Mathematical Models for Intraoperative Prediction of Metastasis to Lymph Nodes in the Hilar-intrapulmonary or the Mediastinal Region in Patients With Clinical Stage I Non-small Cell Lung Cancer: a Retrospective Cohort Study

Mathematical Models for Intraoperative Prediction of Metastasis to Lymph Nodes in the Hilar-intrapulmonary or the Mediastinal Region in Patients With Clinical Stage I Non-small Cell Lung Cancer: a Retrospective Cohort Study | 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 Mathematical Models for Intraoperative Prediction of Metastasis to Lymph Nodes in the Hilar-intrapulmonary or the Mediastinal Region in Patients With Clinical Stage I Non-small Cell Lung Cancer: a Retrospective Cohort Study Yue Zhou, Junjie Du, Changhui Ma, Fei Zhao, Hai Li, Guoqiang Ping, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-120644/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: It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. Methods: The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analysed for their association with the HNM or the MNM. After analysing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients. Results: It was confirmed that CEA (> 5.75 ng/ml), CYFRA211 (> 2.85 ng/ml), the maximum diameter of tumour (> 2.75 cm), tumour differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and CEA (>8.25 ng/ml), CYFRA211 (> 2.95 ng/ml), the maximum diameter of tumour (> 2.75 cm), tumour differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power. Conclusions: Our predictive models are helpful in the decision‑making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC. Medical Informatics Lymph node metastasis Hilar-intrapulmonary region Mediastinal region Prediction model Non-small cell lung cancer Figures Figure 1 Figure 2 Figure 3 1. Background Non-small cell lung cancer (NSCLC) is among the deadliest malignancies in the world [ 1 ]. Lobectomy plus complete lymph node (LN) dissection with removal of all ipsilateral hilar and mediastinal lymphatic tissue remains the standard surgical procedure for the treatment of dissectible lung cancer [ 2 ]. For the last decade, more early-stage lung cancer has been diagnosed, partly thanks to the development of computed tomography (CT) screening [ 3 , 4 ]. In the current era of value-driven healthcare, it is important to consider novel approaches to maintaining the curative intent pulmonary operation while decreasing unnecessary removal of surrounding healthy tissues. Tailoring LN dissection during operation for early-stage NSCLC has gradually become a particularly attractive target for value optimization because limiting LN harvest may avoid unnecessary intraoperative injury, shorten the operative time, reduce post-operative morbidity, and have better cost-effective outcomes [ 5 – 9 ]. Variety of techniques have been developed to detect the clinical N-category, such as radiologic imaging, endoscopic and surgical techniques [ 10 ]. However, it is still hard to find the highest-quality and most cost-effective investigation to accurately determine pathological N stage of an early NSCLC [ 11 – 13 ]. Therefore, there is no universally accepted method of LN dissection for this patient population [ 14 , 15 ]. A quick and accurate prediction of the presence and the precise regions of LN metastasis of clinical stage I NSCLC before or during operation will help surgeons choose optimized surgical approaches. The authors have previously explored relevant clinicopathologic factors affecting regional LN metastasis in clinical stage I NSCLC [ 16 ]. In this study, we further developed mathematical models to predict regional LN status in patients with clinical stage I NSCLC and to help surgeons make reasonable decisions of LN dissection by studying the relationship between the clinicopathologic variables and the hilar-intrapulmonary nodal metastasis (HNM) or the mediastinal nodal metastasis (MNM). 2. Methods 2.1 Study Population The institutional ethics committee approved the study with waiver of consent (approval no.2017-SR-097). The work was registered in www.chictr.org.cn with a registration number (ChiCTR2000031620). Clinical data of the consecutive patients with primary lung cancer who underwent video-assisted thoracoscopic surgery at our hospital from January 2017 to September 2019 were collected and reviewed retrospectively. The enrollment criteria of this study were as follows: (1) diagnosed as having clinical stage I NSCLC based on the new International Staging System for NSCLC (National Comprehensive Cancer Network Guidelines Version 3.2014: Staging Non-Small Cell Lung Cancer); (2) underwent lobectomy with postoperative pathological confirmation of NSCLC and had complete LN dissection. Patients who exhibited any one of the following conditions were excluded from this study: (1) preoperative tumour size > 4 cm on CT imaging; (2) preoperative LN > 1 cm at the shortest diameter on CT imaging; (3) had evidence of distant metastasis; (4) had preoperative chemotherapy or radiotherapy; (5) had previous or coexistent tuberculosis or malignant diseases; (6) had LN dissection that did not meet the current standards of complete LN dissection (i.e., all LN stations, including stations 10–14, right-hand stations 2–4 and 7–9, and left-hand stations 4–9); (7) had synchronous lung cancers or multiple primary cancers; (8) postoperative pathological diagnosis revealed special types of pulmonary infection or other LN diseases; or (9) had incomplete clinical data. Eligible patients who underwent surgery before December 2018 were included in the derivation cohort to establish the models, and patients who underwent surgery after December 2018 were entered into the validation cohort. 2.2 Clinicopathological Variables Trained chart abstractors collected clinical variables such as age, gender, smoking history, family history, tumour markers in blood, the identity of the lobe, tumour size, tumour location within the lobe. The chart abstractors also collected postoperative pathological results of the lymph node metastasis from the conventional sections as mentioned below. the Two clinicians (YZ and CM) verified the accuracy of predictor variables. For histopathologic assessment, a part of the pulmonary tumour was processed for rapidly frozen sections during operation. The remaining tumour tissue with all lymph nodes were fixed using 10% formalin, embedded in paraffin and conventionally sectioned. Both the intraoperative rapidly frozen sections and the postoperative conventional sections were kept in the department of pathology in our hospital for 10 years. All rapidly frozen sections in this study were reviewed and assessed independently by two experienced pathologists. The classification of pathological variables such as histological tumour type and grading, lymphatic vessel invasion status, bronchial mucosa and cartilage invasion status, visceral pleural invasion status and nerve invasion status was based on the consensus of these two pathologists without knowing the results of postoperative formalin-fixed paraffin-embedded sections. 2.3 Model Development The method for developing mathematical models of predicting HNM and MNM was previously described in details [ 17 ]. For clinical use of the models, two nomograms were formulated based on proportionally converting each regression coefficient in multivariate logistic regression to a 0- to 100-point scale by using the rms package of software R 3.3.1 ( www.r-project.org ). 2.4 Statistical Analysis All statistical analyses were performed using software SPSS 18.0 (IBM, Armonk, NY). The Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. In all analyses, p < 0.05 was considered to indicate statistical significance. 3. Results 3.1 Patients’ characteristics During the study period, 3765 consecutive patients with primary lung cancer underwent video-assisted thoracoscopic surgery. Of these, 1003 patients who met the inclusion criteria were enrolled and divided into the derivation and validation cohorts (585 and 418 patients respectively). The clinicopathologic characteristics of the patients were listed in Table 1 . Table 1 Characteristics of the study population Characteristics Cohort, No. (%) Derivation (n = 585) Validation (n = 418) Age (years) Mean ± SD 61.7 ± 8.8 60.6 ± 9.4 Gender, n (%) Male 309 (52.8%) 212 (50.7%) Female 276 (47.2%) 206 (49.3%) Smoking history No 464 (79.3%) 299 (71.5%) Yes 121 (20.7%) 119 (28.5%) Family history No 573 (97.9%) 397 (95.0%) Yes 12 (2.1%) 21 (5.0%) CEA ≤ 5.75 ng/ml 455 (77.8%) 355 (84.9%) > 5.75 ng/ml 130 (22.2%) 63 (15.1%) ≤ 8.25 ng/ml 492 (84.1%) 379 (90.7%) > 8.25 ng/ml 93 (15.9%) 39 (9.3%) CYFRA211 ≤ 2.85 ng/ml 396 (67.7%) 326 (78.0%) > 2.85 ng/ml 189 (32.3%) 92 (22.0%) ≤ 2.95 ng/ml 407 (69.6%) 333 (79.7%) > 2.95 ng/ml 178 (30.4%) 85 (20.3%) Tumor position Right Upper Lobe 158 (27.0%) 140 (33.5%) Right Middle Lobe 42 (7.2%) 23 (5.6%) Right Lower Lobe 116 (19.8%) 61 (14.5%) Left Upper Lobe 142 (24.3%) 117 (27.9%) Left Lower Lobe 111 (19.0%) 65 (15.6%) Mixed lobes 16 (2.7%) 120 (2.8%) Maximum diameter of tumor ≤ 2.75 cm 359 (61.4%) 320 (76.6%) > 2.75 