studies have pointed out that the count of positive lymph nodes and examined lymph nodes can reveal the effectiveness of lymphadenectomy then oncological outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other researchers also found the ratio of metastatic lymph nodes ratio (MLNR) is a prognostic factor in many tumors, including esophageal carcinoma[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and medullary thyroid cancer[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNomogram is a reliable tool to quantify risk which can reduce statistical predictive models into a single numerical estimate of the probability of an event[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By creating precise prediction, that is to say, combining independent prognostic factors such as patients\u0026rsquo; age, histology type, and other potential prognostic factors, it can provide essential information to therapy regime option and cancer control. However, nomograms for predicting long-term survival outcomes for NSCLC patients after surgery are scarce.\u003c/p\u003e \u003cp\u003eIn this study, we are going to explore the ability of examined lymph nodes number (ELNs) and MLNR to predict prognosis for NSCLC patients after surgery employing a cohort from population-based Surveillance, Epidemiology, and End Results (SEER) program. By comparing it with conventional TNM staging criteria, we show that nomograms can confer better survival prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and collection\u003c/h2\u003e \u003cp\u003eAll data were collected from the SEER database. It is supported by the Surveillance Research Program which provides national leadership in the science of cancer surveillance as well as analytical tools and methodological expertise in collecting, analyzing, interpreting, and disseminating reliable population-based statistics[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. NSCLC cases between 2006 and 2015 in the SEER public access database and their corresponding details were identified with the use of SEER*Stat version 8.1.5 software. Patients were uniformly reviewed and staged according to the eighth edition of the TNM classification.\u003c/p\u003e \u003cp\u003eThe extent of surgery was analyzed by regional nodes positive (1988+) and RX Summ-Surg Prim Site (1998+)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Only patients diagnosed with NSCLC who underwent radical resection were enrolled in the study. Patients were eliminated when some of the criteria meet: a) patients with local advanced or metastatic disease (TNM stage IIIB or stage IV). b) more than one primary tumor c) missing information on extracted variables. d) fewer than one examined LN were eligible.)\u003c/p\u003e \u003cp\u003e Demographic characters (age, gender), the number of metastatic lymph nodes, the number of examined lymph nodes, American Joint Committee on Cancer (AJCC) TNM staging system (8th edition), and oncological outcomes were identified from the SEER database. We deemed endpoint of NSCLC patients as the time from histological diagnosis to the date of death from lung cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis was performed with R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.r-project.org\" target=\"_blank\"\u003ewww.r-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 3.6.1 and RStudio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.r-project.org\" target=\"_blank\"\u003ewww.rstudio.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.rstudio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) version 1.1.456. Categorical variables were shown as frequency and percentage. Continuous variables were transformed into categorical variables based on recognized cutoff values (for age) or median number (examined lymph nodes lymph node station). The X-tile program was applied to calculate the optimal threshold for MLNR and risk levels with maximum specificity and sensitivity. The variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the univariate analysis were chosen for the next step to built the Cox proportional hazards regression model to find risk factors linked to the prognosis of NSCLC.\u003c/p\u003e \u003cp\u003eThe nomogram prediction model was constructed based on chosen prognostic factors by multivariate cox regression analysis from the training set. Accuracy of nomograms was evaluated by Harrell\u0026rsquo;s concordance index (C-index) calculated through rcorrcens function in Hmisc package version 4.3-1. The predicted survival rates were compared with actual survival rates determined using a Kaplan-Meier analysis, and calibrations were generated in the training set and testing set trough calibrate function in rms package version 5.1-4. Bootstraps were used for these analyses at 1000 reiterations. Kaplan-Meier survival curves were constructed for chosen variables through ggsurvplot function included in package survminer version 0.4.6 and were compared with cox\u0026rsquo;s regression through coxph function included in package survival version 3.1-8. A comparison of the C-index of two different models was based on methods previously described[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTests were two-sided and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant in all analyses mentioned above.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eClinical characteristics of patients\u003c/h2\u003e\n\u003cp\u003eDuring 2006\u0026ndash;2015, a total of 456090 NSCLC patients were collected. Patients without lymphadenectomy or clear ELNs and MLNR, recorded staging information, stage IIIB or IV disease, not a first tumor and distant metastases were excluded. According to the inclusion criteria, 40853 patients with a history of surgical treatment at the primary site were finally identified. Based on the eighth TNM staging system, the proportion of patients at T1, T2, T3 and T4 stages was 42.24%(n\u0026thinsp;=\u0026thinsp;17255), 33.39% (n\u0026thinsp;=\u0026thinsp;13653), 14.27% (n\u0026thinsp;=\u0026thinsp;5837) and 10.06%(n\u0026thinsp;=\u0026thinsp;4108), respectively. Most patients (n\u0026thinsp;=\u0026thinsp;22767,55.729%) were of an adenocarcinoma histological type. The median ELNs were 8 (1\u0026thinsp;~\u0026thinsp;90), and the median MLNR was 0(range from 0\u0026thinsp;~\u0026thinsp;1). Among 4351 patients who accept adjuvant therapy, 1081 patients received radiotherapy, 2225 patients received chemotherapy and 1045 received both. Detailed information on demographic features and clinicopathological characteristics are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical characteristics of training and testing set of AJCC 8th staging IA to IIIB NSCLC patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal [n (%)]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTraining Cohort\u003c/p\u003e\n\u003cp\u003e[n (%)]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValidation Cohort\u003c/p\u003e\n\u003cp\u003e[n (%)]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\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\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10909(26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7799(26.