A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma

A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma | 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 A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma Xinyu Zhu, Xinyu Jia, Shibing Teng, Kai Fu, Jiawei Chen, Jun Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5267564/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 Purpose A precise assessment of lymph nodal status is essential for guiding an individualized treatment plan in lung adenocarcinoma patients. A novel nomogram using easily accessible indicators was developed and validated in this study to predict CT-negative lymph nodal metastasis. Methods Between September 2020 and December 2023, data from 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection with systemic lymph node dissection or sampling were retrospectively reviewed. Risk factors associated with lymph nodal metastasis were identified using univariable and multivariable logistic regression analyses. Subsequently, a nomogram was developed on basis of these identified parameters. The performance and validity of the nomogram were evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and bootstrap resampling techniques. Results Four predictors (primary tumor location, primary tumor SUVmax value, N1 lymph node SUVmax, and N2 lymph node SUVmax) were identified and incorporated into the nomogram. The nomogram exhibited notable discrimination, evidenced by an area under the receiver operating characteristic (ROC) curve of 0.825 (95% CI: 0.749–0.886, P < 0.001). Excellent concordance between the predicted and observed probabilities of lymph nodal involvement was demonstrated by the calibration curve. Furthermore, decision curve analysis indicated a net benefit associated with the use of our nomogram. Conclusion The nomogram demonstrated efficacy and practicality in predicting CT-negative lymph node metastasis for lung adenocarcinoma patients. It holds potential to offer valuable treatment guidance for clinicians. Positron emission tomography/computed tomography (PET/CT) lung adenocarcinoma lymph nodal metastasis nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Lung cancer represents a predominant cause of cancer-specific mortality globally.(Nie et al. 2021 ; Wang S. et al. 2021 ) Non-small cell lung cancer (NSCLC) constitutes approximately 80% of lung cancer cases, with adenocarcinoma (AC) being the most prevalent histological subtype within NSCLC.(Arbour and Riely 2019 ; Wang S. et al. 2021 ) Accurate lymph node staging is essential in the comprehensive management of lung cancer patients, influencing both surgical decision-making and the administration of adjuvant therapies.(Ran et al. 2021 ) Consequently, the development of an effective and practical method for predicting lymph nodal metastasis in patients with AC is of critical clinical significance. Pathological biopsy, necessitating invasive techniques like endobronchial ultrasound-guided biopsies and mediastinoscopy, remains the gold-standard reference for determining lymph nodal status in the preoperative setting. However, the routine implementation of these procedures heightens the risk of overdiagnosis and appears to offer no additional benefits beyond confirming an N0 pathological state in patients without lymph nodal metastasis (LNM).(Zhong et al. 2023 ) Furthermore, the feasibility and precision of safely conducting an invasive procedure are constrained by the potential for additional costs, trauma, and complications, particularly in patients with substantial comorbidities and diminutive lymph nodes. Computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) serve as crucial non-invasive modalities for tumor detection, diagnosis, staging, and clinical decision-making in individuals with lung cancer. CT is capable of diagnosing lymph nodes based on their size; however, it lacks sufficient accuracy for evaluating lymph nodes with small lesions. PET/CT provides a concurrent anatomical and metabolic characterization of lesions, yet its accuracy is not entirely reliable to eliminate the need for pathological confirmation of nodal malignancy.(Mattes et al. 2015 ) Moreover, infections and inflammatory processes can lead to false-positive results. Nomograms, which integrate multiple risk factors rather than depending on a single variable, have been demonstrated to be a reliable and effective method for aiding surgeons in the formulation of more precise diagnostic, therapeutic, and prognostic strategies.(Balachandran et al. 2015 ; Fu et al. 2021 ) Several models have been developed to predict lymph nodal metastasis in lung cancer patients using PET/CT-related variables.(Lv et al. 2021 ; Mattes et al. 2015 ; Wei et al. 2023 ) However, predictive models for CT-negative patients with AC remain limited. Hence, this study sought to develop and validate a practical nomogram on basis of PET/CT to enhance noninvasive nodal evaluation in CT-negative patients with AC. 2. Methods 2.1 Patients We conducted a retrospective enrollment of 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection accompanied by systemic lymph node dissection or sampling at the Thoracic Surgery Department of The First Affiliated Hospital of Soochow University (Suzhou, China) from September 2020 to December 2023. The inclusion criteria for this study were as follows: (1) Patients had undergone PET/CT imaging during their hospitalization prior to surgical operation, without evidence of distant metastasis detected; (2) No enlarged lymph nodes were identified on CT scans, with lymph nodes measuring less than 1 cm in the shortest dimension; (3) Patients were pathologically diagnosed with primary adenocarcinoma, including both classic adenocarcinoma and mucinous adenocarcinoma; (4) Necessary clinicopathological data of the patient were complete. The exclusion criteria were as follows: (1) PET/CT scanning was performed in other institution; (2) Pleural metastasis was revealed during surgery; (3) CT scan indicated that the short axis of the lymph node exceeded 1 cm; (4) Systematic dissection or sampling of hilar and mediastinal lymph nodes was not performed; (5) Final pathological diagnosis was not adenocarcinoma. Baseline clinicopathological data, encompassing age, gender, smoking history, peripheral blood cell parameters, pathological findings, and initial PET/CT data, were extracted from the HaiTai electronic medical record system (Nanjing, Jiangsu, China). Primary tumors situated within the proximal third of the hemithorax were classified as central-type tumors, whereas those located beyond the proximal third of the hemithorax were categorized as peripheral-type tumors.(Jin et al. 2017 ) This study was retrospectively conducted following the principles delineated in the Declaration of Helsinki (as revised in 2013) and received approval from the ethical committee and institutional review board of The First Affiliated Hospital of Soochow University (No. 2024349). Individual consent was waived for this retrospective analysis. All data were anonymized to ensure the protection of personal privacy. 2.2 18 F-FDG PET/CT examination All patients underwent integrated FDG PET/CT imaging prior to surgical operation. Patients fasted for a minimum of 6 hours to ensure their blood glucose levels were below 11 mmol/L before receiving an 18F-FDG injection, with a dosage ranging from 4.07 to 5.55 MBq/kg. Approximately 40 to 60 minutes post-injection, PET/CT scans were conducted from the base of the skull to the mid-thigh region, with an acquisition time of 2 to 3 minutes per bed position. The ordered subset expectation-maximization algorithm was used to conduct image reconstruction. The imaging was carried out using a Discovery PET/CT system (General Electric Medical Systems, Milwaukee, WI, USA) with low-dose CT parameters set at 140 kV, 120 mA, a transaxial field of view of 70 cm, and a slice thickness of 3.75 mm. The maximum standardized uptake value (SUVmax) of the tumor and suspected lymph nodes was ascertained by delineating a region of interest around each respective area. For part-solid tumors, tumor size was determined by measuring the solid component of the lesion. Final pathological findings were utilized as the gold standard to compare with PET/CT results. 2.3 Blood parameters Each patient received a routine blood test during the week preceding surgery. The quantities of monocytes, neutrophils, platelets, and lymphocytes were measured. Subsequently, the ratios of neutrophils to lymphocytes (NLR), lymphocytes to monocytes (LMR), and platelets to lymphocytes (PLR) were individually calculated. 2.4 Lymph nodal staging Tumor staging was assessed following the 8th edition of the TNM classification for NSCLC.(Goldstraw et al. 2016 ) Lymph nodal metastasis was defined as pathologically confirmed pN1 or pN2 status, whereas those without such confirmation were categorized as pN0. Clinically, lymph nodes were deemed positive if they exceeded 1 cm in the shortest dimension on CT or exhibited an SUVmax greater than 2.5 PET/CT. 2.5 Surgical procedures Generally, lung cancer is typically excised via wedge resection, segmentectomy, lobectomy, or bilobectomy, accompanied by systemic lymph node dissection or sampling. Complete resection is characterized by the achievement of both macroscopic and microscopic tumor-free margins. At least 1 N1 (hilar, interlobar, and intrapulmonary) station and 3 N2 (mediastinal) stations including the subcarinal station were excised. 