cm 226 (38.6%) 98 (23.4%) Pathological type Adenocarcinoma 497 (85.0%) 359 (85.9%) Squamous cell carcinoma 88 (15.0%) 59 (14.1%) Tumor differentiation I 138 (23.6%) 143 (34.2%) II 224 (38.3%) 245 (58.6%) III 223 (38.1%) 30 (7.2%) Bronchial mucosa and cartilage invasion No 489 (83.6%) 355 (84.9%) Yes 96 (16.4%) 63 (15.1%) Vascular invasion No 544 (93.0%) 388 (92.8%) Yes 41 (7.0%) 30 (7.2%) Visceral pleural invasion No 432 (73.8%) 365 (87.3%) Yes 153 (26.2%) 53 (12.7%) Nerve invasion No 574 (98.1%) 398 (95.3%) Yes 11 (1.9%) 20 (4.7%) 3.2 Analysis of predictive factors for HNM in the derivation cohort The clinicopathological factors of the patients from the derivation cohort with and without the presence of HNM were evaluated. To help yield a simple risk score model later, continuous variables were converted into categorical variables based on the receiver operating characteristic (ROC) curve analysis with the maximum Youden index for the best cut-off value (Supplementary table 1 and 2). Univariate and multivariable logistic regression analysis revealed the following significant predictors of HNM: CEA > 5.75 ng/ml, CYFRA211 > 2.85 ng/ml, the maximum diameter of tumour > 2.75 cm, tumour differentiation, presence of bronchial mucosa and cartilage invasion, and vascular invasion (Table 2 ). Table 2 Univariate and multivariate logistic regression predictors of hilar-intrapulmonary lymph node metastasis Independents Variable Univariate Predictors Multivariate Predictors Metastasis rate, n (%) Odds Ratio (95% CI), P-value B Odds Ratio (95% CI), P-value CEA ≤ 5.75 ng/ml 83/455 (18.2%) 5.07 (3.34–7.71), 0.000 1.102 3.01 (1.85–4.91), 0.000 > 5.75 ng/ml 69/130 (53.1%) CYFRA211 ≤ 2.85 ng/ml 74/396(18.7%) 3.06 (2.08–4.49), 0.000 0.675 1.96 (1.25–3.09), 0.004 > 2.85 ng/ml 78/189(41.3%) Maximum diameter of tumor ≤ 2.75 cm 52/359(14.5%) 4.69 (3.16–6.95), 0.000 0.996 2.71 (1.73–4.23), 0.000 > 2.75 cm 100/226(44.2%) Tumor differentiation I, II 53/362(14.6%) 4.66 (3.14–6.90), 0.000 0.813 2.26 (1.44–3.54), 0.000 III 99/223(44.4%) Pathological type Adenocarcinoma 114/497 (22.9%) 2.55 (1.60–4.09), 0.000 Squamous cell carcinoma 38/88 (43.2%) Bronchial mucosa and cartilage invasion No 99/489(20.2%) 4.86 (3.07–7.68), 0.000 1.064 2.90 (1.71–4.91), 0.000 Yes 53/96(55.2%) Vascular invasion No 125/544(23.0%) 6.47 (3.29–12.71), 0.000 1.469 4.35 (2.00-9.47), 0.000 Yes 27/41(65.9%) Visceral pleural invasion No 106/432(24.5%) 1.32 (0.88–1.99), 0.181 Yes 46/153(30.1%) Nerve invasion No 143/574(24.9%) 13.56 (2.90-63.51), 0.001 Yes 9/11(81.8%) 3.3 Analysis of predictive factors for MNM in the derivation cohort The clinicopathological factors of the patients from the derivation cohort with and without the presence of MNM were evaluated. Univariate and multivariable logistic regression analysis showed the following predictors of MNM: CEA > 8.25 ng/ml, CYFRA211 > 2.95 ng/ml, the maximum diameter of tumour > 2.75 cm, tumour differentiation, presence of bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion (Table 3 ). Table 3 Univariate and multivariate logistic regression predictors of mediastinal lymph node metastasis Independents Variable Univariate Predictors Multivariate Predictors Metastasis rate Odds Ratio (95% CI), P-value B Odds Ratio (95% CI), P-value CEA ≤ 8.25 ng/ml 64/492 (13.0%) 7.78 (4.79–12.63), 0.000 1.546 4.69 (2.73–8.08), 0.000 > 8.25 ng/ml 50/93 (53.8%) CYFRA211 ≤ 2.95 ng/ml 58/407 (14.3%) 2.76 (1.81–4.21), 0.000 0.623 1.87 (1.13–3.07), 0.015 > 2.95 ng/ml 56/178 (31.5%) Maximum diameter of tumor ≤ 2.75 cm 39/359 (10.9%) 4.08 (2.64–6.28), 0.000 0.807 2.24 (1.37–3.67), 0.001 > 2.75 cm 75/226 (33.2%) Tumor differentiation I, II 41/362 (11.3%) 3.81 (2.48–5.85), 0.000 0.579 1.78 (1.08–2.94), 0.023 III 73/223 (32.7%) Pathological type Adenocarcinoma 101/497 (20.3%) 0.68 (0.36–1.27), 0.228 Squamous cell carcinoma 13/88 (14.8%) Bronchial mucosa and cartilage invasion No 76/489 (15.5%) 3.56 (2.21–5.73), 0.000 0.778 2.18 (1.24–3.83), 0.007 Yes 38/96 (39.6%) Vascular invasion No 92/544 (16.9%) 5.69 (2.96–10.94), 0.000 1.272 3.57 (1.68–7.57), 0.001 Yes 22/41 (53.7%) Visceral pleural invasion No 70/432 (16.2%) 2.09 (1.35–3.22), 0.001 0.862 2.37 (1.41–3.97), 0.001 Yes 44/153 (28.8%) Nerve invasion No 110/574 (19.2%) 2.41 (0.69–8.38), 0.166 Yes 4/11 (36.4%) Table 4 Recommendation of lymph node dissection for clinical stage I patients Combination of HNM and MNM prediction Recommendation for regional LN dissection HNM low risk + MNM low risk No need for LN dissection HNM high risk + MNM low risk Hilar and intrapulmonary LN radical dissection HNM low risk + MNM high risk Mediastinal LN radical dissection HNM low risk + MNM high risk systematic LN dissection HNM, hilar-intrapulmonary node metastasis. MNM, mediastinal node metastasis. LN, lymph node 3.4 Development of mathematical models of predicting HNM and MNM Abovementioned six independent risk factors associated with HNM and seven factors with MNM were used to form mathematic models. The resulting beta coefficients in multivariate analysis were applied to calculate predicted values from the logistic equation by using the following weighted sum for HNM: xβ = −2.769 + (1.102 × CEA) + (0.675 × CYFRA211) + (0.996 × Maximum diameter of tumor) + (0.813 × Tumor differentiation) + (1.064 × Bronchial mucosa and cartilage invasion) + (1.469 × Vascular invasion), and for MNM: xβ = −3.256 + (1.546 × CEA) + (0.623 × CYFRA211) + (0.807 × Maximum diameter of tumor) +(0.579 × Tumor differentiation) +(0.778 × Bronchial mucosa and cartilage invasion) + (1.272 × Vascular invasion) + (0.862 × Visceral pleural invasion). The corresponding value of each variable in the equation was listed in Supplementary table 3 and 4. ROC curves of the models for HNM and MNM were generated and demonstrated in Fig. 1 . 3.5 Validation tests of the prediction models The fitted models derived from the derivation cohort were applied to the validation cohort to produce estimated values of the possibility of risk for HNM or MNM. The area under curve (AUC) of the ROC curve of the estimated value for HNM was 0.872 (95% CI, 0.831–0.913) and 0.823 (95% CI, 0.766–0.879) for MNM in the validation cohort, demonstrating good discriminatory power (Fig. 1 ). 3.6 Definition of low and high risks of LN metastasis According to the maximized Youden's index, i.e., the sum of sensitivity and specificity, the optimal clinically applicable cut-off value of estimated risks was 0.209 for HNM and 0.132 for MNM (Supplementary table 5). Therefore, we defined the groups with different predicted risks by using a priori based on cut-off values at 0.209 and 0.132 of estimated possibilities for HNM and MNM respectively. 3.7 Development of prediction nomograms of LN metastasis We further built two nomograms for intraoperative use in prodicting the probability of HNM and MNM for patients with clinical stage I NSCLC (Figs. 2 and 3 ). First, we identified the points for each predictor based on the point scale at the top of the nomograms, and then summed up all points. The risk of metastasis was then obtained based on the bottom point scale. The cut-off point in the nomograms was 0.209 for HNM, and 0.132 for MNM. 4. Discussion The incidence of LN metastasis in patients with clinical stage I NSCLC is significantly lower than that in patients with advanced lung cancer. A randomized trial named ACOSOG Z0030 concluded that systematic mediastinal lymph node dissection could not improve the survival for patients with early‑stage NSCLC by confirming that there was no positive lymph node either in mediastinum or in hilum through presection sampling [ 18 ]. However, other scholars pointed out that postoperative pathology has shown that even the small-sized lung cancer (< 2 cm) had hilar and mediastinal node metastasis with an incidence of up to 20% [ 14 , 19 ]. Furthermore, patients with positive MNM exhibited a 20–38% incidence of skip metastasis, a phenomenon in which MNM occurs without the involvement of HNM [ 20 , 21 ]. Therefore, accurate integration of lymph node staging in patients with clinical stage I NSCLC is important in guiding the choices of surgical treatments. In this study, we identified six clinical variables as the independent predictors in common for regional lymph node metastases including the hilar-intrapulmonary region and the mediastinal region, which is consistent with the literature [ 22 – 24 ]. However, the visceral pleural invasion was only associated with the MNM. This phenomenon seems to indicate that we should discuss the HNM and MNM separately. We think this is based on the following anatomical structures of LN system in the lung. The lymph nodes associated with the cancer metastasis are widely labelled using a system of numerical levels and assigned names based on their anatomical locations. First, the hilar-intrapulmonary lymph nodes (groups 10–14), and second, the mediastinal lymph nodes (includes group 2R, 3, 4R, 7, 8, 9R, and group 4L, 5, 6, 7, 8, 9L). Generally, the sequence of LN metastasis of central NSCLC should be step by step as follows: along the bronchial tree from the intrapulmonary region to hilar, and