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3110(26.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.949\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u0026ndash;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14758(36.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10549(36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4209(36.1)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e༞ 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15186(37.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10833(37.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4353(37.3)\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\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21170(51.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15112(51.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6058(51.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.842\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19683(48.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14069(48.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5614(48.1)\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\u003eRace\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33903(82.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24255(83.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9648(82.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.405\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3729(9.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2629(9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1100(9.4)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3221(7.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2297(7.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e924(7.9)\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\u003eAssistant Treat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeither\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22767(55.729)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20070(68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8018(68.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.985\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1081(2.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e711(2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e279(2.4)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2225(5.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5499(18.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2214(19)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1045(2.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2901(9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1161(9.9)\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\u003ePathology Type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1134(2.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16283(55.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6484(55.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1969(4.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e736(2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345(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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10632(26.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1578(5.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e647(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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30628(74.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e753(2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e292(2.5)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNSCLC (unspecified)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6643(16.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e815(2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e319(2.7)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3582(8.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1428(4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e541(4.6)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22356(54.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7588(26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3044(26.1)\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\u003eMLNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian(range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(0\u0026ndash;90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(0\u0026ndash;90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(0\u0026ndash;90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELNs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emedian(range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0(0\u0026ndash;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.586\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAJCC 8th T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1687(4.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1199(4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e488(4.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.957\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8744(21.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6255(21.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2489(21.3)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1c\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6824(16.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4862(16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1962(16.8)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10567(25.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7583(26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2984(25.6)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3086(7.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2185(7.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e901(7.7)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5837(14.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4159(14.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1678(14.4)\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4108(10.