2.6 Statistical analysis The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of PET/CT for the evaluation of lymph nodal metastasis were assessed using histological results as the reference standard. Continuous variables with non-normal distributions were reported as medians (range) and analyzed using the Mann–Whitney U test. Categorical variables were expressed as numbers (rates) and analyzed using the χ² test or Fisher's exact test. Univariable and multivariable binary logistic regression analyses were conducted to identify independent risk factors for lymph nodal metastasis, which were subsequently used to construct a nomogram. Receiver operating characteristic (ROC) curves were generated using MedCalc, and the area under the curves (AUC) was calculated to demonstrate the predictive accuracy of the nomogram. Internal validation of the nomogram was performed with 1000 bootstrap resamples. Harrell's C-index was employed to evaluate the discrimination performance. Calibration plots and decision curve analysis (DCA) were utilized to assess the calibration and clinical utility of the nomogram. Clinical baseline data analysis was conducted using the SPSS statistical package (version 26.0, IBM Corp., Armonk, NY, USA). The nomogram, calibration plot, and DCA curve were generated using R software version 4.4.1. Two-sided p < 0.05 was considered statistically significant. 3. Results From September 2020 to December 2023, a total of 132 lung adenocarcinoma patients consisting of 54 males and 78 females were enrolled in the study. All patients underwent integrated FDG PET/CT before pulmonary resection and were at least diagnosed with CT negative (the shortest dimension of lymph nodes less than 1 cm). Table 1 presents the diagnostic performance of PET-CT lymph nodal staging in lung adenocarcinoma patients. The sensitivity and specificity for N1 lymph nodes were 34.6% and 91.5%, respectively; for N2 lymph nodes, 50% and 99.1%, respectively; and for the overall patient cohort, 45.2% and 90.1%, respectively. The overall diagnostic accuracy was 79.5%. In all patients, the incidence of lymph nodal metastasis was 23.5% (31/132). Among these patients, 26 patients had N1 LNM and 18 patients had N2 LNM. Of all the patients, 6 underwent wedge resection, 3 underwent segmentectomy, 120 underwent lobectomy and 3 underwent bilobectomy. Table 1 Overview of FDG-PET/CT in diagnosing LNM (n = 132) Sensitivity % (n) Specificity % (n) Positive predictive value % (n) Negative predictive value % (n) Accuracy % (n) N1 LNM 34.6(9/26) 91.5(97/106) 50(9/18) 85.1(97/114) 80.3(106/132) N2 LNM 50(9/18) 99.1(113/114) 90(9/10) 92.6(113/122) 92.4(122/132) Overall LNM 45.2(14/31) 90.1(91/101) 58.3(14/24) 84.3(91/108) 79.5(105/132) Abbreviations: LNM, lymph nodal metastasis 3.1 Clinicopathological features and nomogram construction All patients’ data including demographic, imaging, and pathological characteristics are summarized in Table 2 . The distribution of these characteristics was compared between the lymph nodes negative (LN-) group and the lymph nodes positive (LN+) group. Compared to LN- patients, LN + patients exhibited a larger tumor size (29 vs 23; P = 0.018) and higher tumor SUVmax value (7.3 vs 2.9; P = 0.000). More patients in the LN + group had a central-type tumor (35.5% vs 13.9%; P = 0.007), a pure solid nodule (90.3% vs 71.3%; P = 0.031), and were with N1 LN SUVmax value ≥ 2.5 (29% vs 8.9%; P = 0.011), N2 LN SUVmax value ≥ 2.5 (29% vs 1%; P = 0.000), poor tumor grade (29% vs 11.9%; P = 0.045), solid pattern (58.1% vs 30.7%; P = 0.006). However, no statistically significant differences were observed in peripheral blood cell parameters. Independent risk factors for LNM were identified through univariable and multivariable binary logistic regression analyses (Table 3 ). Ultimately, tumor location (OR, 3.90; 95% CI, 1.21–12.53; P = 0.022), tumor SUVmax value (OR, 1.16; 95% CI, 1.0-1.35; P = 0.049), N1 LN SUVmax value (OR, 5.25; 95% CI, 1.37–20.04; P = 0.015) and N2 LN SUVmax value (OR, 21.89; 95% CI, 2.27-210.86; P = 0.008) were identified for the construction of the nomogram (Fig. 1 ). Table 2 Clinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis Variables LN(-) cohort (n = 101) LN(+) cohort (n = 31) P value Age, years (Median, range) 64(37–85) 66(34–77) 0.711 Gender, n (%) Female/Male 61(60.4)/40(39.6) 17(54.8)/14(45.2) 0.582 Smoking history, n (%) Never/Ever 83(82.2)/18(17.8) 29(93.5)/2(6.5) 0.208 PLR (Median, range) 109.3(8.7-318.8) 119.6(43.1-293.5) 0.673 NLR (Median, range) 1.8(0.1–9.5) 1.8(0.5–4.8) 0.741 LMR (Median, range) 4(0.5–33.7) 3.5(1.6–8.9) 0.492 Tumor size, mm (Median, range) 23(10–84) 29(13–61) 0.018* Tumor side, n (%) Left/Right 45(44.6)/56(55.4) 16(51.6)/15(48.4) 0.490 Tumor location, n (%) Central/Peripheral 14(13.9)/87(86.1) 11(35.5)/20(64.5) 0.007* Pure solid nodule, n (%) Yes/No 72(71.3)/29(28.7) 28(90.3)/3(9.7) 0.031* Tumor SUVmax value (Median, range) 2.9(0-17.8) 7.3(1.7–18.5) 0.000* N1 LN SUVmax value, n (%) ≥ 2.5/<2.5 9(8.9)/92(91.1) 9(29)/22(71) 0.011* N2 LN SUVmax value, n (%) ≥ 2.5/<2.5 1(1)/100(99) 9(29)/22(71) 0.000* Tumor grade, n (%) Well or Moderate/Poor 89(88.1)/12(11.9) 22(71)/9(29) 0.045* Pathological subtype Absent/Present, n (%) Lepidic pattern 83(82.2)/18(17.8) 28(90.3)/3(9.7) 0.422 Acinar pattern 9(8.9)/92(91.1) 7(22.6)/24(77.4) 0.084 Papillary pattern 27(26.7)/74(73.3) 9(29)/22(71) 0.801 Micropapillary pattern 45(44.6)/56(55.4) 12(38.7)/19(61.3) 0.566 Solid pattern 70(69.3)/31(30.7) 13(41.9)/18(58.1) 0.006* Mucinous pattern 98(97)/3(3) 30(96.8)/1(3.2) 1.000 Abbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. * p <0.05 Table 3 Univariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma Variables Univariable Multivariable OR 95%CI P value OR 95%CI P value Tumor size, mm 1.03 1.0-1.07 0.051 Tumor location Peripheral vs Central 3.42 1.35–8.64 0.009* 3.90 1.21–12.53 0.022* Pure solid nodule No vs Yes 3.76 1.06–13.33 0.04* 1.06 0.24–4.61 0.94 Tumor SUVmax value 1.26 1.12–1.41 0.000* 1.16 1.0-1.35 0.049* N1 LN SUVmax value <2.5 vs ≥ 2.5 4.182 1.49–11.77 0.007* 5.25 1.37–20.04 0.015* N2 LN SUVmax value <2.5 vs ≥ 2.5 40.91 4.93-339.77 0.001* 21.89 2.27-210.86 0.008* Tumor grade Well or Moderate vs Poor 3.03 1.14–8.10 0.027* 1.512 0.34–6.74 0.59 Solid pattern Absent vs Present 3.13 1.34–7.17 0.007* 2.29 0.71–7.37 0.16 Abbreviations: CI: confidence interval; OR: odds ratio. * p <0.05 3.2 Nomogram evaluation Our model demonstrated robust discrimination, evidenced by a C-index of 0.810 for LNM when evaluated using 1000 bootstrap resamples. Internal validation further corroborated this performance, yielding a high C-index value of 0.825. Calibration curves shown in Fig. 2 indicated strong concordance between the predicted and actual probabilities of LNM. Decision curve analysis revealed that, for patients with a threshold probability exceeding 10%, the nomogram provided greater clinical benefit compared to both the treat-all and treat-none strategies (Fig. 3 ). Additionally, the area under the ROC curve was 0.825 (95% CI 0.749–0.886, P < 0.001) (Fig. 4 ), signifying a notable discrimination capability. 4. Discussion A novel nomogram was developed and validated in this retrospective study to predict the incidence of lymph node metastasis in patients with lung adenocarcinoma, utilizing readily accessible PET/CT indicators. The analysis identified primary tumor location, tumor SUVmax, N1 lymph node SUVmax ≥ 2.5, and N2 lymph node SUVmax ≥ 2.5 as independent risk factors. Our nomogram indicated that patients with central-type lung adenocarcinoma, elevated tumor SUVmax, and N1 and N2 lymph node SUVmax ≥ 2.5 are at a higher risk of lymph nodal involvement. The model demonstrated robust discrimination and calibration. Hence, it might possess potential clinical utility to evaluate preoperative lymph nodal status in lung adenocarcinoma patients and might provide clinicians with valuable treatment guidance. Clinicians predominantly depend on specific clinical features, particularly imaging characteristics, to assess the risk of LNM in lung cancer during routine practice. Several studies have suggested that metabolic and morphologic parameters observed in PET/CT scans, such as tumor size, tumor location, consolidation ratio, and metabolic value, may offer valuable insights into the likelihood of LNM.(Kagimoto et al. 2020 ; Kameyama et al. 2022 ; Nie et al. 2021 ; Zhong et al. 2023 ) Nevertheless, this subjective evaluation is limited in its ability to comprehensively estimate the probability of LNM due to the variability in clinicians' experiences. This limitation has direct implications for the management strategies employed for individual patients. Therefore, various models utilizing PET/CT have been developed and validated to predict lymph nodal involvement in NSCLC.(Fu et al. 2021 ; Lv et al. 2021 ; Mattes et al. 2015 ; Wei et al. 2023 ) Whereas, to the best of our knowledge, there are limited studies specifically aimed at constructing prediction models for LNM in lung adenocarcinoma. Consistent with previous studies, factors such as tumor location, the SUVmax of the primary tumor, and suspicious lymph nodes have been identified as significant risk factors for lymph nodal metastasis.(Fu et al. 2021 ; Kawamoto et al. 2023 ; Lv et al. 2021 ; Wei et al. 2023 ) Notably, central lung cancer characterized by a high SUVmax value of both the primary tumor and suspicious lymph nodes exhibits a markedly higher prevalence of lymph nodal metastasis. Given that our model was designed for lung adenocarcinoma patients, which could be confirmed by preoperative biopsy or intraoperative frozen section, we incorporated the precise SUVmax of the primary tumor into our model to minimize measurement error. However, for the suspicious lymph nodes, 2.5 was used as the cut-off value associated with positive lymph nodes, which is more pragmatic and consistent with current clinical habits.