then to the mediastinal region. Different from the central NSCLC, the LN metastasis of peripheral NSCLC with the peripheral capsule being invaded may incline to skip to MNM without HNM due to the lymphatic capillaries directly from the peripheral membrane to the mediastinal lymph nodes [ 25 , 26 ]. The rapid pathological results during operation would help surgeons to select the right procedures for the patients, for instance, wedge resection, segmentectomy, or lobectomy [ 27 ]. However, surgeons do not know which pattern to choose for lymph node dissection because of the complicated lymphatic spreading of lung cancer. Therefore, we creatively developed two utilizable prediction models from a logistic equation for regional LN metastases in patients with clinical stage I NSCLC. In terms of clinical relevance, the models provide more precise estimates of regional LN metastasis during operation for individual patients with clinical stage I NSCLC. Especially, the model for MNM is a good supplement for surgeons to improve their decision-making process for systematic LN resection. There were some published models developed for the assessment of lymph node disease of lung cancer [ 28 – 32 ]. Some either only focused on the outcome of MNM, or only the preoperative variables were included while appropriate candidates for limited LN resection should be with pathological confirmation of negative LNs, both in the hilar and the mediastinal regions. For these reasons, none of these models is commonly employed in clinical practice. In our models, on the other hand, all variables, from the radiographic size of tumour to visceral pleural invasion, are available before or during operation by regular measures in routine clinical practice without consuming any extra resources or time. The models are even easier to use in clinical practice with the associated nomograms guiding surgeons to choose the optimized method for LN dissection rapidly. In a case of clinical stage I NSCLC, when intraoperative rapid pathological results reveal invasive lung cancer, standard lobectomy will be performed with the following recommendations for LN dissection which are based on the patient’s category of the prediction models (Table 4 ). (1) When HNM low risk plus MNM low risk, there is no need to dissect the lymph nodes or only regional LN sampling is adequate. (2) When HNM high risk plus MNM low risk, it usually refers to a central tumour involving the bronchial tree. In this case, complete dissection of hilar-intrapulmonary LNs is a requirement but no need to dissect mediastinal lymph nodes or just do regional LN sampling in the mediastinum. (3) When HNM low risk plus MNM high risk, it usually indicates skipping metastasis in peripheral lung cancer invading the lung membrane. In this case, complete mediastinal LN dissection is required but no need for hilar-intrapulmonary LN dissection or just do regional LN sampling in this region. (4) When HNM high risk plus MNM high risk, a systematic LN dissection is required, including the hilar-intrapulmonary region and the mediastinal region. Given the retrospective nature of this single-institution study, selection bias is inherent in our study population. Although we validated the models, they still need to be validated by patients from comparative centres. The serum status of CEA and CYFRA221 was predictive factors in our models, but the kinds of tumour marker may not be the same at different hospitals which limits the application of the model. Moreover, the interval between blood test of tumour markers and surgery was not uniformly standardized, which may exert an uncertain impact on the serum status. As our study did not include the tumor recurrent status or the survival rate in this patient population, the relationship between survival rate and predictive value is unknown [ 33 ]. Last but not least, there occurs inaccuracy to some extent on the rapid pathological results of the risk-predicting variables during operation, such as visceral pleural invasion, bronchial mucosa and cartilage invasion, and vascular invasion. However, it could be improved in the near future with the development of advanced tools for pathological diagnosis. 5. Conclusions Taken together, the mathematical models for prediction of regional lymph node metastasis were accurate and easy‑to‑use. Based on the patients’ clinicopathologic variables before and during operation, these models are helpful in the surgical decision‑making process for LN dissection in patients with clinical stage I NSCLC. List of abbreviations AUC, area under curve Cis, confidence intervals CT, computed tomography HNM, hilar-intrapulmonary nodal metastasis LN, lymph node MNM, mediastinal nodal metastasis NSCLC, non-small cell lung cancer ORs, odds ratios ROC, receiver operating characteristic Declarations Ethics approval and consent to participate The Ethics Committee of Nanjing Medical University approved the study with waiver of consent (approval no.2017-SR-097). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Study concept and design: YZ, JD, CM, SZ. Acquisition of Data: YZ, JD, CM, FZ, HL, GP, WW, JL, LC, KZ, SZ. Drafting of the article: YZ, JD, CM, KZ, SZ. Critical revision for important intellectual content: all authors. Final approval of the manuscript:all authors. Acknowledgements Not applicable. References Reif MS, Socinski MA, Rivera MP (2000) Evidence-based medicine in the treatment of non-small-cell lung cancer. Clin Chest Med 21:107-20. Rusch VW, Asamura H, Watanabe H, et al (2009) The IASLC lung cancer staging project: a proposal for a new international lymph node map in the forthcoming seventh edition of the TNM classification for lung cancer. J Thorac Oncol 4:568–77. 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El-Sherief AH, Lau CT, Carter BW, et al (2018) Staging Lung Cancer: Regional Lymph Node Classification. Radiol Clin North Am 56:399–409. El-Sherief AH, Lau CT, Obuchowski NA, et al (2017) Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning. Chest 151:776–85. Chen KZ, Yang F, Wang X, et al (2015) A clinical prediction model for N2 lymph node metastasis in clinical stage I non-small cell lung cancer. Beijing Da Xue Xue Bao 47:295–301. Zhang Y, Sun Y, Xiang J, et al (2012) A prediction model for N2 disease in T1 non-small cell lung cancer. J Thorac Cardiovasc Surg 144:1360–4. Chen K, Yang F, Jiang G, et al (2013) Development and validation of a clinical prediction model for N2 lymph node metastasis in non-small cell lung cancer. The Annals of Thoracic Surgery 96:1761–8. Moulla Y, Gradistanac T, Wittekind C, et al (2019) Predictive risk factors for lymph node metastasis in patients with resected non-small cell lung cancer: a case control study. J Cardiothorac Surg 14:11 Verdial FC, Madtes DK, Hwang B, et al (2019) Prediction model for nodal disease among patients with non–small cell lung cancer. Ann Thorac Surg 107:1600–6. Yendamuri S, Dhillon SS, Groman A, et al (2018) Effect of the number of lymph nodes examined on the survival of patients with stage I non-small cell lung cancer who undergo sublobar resection. J Thorac Cardiovasc Surg 156:394–402. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-120644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":5745872,"identity":"c49387d7-cf35-4e4c-aebd-fee6292bf81a","order_by":0,"name":"Yue Zhou","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhou","suffix":""},{"id":5745873,"identity":"52fe1231-de40-4f57-955e-229770108d0c","order_by":1,"name":"Junjie Du","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Du","suffix":""},{"id":5745874,"identity":"2910d152-a6b0-4d98-a072-3976d1c332e4","order_by":2,"name":"Changhui Ma","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changhui","middleName":"","lastName":"Ma","suffix":""},{"id":5745875,"identity":"3ed4a604-524a-4b94-826e-a2d6358e6872","order_by":3,"name":"Fei Zhao","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zhao","suffix":""},{"id":5745876,"identity":"faec9d13-fe75-4340-9c8f-86906d763f54","order_by":4,"name":"Hai Li","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Li","suffix":""},{"id":5745877,"identity":"4ee0e41a-9f5a-49c9-80ba-ebdc3d1016ad","order_by":5,"name":"Guoqiang Ping","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guoqiang","middleName":"","lastName":"Ping","suffix":""},{"id":5745878,"identity":"1dd24d04-5380-4f03-84b3-1571f1f141e0","order_by":6,"name":"Wei Wang","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":5745879,"identity":"a43b6f0c-1e09-49d3-883b-11eac7bda498","order_by":7,"name":"Jinhua Luo","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Luo","suffix":""},{"id":5745880,"identity":"71325a51-c6e7-4a12-8bc4-ae9b5003e4dd","order_by":8,"name":"Liang Chen","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Chen","suffix":""},{"id":5745881,"identity":"55e1cd18-74a4-4799-8c2b-d1b27b405038","order_by":9,"name":"Kai