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2938(10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1170(10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAJCC: The American Joint Committee for Cancer; CI: confidence interval; HR: hazardous ratio; ELNs: examined lymph nodes number; MLNR: metastatic lymph nodes ratio; ADC: adenocarcinoma; ADSC: adenosquamous carcinoma; BAC: bronchi alveolar carcinoma; LCC: large cell carcinoma; SCC: squamous cell carcinoma; NSCLC (unspecified): non-small cell lung carcinoma (unspecified but not listed above); Other: other malignant tumors categories other than above and small cell lung cancer in SEER database.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eCut-off points for metastatic lymph nodes ratio\u003c/h2\u003e\n\u003cp\u003ePatient cohorts were made up of a training set and a testing set. Those two groups were randomly generated with 29181 (71%) in the former set and 11672(29%) in the later one. The discrepancy of clinical and oncological characteristics between those two parts was very small with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimal cut‑off value of MLNR(0, 0.31) in the training set was calculated using X‑tile(P-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). After enrolled all the variables into univariate and multivariate analysis, factors like MLNR (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), age (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), T stage (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), pathology type (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), treatment(p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were deemed as independent prognostic factors.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnivariate and Multivariate Cox regression analyses of OS of AJCC 8th staging IA to IIIB NSCLC patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003cp\u003e(40853)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eUnivariate Cox\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMultivariate Cox\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\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\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10909(26.7%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u0026ndash;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14758(36.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.222\u0026ndash;1.329\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.273\u0026ndash;1.386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e༞ 70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15186(37.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.710\u0026ndash;1.852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.847\u0026ndash;2.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21170(51.8%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19683(48.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.476\u0026ndash;1.569\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.319\u0026ndash;1.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33903(82.98%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\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\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3729(9.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.915\u0026ndash;1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.189\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\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3221(7.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.773\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.726\u0026ndash;0.823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreatment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeither\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28088(68.75%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e990(2.4233%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.215\u0026ndash;2.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.421\u0026ndash;1.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7713(18.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.241\u0026ndash;1.341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.784\u0026ndash;0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4062(9.943%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.915\u0026ndash;2.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991\u0026ndash;1.106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathology Type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22767(55.729%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1081(2.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.408\u0026ndash;1.671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u0026ndash;1.406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2225(5.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.592\u0026ndash;0.689\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.704\u0026ndash;0.816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1045(2.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.495\u0026ndash;1.767\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.461\u0026ndash;1.728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNSCLC (unspecified)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1134(2.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.344\u0026ndash;1.578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.184\u0026ndash;1.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1969(4.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.808\u0026ndash;0.948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.839\u0026ndash;0.985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10632(26.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.411\u0026ndash;1.512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u0026ndash;1.273\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMLNR\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=\"char\" char=\".\"\u003e\n\u003cp\u003e30628(74.97%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0-0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6643(16.26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.858\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.789\u0026ndash;1.930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.678\u0026ndash;1.829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3582(8.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.581\u0026ndash;2.820\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.399\u0026ndash;2.648\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELNs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22356(54.72%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e༜ 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18497(45.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.986\u0026ndash;1.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAJCC 8th T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1687(4.13%)\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\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\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8744(21.41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.163\u0026ndash;1.446\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.088\u0026ndash;1.