(Kaseda et al. 2016 ; Miyasaka et al. 2013 ) We exclusively utilized the lymph node station with the highest SUVmax, despite the potential inaccuracy arising from the possibility that a single individual may present with multiple lymph nodes exhibiting abnormal SUVmax. This approach is justifiable for two reasons: firstly, our study aimed to evaluate overall lymph nodal involvement rather than the risk associated with specific lymph nodal stations; secondly, distinguishing between metastatic hilar and interlobar lymph nodes and those that are non-metastatic is challenging due to their proximity to the bronchus and similar soft tissue characteristics.(Dyas et al. 2018 ; Fu et al. 2021 ) Thus, both N1 and N2 lymph node SUVmax were included in our model, with the latter demonstrating a more substantial contribution to the risk of LNM. Fundamentally, the identification of LNM and improvement of staging accuracy is determined depending on complete and en bloc resection of each lymph node station.(Darling et al. 2011 ; Doddoli et al. 2005 ) In consideration of the importance of inflammation in tumor initiation, progression, and metastasis, Wei, Chen, and Wang reported that peripheral blood cell parameters such as NLR and PLR were significantly elevated in LNM patients.(Chen et al. 2020 ; Wang Y. et al. 2021 ; Wei et al. 2023 ) However, no statistical difference was found in our cohort. This may be derived from the different biological characteristics of adenocarcinoma and other types of NSCLC. Additionally, micropapillary and/or solid subtypes of lung adenocarcinoma have been demonstrated to correlate with LNM and poor prognosis.(Hung et al. 2016 ; Zhao et al. 2016 ) While the pathological subtype significantly influenced the risk of lymph node metastasis in univariable analysis, it was not identified as an independent predictor in our multivariable analysis. This may be because previous studies did not incorporate both PET/CT-related parameters and clinicopathological features simultaneously. Thus, these intriguing findings require to be validated by further studies with a larger sample size. Notably, some clinicopathologic factors such as vascular invasion, pleural invasion, and the existence of tumor spread through air spaces (STAS) were not incorporated into our prediction model, despite numerous studies reporting the potential increase of LNM in lung adenocarcinoma patients presenting with these characteristics.(Hung et al. 2016 ; Moon et al. 2016 ; Vaghjiani et al. 2020 ) For the moment, the determination of these pathological characteristics by preoperative biopsy or intraoperative FS is challenging for most pathologists due to limited access to the tissue and detection techniques, causing relatively low accuracy. Nodal biopsy is considered the gold standard for lymph node staging in the preoperative setting, but the potential risk of invasive procedures cannot be ignored. It is thus necessary to balance the advantages and disadvantages of this dual-nature procedure to individualize the lymph node staging for lung cancer patients.(Czarnecka-Kujawa and Yasufuku 2017 ; Detterbeck et al. 2007 ) Besides, in surgical decision-making, sublobar resection and lobe-specific lymph node dissection are increasingly utilized for the surgical treatment of early-stage NSCLC due to more lung parenchyma preservation and less surgical trauma. However, lobectomy with systematic lymph node dissection is still the more appropriate choice if LNM occurs.(Dezube et al. 2022 ; Zhao et al. 2017 ) Therefore, it is advisable to recommend more aggressive diagnostic and therapeutic strategies for patients predicted by the model to have a high incidence of lymph node metastasis. Ultimately, our model may aid in identifying patients at high risk for lymph node involvement, thereby preventing missed opportunities for perioperative adjuvant therapy. Several limitations of this study warrant acknowledgment. Firstly, selection bias was unavoidable in this single-center retrospective study, raising questions about the generalizability of our findings to other populations. There still exists some unknown potential biases between the groups although multivariable analysis was conducted to balance the apparent biases. Secondly, all included cases were adenocarcinomas. Different histological types exhibit distinct radiological phenotypes and tumor aggressiveness, which contribute to heterogeneity in metastatic behavior. Consequently, patients were divided into two groups based solely on lymph node metastasis status, a constraint necessitated by the small sample size. Additionally, PET/CT imaging is not a mandatory preoperative procedure for every patient within our department. Consequently, our analysis was limited to the subset of patients who had undergone this imaging modality. Despite conducting internal validation to mitigate adverse influence and calibrate the model, external validation using data from other centers is necessary to ensure the generalizability of this nomogram. Table 1 Overview of FDG-PET/CT in diagnosing LNM (n = 132) Declarations Conflict of interest The authors declare no conflicts of interest. Funding information This work was supported by the grants from National Natural Science Foundation of China Author Contribution Design: XY.Z., XY.J. and C.L.; Collection of statistics: SB.T.; Statistical analysis: JW.C. and K.F.; Manuscript writing: XY.Z., J.Z. and C.L. All authors reviewed the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Arbour KC, Riely GJ (2019) Systemic Therapy for Locally Advanced and Metastatic Non-Small Cell Lung Cancer: A Review. JAMA 322(8):764–774. https://doi.org/10.1001/jama.2019.11058 Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):e173–e180. https://doi.org/10.1016/s1470-2045(14)71116-7 Chen JL, Wu JN, Lv XD, Yang QC, Chen JR, Zhang DM (2020) The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer. PLoS ONE 15(8):e0237947. https://doi.org/10.1371/journal.pone.0237947 Czarnecka-Kujawa K, Yasufuku K (2017) The role of endobronchial ultrasound versus mediastinoscopy for non-small cell lung cancer. J Thorac Dis 9(Suppl 2):S83–S97. https://doi.org/10.21037/jtd.2017.03.102 Darling GE, Allen MS, Decker PA, Ballman K, Malthaner RA, Inculet RI, Jones DR, McKenna RJ, Landreneau RJ, Rusch VW, Putnam JB Jr. (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(3):662–670. https://doi.org/10.1016/j.jtcvs.2010.11.008 Detterbeck FC, Jantz MA, Wallace M, Vansteenkiste J, Silvestri GA, and American College of Chest P (2007). Invasive mediastinal staging of lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). Chest , 132 (3 Suppl), 202S-220S. https://doi.org/10.1378/chest.07-1362 Dezube AR, Mazzola E, Deeb A, Wiener DC, Marshall MB, Rochefort MW, Jaklitsch MT (2022) Mandatory Nodal Evaluation During Resection of Clinical T1a Non-Small Cell Lung Cancers. Ann Thorac Surg 113(5):1583–1590. https://doi.org/10.1016/j.athoracsur.2021.06.078 Doddoli C, Aragon A, Barlesi F, Chetaille B, Robitail S, Giudicelli R, Fuentes P, Thomas P (2005) Does the extent of lymph node dissection influence outcome in patients with stage I non-small-cell lung cancer? Eur J Cardiothorac Surg 27(4):680–685. https://doi.org/10.1016/j.ejcts.2004.12.035 Dyas AR, King RW, Ghanim AF, Cerfolio RJ (2018) Clinical Misstagings and Risk Factors of Occult Nodal Disease in Non-Small Cell Lung Cancer. Ann Thorac Surg 106(5):1492–1498. https://doi.org/10.1016/j.athoracsur.2018.05.045 Fu Y, Xi X, Tang Y, Li X, Ye X, Hu B, Liu Y (2021) Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer. Thorac Cancer 12(15):2189–2197. https://doi.org/10.1111/1759-7714.14066 Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V, International Association for the Study of Lung Cancer, Prognostic Factors Committee S, Participating AB I, International Association for the Study of Lung Cancer S, Prognostic Factors Committee Advisory B, and, Participating I (2016) The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 11(1):39–51. https://doi.org/10.1016/j.jtho.2015.09.009 Hung JJ, Yeh YC, Jeng WJ, Wu YC, Chou TY, Hsu WH (2016) Factors predicting occult lymph node metastasis in completely resected lung adenocarcinoma of 3 cm or smaller. Eur J Cardiothorac Surg 50(2):329–336. https://doi.org/10.1093/ejcts/ezv485 Jin C, Cao J, Cai Y, Wang L, Liu K, Shen W, Hu J (2017) A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules. J Thorac Cardiovasc Surg 153(2):462–469e461. https://doi.org/10.1016/j.jtcvs.2016.10.019 Kagimoto A, Tsutani Y, Izaki Y, Handa Y, Mimae T, Miyata Y, and Okada M (2020) Prediction of Lymph Node Metastasis Using Semiquantitative Evaluation of PET for Lung Adenocarcinoma. Ann Thorac Surg 110(3):1036–1042. https://doi.org/10.1016/j.athoracsur.2020.03.032 Kameyama K, Imai K, Ishiyama K, Takashima S, Kuriyama S, Atari M, Ishii Y, Kobayashi A, Takahashi S, Kobayashi M, Harata Y, Sato Y, Motoyama S, Hashimoto M, Nomura K, Minamiya Y (2022) New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes. Thorac Cancer 13(5):708–715. https://doi.org/10.1111/1759-7714.14302 Kaseda K, Watanabe K, Asakura K, Kazama A, Ozawa Y (2016) Identification of false-negative and false-positive diagnoses of lymph node metastases in non-small