Zhang","email":"","orcid":"","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhang","suffix":""},{"id":5745882,"identity":"0fcf7669-79c3-43ae-80b8-16ee0a076ec5","order_by":10,"name":"Shijiang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3PsUrEMBjA8ZRCXTJLSrB9AiFSqFv7KgkFu3S4SW64IcfBTeJ80EMfQugcCXTyAQQ79JbOdfDoJCZ10MEER8H8h4+Pkh9JAXC5/mLI44TOix5LMX8U/e/JkwBQE2ol3xdv+0mAjcT1hi/6VZfd15vh8HbXRfmpPKhbsuic/0xI96ge1g7Fet9eJmfNkEB8RRQpklQYCGKKBLLwEU1x2Eh2g6kmgjUGEu80eZdFgMojDvealKOVgGdF2FZmEFVp+Mo1qey3kJncSopQdY1BKxP4Ui0EJeZ/iXflcDEdZa6WJpxWMjqpy4dxXGaRiagCogbjavjw63bjcZ3fq5HrzZusB10ul+u/9gFnpGeMWpC6rAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6209-918X","institution":"Jiangsu Province Hospital and Nanjing Medical University First Affiliated Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shijiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2020-12-02 21:33:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-120644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-120644/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4110756,"identity":"f5104524-c191-4711-8267-f85c82122c21","added_by":"auto","created_at":"2020-12-08 23:04:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238284,"visible":true,"origin":"","legend":"Receiver operator curve (ROC) of model estimated probabilities of lymph node metastasis in derivation cohort (red curves) and validation cohort (blue curves). A, the ROC curves for predicting the hilar-intrapulmonary nodal metastasis. The area under curve (AUC) in the derivation cohort was 0.832 (95% CI, 0.796-0.869) and the AUC in the validation cohort was 0.872 (95%CI, 0.831-0.913). B, the ROC curves for predicting the mediastinal nodal metastasis. The AUC in the derivation cohort was 0.830 (95% CI, 0.789-0.870) and the AUC in the validation cohort was 0.823 (95%CI, 0.766-0.879).","description":"","filename":"OnlineFigure1.Png","url":"https://assets-eu.researchsquare.com/files/rs-120644/v1/b8251ed8093871a1908fdd6c.Png"},{"id":4110757,"identity":"8db2d137-9ae9-4b1b-b4a4-6c15fcd79c1b","added_by":"auto","created_at":"2020-12-08 23:04:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51969,"visible":true,"origin":"","legend":"Nomogram for estimation of the risk of hilar-intrapulmonary nodal metastasis (HNM). ","description":"","filename":"OnlineFigure2.Png","url":"https://assets-eu.researchsquare.com/files/rs-120644/v1/88367df3b3074c587a7cb834.Png"},{"id":4110758,"identity":"072d1b27-0574-4522-a70d-93c8415700f9","added_by":"auto","created_at":"2020-12-08 23:04:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53289,"visible":true,"origin":"","legend":"Nomogram for estimation of the risk of mediastinal nodal metastasis (MNM).","description":"","filename":"OnlineFigure3.Png","url":"https://assets-eu.researchsquare.com/files/rs-120644/v1/098ecf144d79930546197c0b.Png"},{"id":13631324,"identity":"23ac474a-2e57-4a8f-91f1-cae828f228f1","added_by":"auto","created_at":"2021-09-17 08:16:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":871106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-120644/v1/500dc67f-7070-45e1-95c9-c90cd6c4d4bb.pdf"},{"id":4110755,"identity":"8597f841-3fb8-4ee4-92c6-35f759450e00","added_by":"auto","created_at":"2020-12-08 23:04:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24390,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-120644/v1/21b7cdd041e5a024c878672c.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMathematical Models for Intraoperative Prediction of Metastasis to Lymph Nodes in the Hilar-intrapulmonary or the Mediastinal Region in Patients With Clinical Stage I Non-small Cell Lung Cancer: a Retrospective Cohort Study\u003c/p\u003e","fulltext":[{"header":"1. Background","content":" \u003cp\u003eNon-small cell lung cancer (NSCLC) is among the deadliest malignancies in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lobectomy plus complete lymph node (LN) dissection with removal of all ipsilateral hilar and mediastinal lymphatic tissue remains the standard surgical procedure for the treatment of dissectible lung cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For the last decade, more early-stage lung cancer has been diagnosed, partly thanks to the development of computed tomography (CT) screening [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the current era of value-driven healthcare, it is important to consider novel approaches to maintaining the curative intent pulmonary operation while decreasing unnecessary removal of surrounding healthy tissues. Tailoring LN dissection during operation for early-stage NSCLC has gradually become a particularly attractive target for value optimization because limiting LN harvest may avoid unnecessary intraoperative injury, shorten the operative time, reduce post-operative morbidity, and have better cost-effective outcomes [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariety of techniques have been developed to detect the clinical N-category, such as radiologic imaging, endoscopic and surgical techniques [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, it is still hard to find the highest-quality and most cost-effective investigation to accurately determine pathological N stage of an early NSCLC [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, there is no universally accepted method of LN dissection for this patient population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA quick and accurate prediction of the presence and the precise regions of LN metastasis of clinical stage I NSCLC before or during operation will help surgeons choose optimized surgical approaches. The authors have previously explored relevant clinicopathologic factors affecting regional LN metastasis in clinical stage I NSCLC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, we further developed mathematical models to predict regional LN status in patients with clinical stage I NSCLC and to help surgeons make reasonable decisions of LN dissection by studying the relationship between the clinicopathologic variables and the hilar-intrapulmonary nodal metastasis (HNM) or the mediastinal nodal metastasis (MNM).\u003c/p\u003e "},{"header":"2. Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThe institutional ethics committee approved the study with waiver of consent (approval no.2017-SR-097). The work was registered in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.chictr.org.cn\" target=\"_blank\"\u003ewww.chictr.org.cn\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e with a registration number (ChiCTR2000031620). Clinical data of the consecutive patients with primary lung cancer who underwent video-assisted thoracoscopic surgery at our hospital from January 2017 to September 2019 were collected and reviewed retrospectively.\u003c/p\u003e \u003cp\u003eThe enrollment criteria of this study were as follows: (1) diagnosed as having clinical stage I NSCLC based on the new International Staging System for NSCLC (National Comprehensive Cancer Network Guidelines Version 3.2014: Staging Non-Small Cell Lung Cancer); (2) underwent lobectomy with postoperative pathological confirmation of NSCLC and had complete LN dissection.\u003c/p\u003e \u003cp\u003ePatients who exhibited any one of the following conditions were excluded from this study: (1) preoperative tumour size\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026nbsp;cm on CT imaging; (2) preoperative LN\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;cm at the shortest diameter on CT imaging; (3) had evidence of distant metastasis; (4) had preoperative chemotherapy or radiotherapy; (5) had previous or coexistent tuberculosis or malignant diseases; (6) had LN dissection that did not meet the current standards of complete LN dissection (i.e., all LN stations, including stations 10\u0026ndash;14, right-hand stations 2\u0026ndash;4 and 7\u0026ndash;9, and left-hand stations 4\u0026ndash;9); (7) had synchronous lung cancers or multiple primary cancers; (8) postoperative pathological diagnosis revealed special types of pulmonary infection or other LN diseases; or (9) had incomplete clinical data.\u003c/p\u003e \u003cp\u003eEligible patients who underwent surgery before December 2018 were included in the derivation cohort to establish the models, and patients who underwent surgery after December 2018 were entered into the validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinicopathological Variables\u003c/h2\u003e \u003cp\u003eTrained chart abstractors collected clinical variables such as age, gender, smoking history, family history, tumour markers in blood, the identity of the lobe, tumour size, tumour location within the lobe. The chart abstractors also collected postoperative pathological results of the lymph node metastasis from the conventional sections as mentioned below. the Two clinicians (YZ and CM) verified the accuracy of predictor variables.\u003c/p\u003e \u003cp\u003eFor histopathologic assessment, a part of the pulmonary tumour was processed for rapidly frozen sections during operation. The remaining tumour tissue with all lymph nodes were fixed using 10% formalin, embedded in paraffin and conventionally sectioned. Both the intraoperative rapidly frozen sections and the postoperative conventional sections were kept in the department of pathology in our hospital for 10\u0026nbsp;years.