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1c\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6824(16.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.607\u0026ndash;1.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.376\u0026ndash;1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10567(25.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.934\u0026ndash;2.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.769\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.591\u0026ndash;1.967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3086(7.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.479\u0026ndash;3.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.887\u0026ndash;2.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5837(14.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.803\u0026ndash;3.476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.142\u0026ndash;2.664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4108(10.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.748\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.362\u0026ndash;4.179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.563\u0026ndash;3.199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruct a new model of survival prediction\u003c/h2\u003e\n\u003cp\u003eAccording to the outcome of multivariate analysis, patients\u0026rsquo; age, MLNR, T stage, pathological type, and adjuvant treatment were identified as independent predictors. The above factors were applied to formulate a nomogram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The final risk point was the sum of the score assigned to each independent parameter. Patients with higher age, lower MLNR, more tend to be large cell carcinoma histology type, radiotherapy only is more likely to get higher points which means poor prognosis. We can visually estimate the probability of death of patients individually through drawing a vertical line.\u003c/p\u003e\n\u003cp\u003eThe calibration plots showed a well-matched 1-, 3-, and 5-year OS between nomogram prediction and clinically observation whether in the training set or testing set (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Harrell\u0026rsquo;s C-index, a classical index used to evaluate model performance, in our model is still superior to the usual TNM category prediction. C-index of 0.683 (95% CI: 0.677\u0026ndash;0.6878) and 0.676(95%CI: 0.648\u0026ndash;0.665) was observed in those two patient cohorts. However, in the AJCC TNM stage system (8th), CI is 0.641(95% CI: 0.636\u0026ndash;0.646) and 0.638(95% CI: 0.626\u0026ndash;0.642) respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Our new model is greater and this difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eC-index of nomogram and AJCC 8th Staging system.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNomogram\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAJCC 8th Stage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP.Value\u003c/p\u003e\n\u003cp\u003e(Nomogram vs AJCC 8th Stage)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC-Index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP.Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC-Index\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP.Value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\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\u003eTraining Set\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6777\u0026ndash;0.6878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6362\u0026ndash;0.6464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eValidation Set\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6762\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6485\u0026ndash;0.6649\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6267\u0026ndash;0.6429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\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\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStratification of risk groups\u003c/h2\u003e\n\u003cp\u003eBased on the cut-off calculated via X-tile program ((Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), patients were grouped into three subgroups as a higher score means an incremental risk: low risk (score 0\u0026ndash;13.9), moderate risk (score 14.0\u0026ndash;21.9), and high risk (score 22.0\u0026ndash;35.0). Significant distinction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between Kaplan-Meier curves was observed in different groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e\n\u003cp\u003eNext, we stratified patients according to the risk level to identify how ELNs、MLNR and adjuvant therapy influence their prognosis. As for ELNs, we can see from Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e that, in the total(p\u0026thinsp;=\u0026thinsp;0.29) and high risk(p\u0026thinsp;=\u0026thinsp;0.55) group, there are no incremental benefits when the examined lymph node is higher. But for patients with low and moderate risk, it is essential to have more lymph nodes examined since it can improve their survival time greatly(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MLNR which considered the examined lymph node and positive lymph node presented as a very good predictor. Kaplan-Meier curves are significantly different among patients with different MLNR in the same risk stratification as we can see from Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. All those above prove that our new model can provide efficient information about cancer control and management.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the TNM staging system is not precise enough especially regarding lymph node status, we want to build a new predictive model to evaluate the survival of NSCLC patients. Although several researchers are trying to construct predictive models[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], a nomogram which takes full account of the lymph node for NSCLC operable patient is scarce. In our study, we established a model which combines the condition of examined and positive lymph node for surgically resected NSCLC.