cell lung cancer patients staged by integrated (18F-)fluorodeoxyglucose-positron emission tomography/computed tomography: A retrospective cohort study. Thorac Cancer 7(4):473–480. https://doi.org/10.1111/1759-7714.12358 Kawamoto N, Tsutani Y, Kamigaichi A, Ohsawa M, Mimae T, Miyata Y, Okada M (2023) Tumour location predicts occult N1 nodal metastasis in clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg 63(2). https://doi.org/10.1093/ejcts/ezac575 Lv X, Wu Z, Cao J, Hu Y, Liu K, Dai X, Yuan X, Wang Y, Zhao K, Lv W, Hu J (2021) A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics. Transl Lung Cancer Res 10(1):430–438. https://doi.org/10.21037/tlcr-20-1026 Mattes MD, Weber WA, Foster A, Moshchinsky AB, Ahsanuddin S, Zhang Z, Shi W, Rizk NP, Wu AJ, Ashamalla H, Rimner A (2015) A Predictive Model for Lymph Node Involvement with Malignancy on PET/CT in Non-Small-Cell Lung Cancer. J Thorac Oncol 10(8):1207–1212. https://doi.org/10.1097/JTO.0000000000000601 Miyasaka Y, Suzuki K, Takamochi K, Matsunaga T, Oh S (2013) The maximum standardized uptake value of fluorodeoxyglucose positron emission tomography of the primary tumour is a good predictor of pathological nodal involvement in clinical N0 non-small-cell lung cancer. Eur J Cardiothorac Surg 44(1):83–87. https://doi.org/10.1093/ejcts/ezs604 Moon Y, Kim KS, Lee KY, Sung SW, Kim YK, Park JK (2016) Clinicopathologic Factors Associated With Occult Lymph Node Metastasis in Patients With Clinically Diagnosed N0 Lung Adenocarcinoma. Ann Thorac Surg 101(5):1928–1935. https://doi.org/10.1016/j.athoracsur.2015.11.056 Nie P, Yang G, Wang N, Yan L, Miao W, Duan Y, Wang Y, Gong A, Zhao Y, Wu J, Zhang C, Wang M, Cui J, Yu M, Li D, Sun Y, Wang Y, Wang Z (2021) Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma. Eur J Nucl Med Mol Imaging 48(1):217–230. https://doi.org/10.1007/s00259-020-04747-5 Ran J, Cao R, Cai J, Yu T, Zhao D, Wang Z (2021) Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature. Front Oncol 11:585942. https://doi.org/10.3389/fonc.2021.585942 Vaghjiani RG, Takahashi Y, Eguchi T, Lu S, Kameda K, Tano Z, Dozier J, Tan KS, Jones DR, Travis WD, Adusumilli PS (2020) Tumor Spread Through Air Spaces Is a Predictor of Occult Lymph Node Metastasis in Clinical Stage IA Lung Adenocarcinoma. J Thorac Oncol 15(5):792–802. https://doi.org/10.1016/j.jtho.2020.01.008 Wang S, Yu Y, Xu W, Lv X, Zhang Y, Liu M (2021) Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study. BMC Cancer 21(1):653. https://doi.org/10.1186/s12885-021-08410-6 Wang Y, Zhou N, Zhu R, Li X, Sun Z, Gao Y, Liu W, Meng C, Ge Y, Bai C, Li T, Liu H (2021) Circulating activated immune cells as a potential blood biomarkers of non-small cell lung cancer occurrence and progression. BMC Pulm Med 21(1):282. https://doi.org/10.1186/s12890-021-01636-x Wei B, Jin X, Lu G, Zhao T, Xue H, Zhang Y (2023) A novel nomogram to predict lymph node metastasis in cT1 non-small-cell lung cancer based on PET/CT and peripheral blood cell parameters. BMC Pulm Med 23(1):44. https://doi.org/10.1186/s12890-023-02341-7 Zhao Y, Wang R, Shen X, Pan Y, Cheng C, Li Y, Shen L, Zhang Y, Li H, Zheng D, Ye T, Zheng S, Sun Y, Chen H (2016) Minor Components of Micropapillary and Solid Subtypes in Lung Adenocarcinoma are Predictors of Lymph Node Metastasis and Poor Prognosis. Ann Surg Oncol 23(6):2099–2105. https://doi.org/10.1245/s10434-015-5043-9 Zhao ZR, Situ DR, Lau RWH, Mok TSK, Chen GG, Underwood MJ, Ng CSH (2017) Comparison of Segmentectomy and Lobectomy in Stage IA Adenocarcinomas. J Thorac Oncol 12(5):890–896. https://doi.org/10.1016/j.jtho.2017.01.012 Zhong Y, Cai C, Chen T, Gui H, Deng J, Yang M, Yu B, Song Y, Wang T, Sun X, Shi J, Chen Y, Xie D, Chen C, She Y (2023) PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer. Nat Commun 14(1):7513. https://doi.org/10.1038/s41467-023-42811-4 Tables Table 1 Overview of FDG-PET/CT in diagnosing LNM (n = 132) Sensitivity % (n) Specificity % (n) Positive predictive value % (n) Negative predictive value % (n) Accuracy % (n) N1 LNM 34.6(9/26) 91.5(97/106) 50(9/18) 85.1(97/114) 80.3(106/132) N2 LNM 50(9/18) 99.1(113/114) 90(9/10) 92.6(113/122) 92.4(122/132) Overall LNM 45.2(14/31) 90.1(91/101) 58.3(14/24) 84.3(91/108) 79.5(105/132) Abbreviations: LNM, lymph nodal metastasis Table 2 Clinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis Variables LN(-) cohort (n=101) LN(+) cohort (n=31) P value Age, years (Median, range) 64(37-85) 66(34-77) 0.711 Gender, n (%) Female/Male 61(60.4)/40(39.6) 17(54.8)/14(45.2) 0.582 Smoking history, n (%) Never/Ever 83(82.2)/18(17.8) 29(93.5)/2(6.5) 0.208 PLR (Median, range) 109.3(8.7-318.8) 119.6(43.1-293.5) 0.673 NLR (Median, range) 1.8(0.1-9.5) 1.8(0.5-4.8) 0.741 LMR (Median, range) 4(0.5-33.7) 3.5(1.6-8.9) 0.492 Tumor size, mm (Median, range) 23(10-84) 29(13-61) 0.018* Tumor side, n (%) Left/Right 45(44.6)/56(55.4) 16(51.6)/15(48.4) 0.490 Tumor location, n (%) Central/Peripheral 14(13.9)/87(86.1) 11(35.5)/20(64.5) 0.007* Pure solid nodule, n (%) Yes/No 72(71.3)/29(28.7) 28(90.3)/3(9.7) 0.031* Tumor SUVmax value (Median, range) 2.9(0-17.8) 7.3(1.7-18.5) 0.000* N1 LN SUVmax value, n (%) ≥2.5/＜2.5 9(8.9)/92(91.1) 9(29)/22(71) 0.011* N2 LN SUVmax value, n (%) ≥2.5/＜2.5 1(1)/100(99) 9(29)/22(71) 0.000* Tumor grade, n (%) Well or Moderate/Poor 89(88.1)/12(11.9) 22(71)/9(29) 0.045* Pathological subtype Absent/Present, n (%) Lepidic pattern 83(82.2)/18(17.8) 28(90.3)/3(9.7) 0.422 Acinar pattern 9(8.9)/92(91.1) 7(22.6)/24(77.4) 0.084 Papillary pattern 27(26.7)/74(73.3) 9(29)/22(71) 0.801 Micropapillary pattern 45(44.6)/56(55.4) 12(38.7)/19(61.3) 0.566 Solid pattern 70(69.3)/31(30.7) 13(41.9)/18(58.1) 0.006* Mucinous pattern 98(97)/3(3) 30(96.8)/1(3.2) 1.000 Abbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. * p ＜0.05 Table 3 Univariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma Variables Univariable Multivariable OR 95%CI P value OR 95%CI P value Tumor size, mm 1.03 1.0-1.07 0.051 Tumor location Peripheral vs Central 3.42 1.35-8.64 0.009* 3.90 1.21-12.53 0.022* Pure solid nodule No vs Yes 3.76 1.06-13.33 0.04* 1.06 0.24-4.61 0.94 Tumor SUVmax value 1.26 1.12-1.41 0.000* 1.16 1.0-1.35 0.049* N1 LN SUVmax value ＜2.5 vs ≥2.5 4.182 1.49-11.77 0.007* 5.25 1.37-20.04 0.015* N2 LN SUVmax value ＜2.5 vs ≥2.5 40.91 4.93-339.77 0.001* 21.89 2.27-210.86 0.008* Tumor grade Well or Moderate vs Poor 3.03 1.14-8.10 0.027* 1.512 0.34-6.74 0.59 Solid pattern Absent vs Present 3.13 1.34-7.17 0.007* 2.29 0.71-7.37 0.16 Abbreviations: CI: confidence interval; OR: odds ratio. * p ＜0.05 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5267564","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367286993,"identity":"73c72967-eac4-4869-8480-f181bee2b4aa","order_by":0,"name":"Xinyu Zhu","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhu","suffix":""},{"id":367286994,"identity":"c2ea35b9-a8f1-4273-a438-296265c592b8","order_by":1,"name":"Xinyu Jia","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Jia","suffix":""},{"id":367286995,"identity":"0546f2f0-ebb8-4e86-b7da-50b77c8b235e","order_by":2,"name":"Shibing Teng","email":"","orcid":"","institution":"Department of Thoracic Surgery, Suzhou Xiangcheng People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shibing","middleName":"","lastName":"Teng","suffix":""},{"id":367286996,"identity":"edfa74d3-5c45-42c6-a8fe-51b2270d99da","order_by":3,"name":"Kai Fu","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Fu","suffix":""},{"id":367286997,"identity":"fcfb4761-a3e9-4c25-a53f-44ff039cc55d","order_by":4,"name":"Jiawei Chen","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Chen","suffix":""},{"id":367286998,"identity":"2ceb95ae-abc4-4b74-bc79-9cf453f93d07","order_by":5,"name":"Jun Zhao","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhao","suffix":""},{"id":367286999,"identity":"b6151bcb-219c-4cdb-8c2b-fd5a9dce4396","order_by":6,"name":"Chang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDACdsbGBwwMzCCmAZFamBmbDUjVwsAmQZoW/mbmtsqfbdbyDOzN2yQYau4Q1iJxmLHtNm9bumEDz7EyCYZjzwhrMWAGamFsO8zYIJFjJsHYcJg4LYU/2w7bN8i/IUELA2/b4cQGCR4itQD90izNcy49uY0nrdgi4RgRWvjb2x9+/FFmbdvPfnjjjQ81RGiBAzYQkUCChlEwCkbBKBgFeAAARNMxyD9GvjkAAAAASUVORK5CYII=","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University.","correspondingAuthor":true,"prefix":"","firstName":"Chang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-15 09:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5267564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5267564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67283976,"identity":"b8f3bf66-ec15-4c7e-98a8-8f2b46a1efb6","added_by":"auto","created_at":"2024-10-23 09:20:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86197,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the probability of lymph nodal metastasis in patients with lung adenocarcinoma. Each indicator is assigned a score based on a point scale. The total score is obtained by summing the individual scores. We can estimate the probability of lymph nodal metastasis by projecting the total score to the lower total point scale.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5267564/v1/2c428a444e84a7533b202743.png"},{"id":67283979,"identity":"e53a04ee-4f4d-4c80-838b-fdf04b4b38d5","added_by":"auto","created_at":"2024-10-23 09:20:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83057,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram. The x-axis shows the predicted probability, while the y-axis shows the actual probability of lymph nodal metastasis.