\u003c/p\u003e \u003cp\u003eAll rapidly frozen sections in this study were reviewed and assessed independently by two experienced pathologists. The classification of pathological variables such as histological tumour type and grading, lymphatic vessel invasion status, bronchial mucosa and cartilage invasion status, visceral pleural invasion status and nerve invasion status was based on the consensus of these two pathologists without knowing the results of postoperative formalin-fixed paraffin-embedded sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Model Development\u003c/h2\u003e \u003cp\u003eThe method for developing mathematical models of predicting HNM and MNM was previously described in details [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For clinical use of the models, two nomograms were formulated based on proportionally converting each regression coefficient in multivariate logistic regression to a 0- to 100-point scale by using the rms package of software R 3.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.chictr.org.cn\" target=\"_blank\"\u003ewww.r-project.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using software SPSS 18.0 (IBM, Armonk, NY). The Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. In all analyses, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Patients\u0026rsquo; characteristics\u003c/h2\u003e\n\u003cp\u003eDuring the study period, 3765 consecutive patients with primary lung cancer underwent video-assisted thoracoscopic surgery. Of these, 1003 patients who met the inclusion criteria were enrolled and divided into the derivation and validation cohorts (585 and 418 patients respectively). The clinicopathologic characteristics of the patients were listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\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\u003eCharacteristics of the study population\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\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCohort, No. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDerivation (n\u0026thinsp;=\u0026thinsp;585)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eValidation (n\u0026thinsp;=\u0026thinsp;418)\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 (years)\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\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender, n (%)\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\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e309 (52.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212 (50.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276 (47.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (49.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking history\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e464 (79.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e299 (71.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (20.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119 (28.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e573 (97.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e397 (95.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (5.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCEA\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\u003e\u0026le;\u0026thinsp;5.75\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e455 (77.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e355 (84.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;5.75\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (22.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63 (15.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;8.25\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e492 (84.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e379 (90.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;8.25\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (15.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (9.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCYFRA211\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\u003e\u0026le;\u0026thinsp;2.85\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e396 (67.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326 (78.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.85\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189 (32.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (22.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;2.95\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e407 (69.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e333 (79.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.95\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 (30.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85 (20.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor position\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\u003eRight Upper Lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158 (27.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (33.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight Middle Lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (5.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRight Lower Lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e116 (19.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (14.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeft Upper Lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142 (24.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (27.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeft Lower Lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111 (19.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (15.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMixed lobes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (2.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120 (2.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\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\u003e\u0026le;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e359 (61.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320 (76.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e226 (38.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (23.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathological type\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\u003eAdenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e497 (85.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e359 (85.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 (15.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (14.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor differentiation\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\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138 (23.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e143 (34.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e224 (38.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e245 (58.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e223 (38.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBronchial mucosa and cartilage invasion\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e489 (83.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e355 (84.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (16.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63 (15.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVascular invasion\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e544 (93.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e388 (92.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (7.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVisceral pleural invasion\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e432 (73.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e365 (87.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153 (26.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (12.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNerve invasion\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\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e574 (98.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e398 (95.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (1.