\u003c/p\u003e \u003cp\u003eWe obtained our data from the SEER database which includes cancer incidence and survival data from 18 population-based cancer registries throughout America, covering about 27.8% of the US population. Thus huge database empowers our model to find out potential efficient predictor factors that influence a patient\u0026rsquo;s survival. Patients in stage IV were out of consideration since for them surgery is not the first treatment option. In this retrospective study, we concentrate on the lymph node status on the respect that this item is an important component in the conventional AJCC stage system. Yanling has pointed out in their research that current TNM classification only states the anatomic extent of lymph node metastasis. They suggest positive lymph node count should be included especially for stage III patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Examined lymph node count is also cannot be neglect as reported in Wenhua\u0026rsquo;s study. They analyzed data from a Chinese multi-institutional registry and US SEER database and found ELN count is associated with improved outcomes in NSCLC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They recommend 16 ELNs as the cut point for the evaluation of the quality of postoperative LN examination or prognostic stratification for patients with declared node-negative disease[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhat is the difference from our study and the referred above is that we aimed to construct a nomogram which considered the condition of the number of examined lymph node and metastatic lymph nodes together. Using multivariable models, we are going to find that MLNR can reflect more-accurate node staging than other indexes like ELNs. Marc reported that the number of LNs is subject to normally distributed interindividual variability, with no significant impact on OS[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Likewise, through univariable analysis by cox regression, ELNs have no association with the survival time of NSCLC patients(p\u0026thinsp;=\u0026thinsp;0.289) in our study.\u003c/p\u003e \u003cp\u003eAfter stratified all the patients based on nomogram points, we managed to formulate 3 different risk level groups. Using the cut-off value screened from X-tile, patients with MLNR higher than 0.31 shows better long-term survival despite their risk grade. Meanwhile, only moderate and low-risk patients can benefit from a greater examined lymph node. More lymph node examined means more exhaustive elimination of remnants and timely impart adjuvant chemotherapy. But for high-risk NSCLC patients, as we know, adjuvant chemotherapy is imperative as long as there are signs of LN metastasis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is speculated that for this group of NSCLC patient, an increased ELNs doesn\u0026rsquo;t present as better survival time. The Benchmark of ELNs we determined is 8 which is different from others[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. LN number we recorded may not reflect the true situation since it\u0026rsquo;s not easy in separating each LN after dissected and crushed nodal tissues often mistaken as several completed ones. Therefore, recommending an ideal ELNs maybe is not suitable.\u003c/p\u003e \u003cp\u003eWe also find patients with different histological types show different prognoses. Horiana recently reported in their study that the addition of histological subtype enhances traditional TNM classification to provide a more accurate risk assessment of patients with early-stage NSCLC[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After taking into account competing risks, those with squamous histology have a higher risk of mortality than those with adenocarcinoma histology[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the statistic method is different, we also found squamous histology shows an inferior prognosis than adenocarcinoma histology. Besides, we included histological types more than this two histology. The highest risk is\u003c/p\u003e \u003cp\u003elarge cell carcinoma while bronchi alveolar carcinoma with the minimum risk. We think to specify this element in our nomogram will make it more precise and reliable.\u003c/p\u003e \u003cp\u003eOur study still has some limitations due to its retrospective nature. And interpret of those data should be cautious since the SEER database lacking some confounding factors like smoking, postoperative complications and access to perform limited resection (open or VATS). The results we get may be influenced to some extent and prospective studies are needed to further test the efficiency of our model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAll in all, to evaluate the survival time of NSCLC patients, it is not enough to just consider the anatomic extent of lymph node metastases. Here we performed a population-based study to summary clinic characteristics of NSCLC patients after surgery. Older age, higher NLPR, histology type, adjuvant treatment, and AJCC 8th stage were independent risk factors of NSCLC. Then elements build a nomogram that was performed greater than the AJCC conventional stage system. Through risk stratification we also able to speculate that NLPR is more suitable than ELNs to predict the survival of NSCLC patients especially for the high risk one.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNSCLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eTNM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;tumor-node-metastasis\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;overall survival\u003c/p\u003e\n\u003cp\u003eMLNR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; metastatic lymph nodes ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC-index \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; concordance index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eELNs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lymph nodes number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lymph node\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEER \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Surveillance, Epidemiology, and End Results\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAJCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; American Joint Committee on Cancer \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence interval\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hazardous ratio\u003c/p\u003e\n\u003cp\u003eADC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; adenocarcinoma\u003c/p\u003e\n\u003cp\u003eADSC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; adenosquamous carcinoma\u003c/p\u003e\n\u003cp\u003eBAC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; bronchi alveolar carcinoma\u003c/p\u003e\n\u003cp\u003eLCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;large cell carcinoma\u003c/p\u003e\n\u003cp\u003eSCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grants from Natural Science Foundation of Fujian province(Grant No. 2019J01169) and Science and Technology Foundation of Quanzhou City (Grant No. 2022NS087) to Xiaoping Lin. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoping Lin and Jianfeng Yaocontributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Xiaoping Lin\u003c/p\u003e\n\u003cp\u003eReproductive Medicine Centre,Quanzhou Maternity and Child Health Care Hospital, Quanzhou, 362000, China\u003c/p\u003e\n\u003cp\u003eJianfeng Yao\u003c/p\u003e\n\u003cp\u003eDepartment of Clinical Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000,China\u003c/p\u003e\n\u003cp\u003eTebin Chen \u0026amp;\u0026nbsp;Rongfu Huang\u003c/p\u003e\n\u003cp\u003ePediatrics, The Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, China\u003c/p\u003e\n\u003cp\u003eBaoshan Huang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed substantially to the development of the study. XL and JY designed the study, collected the data and wrote the main manuscript text. BH did the statistical and prepared table 1-3. TC did the statistical analysis and prepared figure 1-7. BH, TC and RH provided critical comments, suggestions, and revised the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespond\u003c/strong\u003e\u003cstrong\u003eing author\u003c/strong\u003e\u003cstrong\u003e: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Rongfu Huang\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJemal A, Center MM, DeSantis C, Ward EM. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev. 2010;19:1893\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstraw P, Crowley J, Chansky K, Giroux DJ, Groome PA, Rami-Porta R, Postmus PE, Rusch V, Sobin L. 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J Clin Oncol. 2017;35:1162\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukui T, Mori S, Yokoi K, Mitsudomi T. Significance of the number of positive lymph nodes in resected non-small cell lung cancer. J Thorac Oncol. 2006;1:120\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhao W, Ni J, Zou L, Yang X, Yu W, Fu X, Zhao K, Zhang Y, Chen H, Xiang J, Xie C, Zhu Z. Predicting the Value of Adjuvant Therapy in Esophageal Squamous Cell Carcinoma by Combining the Total Number of Examined Lymph Nodes with the Positive Lymph Node Ratio. Ann Surg Oncol. 2019;26:2367\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Qian K, Guo K, Zheng X, Sun W, Sun T, Wang Y, Li D, Wu Y, Ji Q, Wang Z. A Novel N Staging System for Predicting Survival in Patients with Medullary Thyroid Cancer. Ann Surg Oncol. 2019;26:4430\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26:1364\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverview of the SEER Program. In: iewhtml hscgao editor.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Zhao M, Zhou L, Zhang M, Cao P, Tao L. Significance of examined lymph nodes number and metastatic lymph nodes ratio in overall survival and adjuvant treatment decision in resected laryngeal carcinoma. Cancer Med. 2020;9:3006\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWo Y, Yang H, Zhang Y, Wo J. 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Number of Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: A Gaussian Curve, Not a Prognostic Factor. Ann Thorac Surg. 2014;98:224\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaric B, Stojsic V, Tepavac A, Sarcev T, Zarogoulidis P, Darwiche K, Tsakiridis K, Karapantzos I, Kesisis G, Kougioumtzi I, Katsikogiannis N, Machairiotis N, Stylianaki A, Foroulis CN, Zarogoulidis K, Perin B. Adjuvant chemotherapy and radiotherapy in the treatment of non-small cell lung cancer (NSCLC). J Thorac Disease. 2013;5:371\u0026ndash;S377.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Shen JF, Liu LP, Shan LL, He JX, He QH, Jiang L, Guo MZ, Chen XW, Pan H, Peng GL, Shi HH, Ou LM, Liang WH, He JX. Impact of examined lymph node counts on survival of patients with stage IA non-small cell lung cancer undergoing sublobar resection. J Thorac Disease. 2018;10:6569\u0026ndash;.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrosu HB, Manzanera A, Shivakumar S, Sun S, Noguras Gonzalez G, Ost DE. Survival disparities following surgery among patients with different histological types of non-small cell lung cancer. Lung Cancer. 2020;140:55\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"non-small cell lung cancer, tumor-node-metastasis, lymph node, Stage, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-2617566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2617566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u0026nbsp; \u003c/strong\u003eTNM stage is widely applied to classify lung cancer and the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough especially for the N status. In this study, we aim to build a convenient survival prediction model that incorporated the current items of lymph node status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe collected data of resectable NSCLC(IA-IIIB) patients from Surveillance, Epidemiology, and End Results (SEER) database (2006-2015). X-tile program was applied to calculate the optimal threshold of metastatic lymph nodes ratio (MLNR). Then, independent prognostic factors were determined by multivariable cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the concordance index(C-index ) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph nodes number (ELNs) were presented in subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph nodes ratio, histology type, adjuvant treatment, and AJCC 8\u003csup\u003eth\u003c/sup\u003e T stage were deemed as independent prognostic parameters after multivariable cox regression analysis. Nomogram was built using those variables and its efficiency in predicting patients’ survival was better than the conventional AJCC stage system after evaluation. Our new model has a significant higher concordance index(C-index) (training set,0.683 v 0.641, respectively; P\u0026lt;0.01; testing set, 0.676 v 0.638, respectively; p\u0026lt;0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observation in both cohorts. And then, after risk stratification, we found MLNR is more reliable than ELNs in predicting overall survival(OS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWe developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used TNM staging system and could benefits clinicians in treatment options and cancer control.\u003c/p\u003e","manuscriptTitle":"Significance of metastatic lymph nodes ratio in overall survival for patients with resected Non–Small-Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-22 14:27:33","doi":"10.21203/rs.3.rs-2617566/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":"2016e996-ca56-4f9e-98a9-725d8f655f91","owner":[],"postedDate":"March 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-14T06:59:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-22 14:27:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2617566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2617566","identity":"rs-2617566","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}