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5267564/v1/e4200742a1a187415595666a.png"},{"id":67283978,"identity":"f17d671a-c9a5-43cc-bf2b-5806ec570f47","added_by":"auto","created_at":"2024-10-23 09:20:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61339,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for the nomogram. The x-axis shows the threshold probability. The y-axis shows the net benefit. The blue line represents the nomogram, the grey line assumes all patients have lymph nodal metastasis, and the black line assumes none do. The net benefit is calculated by subtracting the false positive rate from the true positive rate. DCA, decision curve analysis.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5267564/v1/c746ab8ce702d51e3b95b446.png"},{"id":67283980,"identity":"61ae63db-6864-4d62-a63d-265ff0e1c24f","added_by":"auto","created_at":"2024-10-23 09:20:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61123,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram in the prediction of lymph nodal metastasis in lung adenocarcinoma patients. ROC, receiver operating characteristic; AUC, area under curve; CI, confidence interval.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5267564/v1/43864d4afab032521c75a051.png"},{"id":67565341,"identity":"8cfc2658-9175-443a-9061-9dc19b7cf113","added_by":"auto","created_at":"2024-10-27 05:31:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5267564/v1/0ecd5be0-3fb4-4e64-87da-662394e11dae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer represents a predominant cause of cancer-specific mortality globally.(Nie et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang S. et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Non-small cell lung cancer (NSCLC) constitutes approximately 80% of lung cancer cases, with adenocarcinoma (AC) being the most prevalent histological subtype within NSCLC.(Arbour and Riely \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang S. et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Accurate lymph node staging is essential in the comprehensive management of lung cancer patients, influencing both surgical decision-making and the administration of adjuvant therapies.(Ran et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Consequently, the development of an effective and practical method for predicting lymph nodal metastasis in patients with AC is of critical clinical significance.\u003c/p\u003e \u003cp\u003ePathological biopsy, necessitating invasive techniques like endobronchial ultrasound-guided biopsies and mediastinoscopy, remains the gold-standard reference for determining lymph nodal status in the preoperative setting. However, the routine implementation of these procedures heightens the risk of overdiagnosis and appears to offer no additional benefits beyond confirming an N0 pathological state in patients without lymph nodal metastasis (LNM).(Zhong et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Furthermore, the feasibility and precision of safely conducting an invasive procedure are constrained by the potential for additional costs, trauma, and complications, particularly in patients with substantial comorbidities and diminutive lymph nodes. Computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) serve as crucial non-invasive modalities for tumor detection, diagnosis, staging, and clinical decision-making in individuals with lung cancer. CT is capable of diagnosing lymph nodes based on their size; however, it lacks sufficient accuracy for evaluating lymph nodes with small lesions. PET/CT provides a concurrent anatomical and metabolic characterization of lesions, yet its accuracy is not entirely reliable to eliminate the need for pathological confirmation of nodal malignancy.(Mattes et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) Moreover, infections and inflammatory processes can lead to false-positive results.\u003c/p\u003e \u003cp\u003eNomograms, which integrate multiple risk factors rather than depending on a single variable, have been demonstrated to be a reliable and effective method for aiding surgeons in the formulation of more precise diagnostic, therapeutic, and prognostic strategies.(Balachandran et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Several models have been developed to predict lymph nodal metastasis in lung cancer patients using PET/CT-related variables.(Lv et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mattes et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) However, predictive models for CT-negative patients with AC remain limited. Hence, this study sought to develop and validate a practical nomogram on basis of PET/CT to enhance noninvasive nodal evaluation in CT-negative patients with AC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective enrollment of 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection accompanied by systemic lymph node dissection or sampling at the Thoracic Surgery Department of The First Affiliated Hospital of Soochow University (Suzhou, China) from September 2020 to December 2023. The inclusion criteria for this study were as follows: (1) Patients had undergone PET/CT imaging during their hospitalization prior to surgical operation, without evidence of distant metastasis detected; (2) No enlarged lymph nodes were identified on CT scans, with lymph nodes measuring less than 1 cm in the shortest dimension; (3) Patients were pathologically diagnosed with primary adenocarcinoma, including both classic adenocarcinoma and mucinous adenocarcinoma; (4) Necessary clinicopathological data of the patient were complete. The exclusion criteria were as follows: (1) PET/CT scanning was performed in other institution; (2) Pleural metastasis was revealed during surgery; (3) CT scan indicated that the short axis of the lymph node exceeded 1 cm; (4) Systematic dissection or sampling of hilar and mediastinal lymph nodes was not performed; (5) Final pathological diagnosis was not adenocarcinoma.\u003c/p\u003e \u003cp\u003eBaseline clinicopathological data, encompassing age, gender, smoking history, peripheral blood cell parameters, pathological findings, and initial PET/CT data, were extracted from the HaiTai electronic medical record system (Nanjing, Jiangsu, China). Primary tumors situated within the proximal third of the hemithorax were classified as central-type tumors, whereas those located beyond the proximal third of the hemithorax were categorized as peripheral-type tumors.(Jin et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e This study was retrospectively conducted following the principles delineated in the Declaration of Helsinki (as revised in 2013) and received approval from the ethical committee and institutional review board of The First Affiliated Hospital of Soochow University (No. 2024349). Individual consent was waived for this retrospective analysis. All data were anonymized to ensure the protection of personal privacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT examination\u003c/h2\u003e \u003cp\u003eAll patients underwent integrated FDG PET/CT imaging prior to surgical operation. Patients fasted for a minimum of 6 hours to ensure their blood glucose levels were below 11 mmol/L before receiving an 18F-FDG injection, with a dosage ranging from 4.07 to 5.55 MBq/kg. Approximately 40 to 60 minutes post-injection, PET/CT scans were conducted from the base of the skull to the mid-thigh region, with an acquisition time of 2 to 3 minutes per bed position. The ordered subset expectation-maximization algorithm was used to conduct image reconstruction. The imaging was carried out using a Discovery PET/CT system (General Electric Medical Systems, Milwaukee, WI, USA) with low-dose CT parameters set at 140 kV, 120 mA, a transaxial field of view of 70 cm, and a slice thickness of 3.75 mm. The maximum standardized uptake value (SUVmax) of the tumor and suspected lymph nodes was ascertained by delineating a region of interest around each respective area. For part-solid tumors, tumor size was determined by measuring the solid component of the lesion. Final pathological findings were utilized as the gold standard to compare with PET/CT results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Blood parameters\u003c/h2\u003e \u003cp\u003eEach patient received a routine blood test during the week preceding surgery. The quantities of monocytes, neutrophils, platelets, and lymphocytes were measured. Subsequently, the ratios of neutrophils to lymphocytes (NLR), lymphocytes to monocytes (LMR), and platelets to lymphocytes (PLR) were individually calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Lymph nodal staging\u003c/h2\u003e \u003cp\u003eTumor staging was assessed following the 8th edition of the TNM classification for NSCLC.(Goldstraw et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) Lymph nodal metastasis was defined as pathologically confirmed pN1 or pN2 status, whereas those without such confirmation were categorized as pN0. Clinically, lymph nodes were deemed positive if they exceeded 1 cm in the shortest dimension on CT or exhibited an SUVmax greater than 2.5 PET/CT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Surgical procedures\u003c/h2\u003e \u003cp\u003eGenerally, lung cancer is typically excised via wedge resection, segmentectomy, lobectomy, or bilobectomy, accompanied by systemic lymph node dissection or sampling. Complete resection is characterized by the achievement of both macroscopic and microscopic tumor-free margins. At least 1 N1 (hilar, interlobar, and intrapulmonary) station and 3 N2 (mediastinal) stations including the subcarinal station were excised.