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Analysis of predictive factors for HNM in the derivation cohort\u003c/h2\u003e\n\u003cp\u003eThe clinicopathological factors of the patients from the derivation cohort with and without the presence of HNM were evaluated. To help yield a simple risk score model later, continuous variables were converted into categorical variables based on the receiver operating characteristic (ROC) curve analysis with the maximum Youden index for the best cut-off value (Supplementary table 1 and 2). Univariate and multivariable logistic regression analysis revealed the following significant predictors of HNM: CEA\u0026thinsp;\u0026gt;\u0026thinsp;5.75\u0026nbsp;ng/ml, CYFRA211\u0026thinsp;\u0026gt;\u0026thinsp;2.85\u0026nbsp;ng/ml, the maximum diameter of tumour\u0026thinsp;\u0026gt;\u0026thinsp;2.75\u0026nbsp;cm, tumour differentiation, presence of bronchial mucosa and cartilage invasion, and vascular invasion (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\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 logistic regression predictors of hilar-intrapulmonary lymph node metastasis\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\u003eIndependents Variable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnivariate Predictors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMultivariate Predictors\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMetastasis rate, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio (95% CI), P-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio (95% CI), P-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\u003eCEA\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;5.75\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83/455 (18.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.07 (3.34\u0026ndash;7.71), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.01 (1.85\u0026ndash;4.91), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;5.75\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69/130 (53.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCYFRA211\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;2.85\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e74/396(18.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.06 (2.08\u0026ndash;4.49), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.96 (1.25\u0026ndash;3.09), 0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.85\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78/189(41.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52/359(14.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.69 (3.16\u0026ndash;6.95), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.71 (1.73\u0026ndash;4.23), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100/226(44.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor differentiation\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI, II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53/362(14.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.66 (3.14\u0026ndash;6.90), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.26 (1.44\u0026ndash;3.54), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e99/223(44.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathological type\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e114/497 (22.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.55 (1.60\u0026ndash;4.09), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38/88 (43.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBronchial mucosa and cartilage invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e99/489(20.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.86 (3.07\u0026ndash;7.68), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.90 (1.71\u0026ndash;4.91), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53/96(55.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVascular invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e125/544(23.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.47 (3.29\u0026ndash;12.71), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.35 (2.00-9.47), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27/41(65.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVisceral pleural invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e106/432(24.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.32 (0.88\u0026ndash;1.99), 0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46/153(30.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNerve invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e143/574(24.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.56 (2.90-63.51), 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9/11(81.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Analysis of predictive factors for MNM in the derivation cohort\u003c/h2\u003e\n\u003cp\u003eThe clinicopathological factors of the patients from the derivation cohort with and without the presence of MNM were evaluated. Univariate and multivariable logistic regression analysis showed the following predictors of MNM: CEA\u0026thinsp;\u0026gt;\u0026thinsp;8.25\u0026nbsp;ng/ml, CYFRA211\u0026thinsp;\u0026gt;\u0026thinsp;2.95\u0026nbsp;ng/ml, the maximum diameter of tumour\u0026thinsp;\u0026gt;\u0026thinsp;2.75\u0026nbsp;cm, tumour differentiation, presence of bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eUnivariate and multivariate logistic regression predictors of mediastinal lymph node metastasis\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\u003eIndependents Variable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnivariate Predictors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMultivariate Predictors\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMetastasis rate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio (95% CI), P-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio (95% CI), P-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\u003eCEA\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;8.25\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64/492 (13.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.78 (4.79\u0026ndash;12.63), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.546\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.69 (2.73\u0026ndash;8.08), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;8.25\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50/93 (53.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCYFRA211\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;2.95\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58/407 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.76 (1.81\u0026ndash;4.21), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.87 (1.13\u0026ndash;3.07), 0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.95\u0026nbsp;ng/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56/178 (31.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum diameter of tumor\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39/359 (10.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.08 (2.64\u0026ndash;6.28), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.24 (1.37\u0026ndash;3.67), 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;2.75\u0026nbsp;cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75/226 (33.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTumor differentiation\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI, II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41/362 (11.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.81 (2.48\u0026ndash;5.85), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.78 (1.08\u0026ndash;2.94), 0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73/223 (32.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathological type\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101/497 (20.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68 (0.36\u0026ndash;1.27), 0.228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13/88 (14.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBronchial mucosa and cartilage invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76/489 (15.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.56 (2.21\u0026ndash;5.73), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.778\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.18 (1.24\u0026ndash;3.83), 0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38/96 (39.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVascular invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92/544 (16.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.69 (2.96\u0026ndash;10.94), 0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.57 (1.68\u0026ndash;7.57), 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22/41 (53.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVisceral pleural invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70/432 (16.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.09 (1.35\u0026ndash;3.22), 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.37 (1.41\u0026ndash;3.97), 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44/153 (28.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNerve invasion\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e110/574 (19.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.41 (0.69\u0026ndash;8.38), 0.166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4/11 (36.