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of PET/CT for the evaluation of lymph nodal metastasis were assessed using histological results as the reference standard. Continuous variables with non-normal distributions were reported as medians (range) and analyzed using the Mann\u0026ndash;Whitney U test. Categorical variables were expressed as numbers (rates) and analyzed using the χ\u0026sup2; test or Fisher's exact test. Univariable and multivariable binary logistic regression analyses were conducted to identify independent risk factors for lymph nodal metastasis, which were subsequently used to construct a nomogram. Receiver operating characteristic (ROC) curves were generated using MedCalc, and the area under the curves (AUC) was calculated to demonstrate the predictive accuracy of the nomogram. Internal validation of the nomogram was performed with 1000 bootstrap resamples. Harrell's C-index was employed to evaluate the discrimination performance. Calibration plots and decision curve analysis (DCA) were utilized to assess the calibration and clinical utility of the nomogram. Clinical baseline data analysis was conducted using the SPSS statistical package (version 26.0, IBM Corp., Armonk, NY, USA). The nomogram, calibration plot, and DCA curve were generated using R software version 4.4.1. Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFrom September 2020 to December 2023, a total of 132 lung adenocarcinoma patients consisting of 54 males and 78 females were enrolled in the study. All patients underwent integrated FDG PET/CT before pulmonary resection and were at least diagnosed with CT negative (the shortest dimension of lymph nodes less than 1 cm). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the diagnostic performance of PET-CT lymph nodal staging in lung adenocarcinoma patients. The sensitivity and specificity for N1 lymph nodes were 34.6% and 91.5%, respectively; for N2 lymph nodes, 50% and 99.1%, respectively; and for the overall patient cohort, 45.2% and 90.1%, respectively. The overall diagnostic accuracy was 79.5%. In all patients, the incidence of lymph nodal metastasis was 23.5% (31/132). Among these patients, 26 patients had N1 LNM and 18 patients had N2 LNM. Of all the patients, 6 underwent wedge resection, 3 underwent segmentectomy, 120 underwent lobectomy and 3 underwent bilobectomy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of FDG-PET/CT in diagnosing LNM (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e% (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e% (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive predictive\u003c/p\u003e \u003cp\u003evalue % (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative predictive\u003c/p\u003e \u003cp\u003evalue % (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e% (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1 LNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6(9/26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.5(97/106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(9/18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.1(97/114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.3(106/132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 LNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(9/18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.1(113/114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(9/10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.6(113/122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.4(122/132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall LNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.2(14/31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.1(91/101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.3(14/24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.3(91/108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.5(105/132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: LNM, lymph nodal metastasis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinicopathological features and nomogram construction\u003c/h2\u003e \u003cp\u003eAll patients\u0026rsquo; data including demographic, imaging, and pathological characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The distribution of these characteristics was compared between the lymph nodes negative (LN-) group and the lymph nodes positive (LN+) group. Compared to LN- patients, LN\u0026thinsp;+\u0026thinsp;patients exhibited a larger tumor size (29 vs 23; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and higher tumor SUVmax value (7.3 vs 2.9; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). More patients in the LN\u0026thinsp;+\u0026thinsp;group had a central-type tumor (35.5% vs 13.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), a pure solid nodule (90.3% vs 71.3%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), and were with N1 LN SUVmax value\u0026thinsp;\u0026ge;\u0026thinsp;2.5 (29% vs 8.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), N2 LN SUVmax value\u0026thinsp;\u0026ge;\u0026thinsp;2.5 (29% vs 1%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), poor tumor grade (29% vs 11.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), solid pattern (58.1% vs 30.7%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). However, no statistically significant differences were observed in peripheral blood cell parameters. Independent risk factors for LNM were identified through univariable and multivariable binary logistic regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Ultimately, tumor location (OR, 3.90; 95% CI, 1.21\u0026ndash;12.53; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), tumor SUVmax value (OR, 1.16; 95% CI, 1.0-1.35; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), N1 LN SUVmax value (OR, 5.25; 95% CI, 1.37\u0026ndash;20.04; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and N2 LN SUVmax value (OR, 21.89; 95% CI, 2.27-210.86; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) were identified for the construction of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN(-) cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLN(+) cohort (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(37\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(34\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(60.4)/40(39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(54.8)/14(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/Ever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(82.2)/18(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(93.5)/2(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR (Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.3(8.7-318.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.6(43.1-293.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8(0.1\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8(0.5\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR (Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.5\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5(1.6\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size, mm (Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(10\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(13\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor side, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft/Right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(44.6)/56(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(51.6)/15(48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral/Peripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(13.9)/87(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(35.5)/20(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure solid nodule, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(71.3)/29(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(90.3)/3(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor SUVmax value\u003c/p\u003e \u003cp\u003e(Median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9(0-17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3(1.7\u0026ndash;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1 LN SUVmax value, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.5/\u0026lt;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(8.9)/92(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(29)/22(71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 LN SUVmax value, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.5/\u0026lt;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1)/100(99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(29)/22(71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor grade, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell or Moderate/Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(88.1)/12(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(71)/9(29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent/Present, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLepidic pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(82.2)/18(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(90.3)/3(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinar pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(8.9)/92(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(22.6)/24(77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapillary pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(26.7)/74(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(29)/22(71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicropapillary pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(44.6)/56(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(38.7)/19(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(69.3)/31(30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(41.9)/18(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(97)/3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(96.8)/1(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. *\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vs Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u0026ndash;8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u0026ndash;12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure solid nodule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo vs Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u0026ndash;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor SUVmax value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1 LN SUVmax value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.5 vs\u0026thinsp;\u0026ge;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026ndash;11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37\u0026ndash;20.