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 4\u003c/p\u003e\n\u003cp\u003eRecommendation of lymph node dissection for clinical stage I patients\u003c/p\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003eCombination of HNM and MNM prediction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eRecommendation for regional LN dissection\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003eHNM low risk + MNM low risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eNo need for LN dissection\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003eHNM high risk + MNM low risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eHilar and intrapulmonary LN radical dissection\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003eHNM low risk + MNM high risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eMediastinal LN radical dissection\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003eHNM low risk + MNM high risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003esystematic LN dissection\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHNM, hilar-intrapulmonary node metastasis. MNM, mediastinal node metastasis. LN, lymph node\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Development of mathematical models of predicting HNM and MNM\u003c/h2\u003e\n\u003cp\u003eAbovementioned six independent risk factors associated with HNM and seven factors with MNM were used to form mathematic models. The resulting beta coefficients in multivariate analysis were applied to calculate predicted values from the logistic equation by using the following weighted sum for HNM: x\u0026beta; = \u0026minus;2.769 + (1.102\u0026thinsp;\u0026times;\u0026thinsp;CEA) + (0.675\u0026thinsp;\u0026times;\u0026thinsp;CYFRA211) + (0.996\u0026thinsp;\u0026times;\u0026thinsp;Maximum diameter of tumor) + (0.813\u0026thinsp;\u0026times;\u0026thinsp;Tumor differentiation) + (1.064\u0026thinsp;\u0026times;\u0026thinsp;Bronchial mucosa and cartilage invasion) + (1.469\u0026thinsp;\u0026times;\u0026thinsp;Vascular invasion), and for MNM: x\u0026beta; = \u0026minus;3.256 + (1.546\u0026thinsp;\u0026times;\u0026thinsp;CEA) + (0.623\u0026thinsp;\u0026times;\u0026thinsp;CYFRA211) + (0.807\u0026thinsp;\u0026times;\u0026thinsp;Maximum diameter of tumor) +(0.579\u0026thinsp;\u0026times;\u0026thinsp;Tumor differentiation) +(0.778\u0026thinsp;\u0026times;\u0026thinsp;Bronchial mucosa and cartilage invasion) + (1.272\u0026thinsp;\u0026times;\u0026thinsp;Vascular invasion) + (0.862\u0026thinsp;\u0026times;\u0026thinsp;Visceral pleural invasion). The corresponding value of each variable in the equation was listed in Supplementary table 3 and 4. ROC curves of the models for HNM and MNM were generated and demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Validation tests of the prediction models\u003c/h2\u003e\n\u003cp\u003eThe fitted models derived from the derivation cohort were applied to the validation cohort to produce estimated values of the possibility of risk for HNM or MNM. The area under curve (AUC) of the ROC curve of the estimated value for HNM was 0.872 (95% CI, 0.831\u0026ndash;0.913) and 0.823 (95% CI, 0.766\u0026ndash;0.879) for MNM in the validation cohort, demonstrating good discriminatory power (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6 Definition of low and high risks of LN metastasis\u003c/h2\u003e\n\u003cp\u003eAccording to the maximized Youden's index, i.e., the sum of sensitivity and specificity, the optimal clinically applicable cut-off value of estimated risks was 0.209 for HNM and 0.132 for MNM (Supplementary table 5). Therefore, we defined the groups with different predicted risks by using a priori based on cut-off values at 0.209 and 0.132 of estimated possibilities for HNM and MNM respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.7 Development of prediction nomograms of LN metastasis\u003c/h2\u003e\n\u003cp\u003eWe further built two nomograms for intraoperative use in prodicting the probability of HNM and MNM for patients with clinical stage I NSCLC (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). First, we identified the points for each predictor based on the point scale at the top of the nomograms, and then summed up all points. The risk of metastasis was then obtained based on the bottom point scale. The cut-off point in the nomograms was 0.209 for HNM, and 0.132 for MNM.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe incidence of LN metastasis in patients with clinical stage I NSCLC is significantly lower than that in patients with advanced lung cancer. A randomized trial named ACOSOG Z0030 concluded that systematic mediastinal lymph node dissection could not improve the survival for patients with early‑stage NSCLC by confirming that there was no positive lymph node either in mediastinum or in hilum through presection sampling [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, other scholars pointed out that postoperative pathology has shown that even the small-sized lung cancer (\u0026lt;\u0026thinsp;2\u0026nbsp;cm) had hilar and mediastinal node metastasis with an incidence of up to 20% [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, patients with positive MNM exhibited a 20\u0026ndash;38% incidence of skip metastasis, a phenomenon in which MNM occurs without the involvement of HNM [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, accurate integration of lymph node staging in patients with clinical stage I NSCLC is important in guiding the choices of surgical treatments.\u003c/p\u003e\n\u003cp\u003eIn this study, we identified six clinical variables as the independent predictors in common for regional lymph node metastases including the hilar-intrapulmonary region and the mediastinal region, which is consistent with the literature [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the visceral pleural invasion was only associated with the MNM. This phenomenon seems to indicate that we should discuss the HNM and MNM separately. We think this is based on the following anatomical structures of LN system in the lung. The lymph nodes associated with the cancer metastasis are widely labelled using a system of numerical levels and assigned names based on their anatomical locations. First, the hilar-intrapulmonary lymph nodes (groups 10\u0026ndash;14), and second, the mediastinal lymph nodes (includes group 2R, 3, 4R, 7, 8, 9R, and group 4L, 5, 6, 7, 8, 9L). Generally, the sequence of LN metastasis of central NSCLC should be step by step as follows: along the bronchial tree from the intrapulmonary region to hilar, and then to the mediastinal region. Different from the central NSCLC, the LN metastasis of peripheral NSCLC with the peripheral capsule being invaded may incline to skip to MNM without HNM due to the lymphatic capillaries directly from the peripheral membrane to the mediastinal lymph nodes [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe rapid pathological results during operation would help surgeons to select the right procedures for the patients, for instance, wedge resection, segmentectomy, or lobectomy [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, surgeons do not know which pattern to choose for lymph node dissection because of the complicated lymphatic spreading of lung cancer. Therefore, we creatively developed two utilizable prediction models from a logistic equation for regional LN metastases in patients with clinical stage I NSCLC. In terms of clinical relevance, the models provide more precise estimates of regional LN metastasis during operation for individual patients with clinical stage I NSCLC. Especially, the model for MNM is a good supplement for surgeons to improve their decision-making process for systematic LN resection.\u003c/p\u003e\n\u003cp\u003eThere were some published models developed for the assessment of lymph node disease of lung cancer [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Some either only focused on the outcome of MNM, or only the preoperative variables were included while appropriate candidates for limited LN resection should be with pathological confirmation of negative LNs, both in the hilar and the mediastinal regions. For these reasons, none of these models is commonly employed in clinical practice. In our models, on the other hand, all variables, from the radiographic size of tumour to visceral pleural invasion, are available before or during operation by regular measures in routine clinical practice without consuming any extra resources or time. The models are even easier to use in clinical practice with the associated nomograms guiding surgeons to choose the optimized method for LN dissection rapidly.\u003c/p\u003e\n\u003cp\u003eIn a case of clinical stage I NSCLC, when intraoperative rapid pathological results reveal invasive lung cancer, standard lobectomy will be performed with the following recommendations for LN dissection which are based on the patient\u0026rsquo;s category of the prediction models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). (1) When HNM low risk plus MNM low risk, there is no need to dissect the lymph nodes or only regional LN sampling is adequate. (2) When HNM high risk plus MNM low risk, it usually refers to a central tumour involving the bronchial tree. In this case, complete dissection of hilar-intrapulmonary LNs is a requirement but no need to dissect mediastinal lymph nodes or just do regional LN sampling in the mediastinum. (3) When HNM low risk plus MNM high risk, it usually indicates skipping metastasis in peripheral lung cancer invading the lung membrane. In this case, complete mediastinal LN dissection is required but no need for hilar-intrapulmonary LN dissection or just do regional LN sampling in this region. (4) When HNM high risk plus MNM high risk, a systematic LN dissection is required, including the hilar-intrapulmonary region and the mediastinal region.