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 LN SUVmax value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.5 vs\u0026thinsp;\u0026ge;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93-339.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.27-210.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell or Moderate vs Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026ndash;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u0026ndash;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent vs Present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u0026ndash;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u0026ndash;7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: CI: confidence interval; OR: odds ratio. *\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Nomogram evaluation\u003c/h2\u003e \u003cp\u003eOur model demonstrated robust discrimination, evidenced by a C-index of 0.810 for LNM when evaluated using 1000 bootstrap resamples. Internal validation further corroborated this performance, yielding a high C-index value of 0.825. Calibration curves shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicated strong concordance between the predicted and actual probabilities of LNM. Decision curve analysis revealed that, for patients with a threshold probability exceeding 10%, the nomogram provided greater clinical benefit compared to both the treat-all and treat-none strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the area under the ROC curve was 0.825 (95% CI 0.749\u0026ndash;0.886, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), signifying a notable discrimination capability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eA novel nomogram was developed and validated in this retrospective study to predict the incidence of lymph node metastasis in patients with lung adenocarcinoma, utilizing readily accessible PET/CT indicators. The analysis identified primary tumor location, tumor SUVmax, N1 lymph node SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;2.5, and N2 lymph node SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;2.5 as independent risk factors. Our nomogram indicated that patients with central-type lung adenocarcinoma, elevated tumor SUVmax, and N1 and N2 lymph node SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;2.5 are at a higher risk of lymph nodal involvement. The model demonstrated robust discrimination and calibration. Hence, it might possess potential clinical utility to evaluate preoperative lymph nodal status in lung adenocarcinoma patients and might provide clinicians with valuable treatment guidance.\u003c/p\u003e \u003cp\u003eClinicians predominantly depend on specific clinical features, particularly imaging characteristics, to assess the risk of LNM in lung cancer during routine practice. Several studies have suggested that metabolic and morphologic parameters observed in PET/CT scans, such as tumor size, tumor location, consolidation ratio, and metabolic value, may offer valuable insights into the likelihood of LNM.(Kagimoto et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kameyama et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nie et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Nevertheless, this subjective evaluation is limited in its ability to comprehensively estimate the probability of LNM due to the variability in clinicians' experiences. This limitation has direct implications for the management strategies employed for individual patients. Therefore, various models utilizing PET/CT have been developed and validated to predict lymph nodal involvement in NSCLC.(Fu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lv et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mattes et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Whereas, to the best of our knowledge, there are limited studies specifically aimed at constructing prediction models for LNM in lung adenocarcinoma. Consistent with previous studies, factors such as tumor location, the SUVmax of the primary tumor, and suspicious lymph nodes have been identified as significant risk factors for lymph nodal metastasis.(Fu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kawamoto et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lv et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Notably, central lung cancer characterized by a high SUVmax value of both the primary tumor and suspicious lymph nodes exhibits a markedly higher prevalence of lymph nodal metastasis. Given that our model was designed for lung adenocarcinoma patients, which could be confirmed by preoperative biopsy or intraoperative frozen section, we incorporated the precise SUVmax of the primary tumor into our model to minimize measurement error. However, for the suspicious lymph nodes, 2.5 was used as the cut-off value associated with positive lymph nodes, which is more pragmatic and consistent with current clinical habits.(Kaseda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Miyasaka et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) We exclusively utilized the lymph node station with the highest SUVmax, despite the potential inaccuracy arising from the possibility that a single individual may present with multiple lymph nodes exhibiting abnormal SUVmax. This approach is justifiable for two reasons: firstly, our study aimed to evaluate overall lymph nodal involvement rather than the risk associated with specific lymph nodal stations; secondly, distinguishing between metastatic hilar and interlobar lymph nodes and those that are non-metastatic is challenging due to their proximity to the bronchus and similar soft tissue characteristics.(Dyas et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Thus, both N1 and N2 lymph node SUVmax were included in our model, with the latter demonstrating a more substantial contribution to the risk of LNM. Fundamentally, the identification of LNM and improvement of staging accuracy is determined depending on complete and en bloc resection of each lymph node station.(Darling et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Doddoli et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn consideration of the importance of inflammation in tumor initiation, progression, and metastasis, Wei, Chen, and Wang reported that peripheral blood cell parameters such as NLR and PLR were significantly elevated in LNM patients.(Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang Y. et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) However, no statistical difference was found in our cohort. This may be derived from the different biological characteristics of adenocarcinoma and other types of NSCLC. Additionally, micropapillary and/or solid subtypes of lung adenocarcinoma have been demonstrated to correlate with LNM and poor prognosis.(Hung et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) While the pathological subtype significantly influenced the risk of lymph node metastasis in univariable analysis, it was not identified as an independent predictor in our multivariable analysis. This may be because previous studies did not incorporate both PET/CT-related parameters and clinicopathological features simultaneously. Thus, these intriguing findings require to be validated by further studies with a larger sample size. Notably, some clinicopathologic factors such as vascular invasion, pleural invasion, and the existence of tumor spread through air spaces (STAS) were not incorporated into our prediction model, despite numerous studies reporting the potential increase of LNM in lung adenocarcinoma patients presenting with these characteristics.(Hung et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moon et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vaghjiani et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) For the moment, the determination of these pathological characteristics by preoperative biopsy or intraoperative FS is challenging for most pathologists due to limited access to the tissue and detection techniques, causing relatively low accuracy.