\u003c/p\u003e\n\u003cp\u003eGiven the retrospective nature of this single-institution study, selection bias is inherent in our study population. Although we validated the models, they still need to be validated by patients from comparative centres. The serum status of CEA and CYFRA221 was predictive factors in our models, but the kinds of tumour marker may not be the same at different hospitals which limits the application of the model. Moreover, the interval between blood test of tumour markers and surgery was not uniformly standardized, which may exert an uncertain impact on the serum status. As our study did not include the tumor recurrent status or the survival rate in this patient population, the relationship between survival rate and predictive value is unknown [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Last but not least, there occurs inaccuracy to some extent on the rapid pathological results of the risk-predicting variables during operation, such as visceral pleural invasion, bronchial mucosa and cartilage invasion, and vascular invasion. However, it could be improved in the near future with the development of advanced tools for pathological diagnosis.\u003c/p\u003e"},{"header":"5. Conclusions","content":" \u003cp\u003eTaken together, the mathematical models for prediction of regional lymph node metastasis were accurate and easy‑to‑use. Based on the patients\u0026rsquo; clinicopathologic variables before and during operation, these models are helpful in the surgical decision‑making process for LN dissection in patients with clinical stage I NSCLC.\u003c/p\u003e "},{"header":"List of abbreviations","content":"\u003cp\u003eAUC, area under curve\u003c/p\u003e\n\u003cp\u003eCis, confidence intervals\u003c/p\u003e\n\u003cp\u003eCT, computed tomography\u003c/p\u003e\n\u003cp\u003eHNM, hilar-intrapulmonary nodal metastasis\u003c/p\u003e\n\u003cp\u003eLN, lymph node\u003c/p\u003e\n\u003cp\u003eMNM, mediastinal nodal metastasis\u003c/p\u003e\n\u003cp\u003eNSCLC, non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eORs, odds ratios\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe Ethics Committee of Nanjing Medical University approved the study with waiver of consent (approval no.2017-SR-097).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003eStudy concept and design: YZ, JD, CM, SZ.\u003c/p\u003e\n\u003cp\u003eAcquisition of Data: YZ, JD, CM, FZ, HL, GP, WW, JL, LC, KZ, SZ.\u003c/p\u003e\n\u003cp\u003eDrafting of the article: YZ, JD, CM, KZ, SZ.\u003c/p\u003e\n\u003cp\u003eCritical revision for important intellectual content: all authors.\u003c/p\u003e\n\u003cp\u003eFinal approval of the manuscript:all authors.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eReif MS, Socinski MA, Rivera MP (2000) Evidence-based medicine in the treatment of non-small-cell lung cancer. 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International Journal of Surgery 66: 53-61\u003c/li\u003e\n\u003cli\u003eDarling GE, Allen MS, Decker PA, et al (2011) Randomized trial of mediastinal lymph node sampling versus complete lymphadenectomy during pulmonary resection in the patient with N0 or N1 (less than hilar) non-small cell carcinoma: results of the American College of Surgery Oncology Group Z0030 Trial. J Thorac Cardiovasc Surg 141:662\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eEl-Chemaly S, Levine SJ, Moss J (2008) Lymphatics in lung disease. Ann N Y Acad Sci 1131:195\u0026ndash;202.\u003c/li\u003e\n\u003cli\u003eBille A, Woo KM, Ahmad U, et al (2017) Incidence of occult pN2 disease following resection and mediastinal lymph node dissection in clinical stage I lung cancer patients. Eur J Cardiothorac Surg 51:674\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eFiorelli A, Sagan D, Mackiewicz L, et al (2015) Incidence, Risk Factors, and Analysis of Survival of Unexpected N2 Disease in Stage I Non-Small Cell Lung Cancer. Thorac Cardiovasc Surg 63:558\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eMatsuguma H, Oki I, Nakahara R, et al (2013) Comparison of three measurements on computed tomography for the prediction of less invasiveness in patients with clinical stage I non-small cell lung cancer. The Annals of Thoracic Surgery 95:1878\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eLee K, Kim HR, Kim DK, et al (2017) Post-recurrence survival analysis of stage I non-small-cell lung cancer. Asian Cardiovasc Thorac Ann 25:623\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eIsaka M, Kojima H, Takahashi S, et al (2018) Risk factors for local recurrence after lobectomy and lymph node dissection in patients with non-small cell lung cancer: Implications for adjuvant therapy. Lung Cancer 115:28\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eStiles BM, Kamel MK, Nasar A, et al (2017) The importance of lymph node dissection accompanying wedge resection for clinical stage IA lung cancer. Eur J Cardiothorac Surg 51:511\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eEl-Sherief AH, Lau CT, Carter BW, et al (2018) Staging Lung Cancer: Regional Lymph Node Classification. Radiol Clin North Am 56:399\u0026ndash;409.\u003c/li\u003e\n\u003cli\u003eEl-Sherief AH, Lau CT, Obuchowski NA, et al (2017) Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning. Chest 151:776\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eChen KZ, Yang F, Wang X, et al (2015) A clinical prediction model for N2 lymph node metastasis in clinical stage I non-small cell lung cancer. Beijing Da Xue Xue Bao 47:295\u0026ndash;301.\u003c/li\u003e\n\u003cli\u003eZhang Y, Sun Y, Xiang J, et al (2012) A prediction model for N2 disease in T1 non-small cell lung cancer. J Thorac Cardiovasc Surg 144:1360\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eChen K, Yang F, Jiang G, et al (2013) Development and validation of a clinical prediction model for N2 lymph node metastasis in non-small cell lung cancer. The Annals of Thoracic Surgery 96:1761\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMoulla Y, Gradistanac T, Wittekind C, et al (2019) Predictive risk factors for lymph node metastasis in patients with resected non-small cell lung cancer: a case control study. J Cardiothorac Surg 14:11\u003c/li\u003e\n\u003cli\u003eVerdial FC, Madtes DK, Hwang B, et al (2019) Prediction model for nodal disease among patients with non\u0026ndash;small cell lung cancer. Ann Thorac Surg 107:1600\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eYendamuri S, Dhillon SS, Groman A, et al (2018) Effect of the number of lymph nodes examined on the survival of patients with stage I non-small cell lung cancer who undergo sublobar resection. J Thorac Cardiovasc Surg 156:394\u0026ndash;402.\u003c/li\u003e\n\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":"Lymph node metastasis, Hilar-intrapulmonary region, Mediastinal region, Prediction model, Non-small cell lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-120644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-120644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground: \u003c/em\u003eIt remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMethods: \u003c/em\u003eThe clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analysed for their association with the HNM or the MNM. After analysing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients.\u003c/p\u003e\u003cp\u003e\u003cem\u003eResults: \u003c/em\u003eIt was confirmed that CEA (\u0026gt; 5.75 ng/ml), CYFRA211 (\u0026gt; 2.85 ng/ml), the maximum diameter of tumour (\u0026gt; 2.75 cm), tumour differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and CEA (\u0026gt;8.25 ng/ml), CYFRA211 (\u0026gt; 2.95 ng/ml), the maximum diameter of tumour (\u0026gt; 2.75 cm), tumour differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power.\u003c/p\u003e\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e Our predictive models are helpful in the decision‑making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC.\u003c/p\u003e","manuscriptTitle":"Mathematical Models for Intraoperative Prediction of Metastasis to Lymph Nodes in the Hilar-intrapulmonary or the Mediastinal Region in Patients With Clinical Stage I Non-small Cell Lung Cancer: a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-08 23:04:20","doi":"10.21203/rs.3.rs-120644/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":"51e78043-93ae-40ca-942d-40f557e30845","owner":[],"postedDate":"December 8th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1380413,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2020-12-08T23:04:20+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-08 23:04:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-120644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-120644","identity":"rs-120644","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}