\u003c/p\u003e \u003cp\u003eNodal biopsy is considered the gold standard for lymph node staging in the preoperative setting, but the potential risk of invasive procedures cannot be ignored. It is thus necessary to balance the advantages and disadvantages of this dual-nature procedure to individualize the lymph node staging for lung cancer patients.(Czarnecka-Kujawa and Yasufuku \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Detterbeck et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Besides, in surgical decision-making, sublobar resection and lobe-specific lymph node dissection are increasingly utilized for the surgical treatment of early-stage NSCLC due to more lung parenchyma preservation and less surgical trauma. However, lobectomy with systematic lymph node dissection is still the more appropriate choice if LNM occurs.(Dezube et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Therefore, it is advisable to recommend more aggressive diagnostic and therapeutic strategies for patients predicted by the model to have a high incidence of lymph node metastasis. Ultimately, our model may aid in identifying patients at high risk for lymph node involvement, thereby preventing missed opportunities for perioperative adjuvant therapy.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study warrant acknowledgment. Firstly, selection bias was unavoidable in this single-center retrospective study, raising questions about the generalizability of our findings to other populations. There still exists some unknown potential biases between the groups although multivariable analysis was conducted to balance the apparent biases. Secondly, all included cases were adenocarcinomas. Different histological types exhibit distinct radiological phenotypes and tumor aggressiveness, which contribute to heterogeneity in metastatic behavior. Consequently, patients were divided into two groups based solely on lymph node metastasis status, a constraint necessitated by the small sample size. Additionally, PET/CT imaging is not a mandatory preoperative procedure for every patient within our department. Consequently, our analysis was limited to the subset of patients who had undergone this imaging modality. Despite conducting internal validation to mitigate adverse influence and calibrate the model, external validation using data from other centers is necessary to ensure the generalizability of this nomogram.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Overview of FDG-PET/CT in diagnosing LNM (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThis work was supported by the grants from National Natural Science Foundation of China\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDesign: XY.Z., XY.J. and C.L.; Collection of statistics: SB.T.; Statistical analysis: JW.C. and K.F.; Manuscript writing: XY.Z., J.Z. and C.L. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArbour KC, Riely GJ (2019) Systemic Therapy for Locally Advanced and Metastatic Non-Small Cell Lung Cancer: A Review. JAMA 322(8):764\u0026ndash;774. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2019.11058\u003c/span\u003e\u003cspan address=\"10.1001/jama.2019.11058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):e173\u0026ndash;e180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1470-2045(14)71116-7\u003c/span\u003e\u003cspan address=\"10.1016/s1470-2045(14)71116-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JL, Wu JN, Lv XD, Yang QC, Chen JR, Zhang DM (2020) The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer. PLoS ONE 15(8):e0237947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0237947\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0237947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzarnecka-Kujawa K, Yasufuku K (2017) The role of endobronchial ultrasound versus mediastinoscopy for non-small cell lung cancer. J Thorac Dis 9(Suppl 2):S83\u0026ndash;S97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/jtd.2017.03.102\u003c/span\u003e\u003cspan address=\"10.21037/jtd.2017.03.102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarling GE, Allen MS, Decker PA, Ballman K, Malthaner RA, Inculet RI, Jones DR, McKenna RJ, Landreneau RJ, Rusch VW, Putnam JB Jr. (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(3):662\u0026ndash;670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtcvs.2010.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jtcvs.2010.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDetterbeck FC, Jantz MA, Wallace M, Vansteenkiste J, Silvestri GA, and American College of Chest P (2007). Invasive mediastinal staging of lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). \u003cem\u003eChest\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(3 Suppl), 202S-220S. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1378/chest.07-1362\u003c/span\u003e\u003cspan address=\"10.1378/chest.07-1362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDezube AR, Mazzola E, Deeb A, Wiener DC, Marshall MB, Rochefort MW, Jaklitsch MT (2022) Mandatory Nodal Evaluation During Resection of Clinical T1a Non-Small Cell Lung Cancers. Ann Thorac Surg 113(5):1583\u0026ndash;1590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.athoracsur.2021.06.078\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2021.06.078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoddoli C, Aragon A, Barlesi F, Chetaille B, Robitail S, Giudicelli R, Fuentes P, Thomas P (2005) Does the extent of lymph node dissection influence outcome in patients with stage I non-small-cell lung cancer? Eur J Cardiothorac Surg 27(4):680\u0026ndash;685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejcts.2004.12.035\u003c/span\u003e\u003cspan address=\"10.1016/j.ejcts.2004.12.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDyas AR, King RW, Ghanim AF, Cerfolio RJ (2018) Clinical Misstagings and Risk Factors of Occult Nodal Disease in Non-Small Cell Lung Cancer. Ann Thorac Surg 106(5):1492\u0026ndash;1498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.athoracsur.2018.05.045\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2018.05.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu Y, Xi X, Tang Y, Li X, Ye X, Hu B, Liu Y (2021) Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer. Thorac Cancer 12(15):2189\u0026ndash;2197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1759-7714.14066\u003c/span\u003e\u003cspan address=\"10.1111/1759-7714.14066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V, International Association for the Study of Lung Cancer, Prognostic Factors Committee S, Participating AB I, International Association for the Study of Lung Cancer S, Prognostic Factors Committee Advisory B, and, Participating I (2016) The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 11(1):39\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtho.2015.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2015.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung JJ, Yeh YC, Jeng WJ, Wu YC, Chou TY, Hsu WH (2016) Factors predicting occult lymph node metastasis in completely resected lung adenocarcinoma of 3 cm or smaller. Eur J Cardiothorac Surg 50(2):329\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ejcts/ezv485\u003c/span\u003e\u003cspan address=\"10.1093/ejcts/ezv485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin C, Cao J, Cai Y, Wang L, Liu K, Shen W, Hu J (2017) A nomogram for predicting the risk of invasive pulmonary adenocarcinoma for patients with solitary peripheral subsolid nodules. J Thorac Cardiovasc Surg 153(2):462\u0026ndash;469e461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtcvs.2016.10.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jtcvs.2016.10.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKagimoto A, Tsutani Y, Izaki Y, Handa Y, Mimae T, Miyata Y, and Okada M (2020) Prediction of Lymph Node Metastasis Using Semiquantitative Evaluation of PET for Lung Adenocarcinoma. Ann Thorac Surg 110(3):1036\u0026ndash;1042. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.athoracsur.2020.03.032\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2020.03.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKameyama K, Imai K, Ishiyama K, Takashima S, Kuriyama S, Atari M, Ishii Y, Kobayashi A, Takahashi S, Kobayashi M, Harata Y, Sato Y, Motoyama S, Hashimoto M, Nomura K, Minamiya Y (2022) New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes. Thorac Cancer 13(5):708\u0026ndash;715. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1759-7714.14302\u003c/span\u003e\u003cspan address=\"10.1111/1759-7714.14302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaseda K, Watanabe K, Asakura K, Kazama A, Ozawa Y (2016) Identification of false-negative and false-positive diagnoses of lymph node metastases in non-small cell lung cancer patients staged by integrated (18F-)fluorodeoxyglucose-positron emission tomography/computed tomography: A retrospective cohort study. Thorac Cancer 7(4):473\u0026ndash;480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1759-7714.12358\u003c/span\u003e\u003cspan address=\"10.1111/1759-7714.12358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawamoto N, Tsutani Y, Kamigaichi A, Ohsawa M, Mimae T, Miyata Y, Okada M (2023) Tumour location predicts occult N1 nodal metastasis in clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg 63(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ejcts/ezac575\u003c/span\u003e\u003cspan address=\"10.1093/ejcts/ezac575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv X, Wu Z, Cao J, Hu Y, Liu K, Dai X, Yuan X, Wang Y, Zhao K, Lv W, Hu J (2021) A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics. Transl Lung Cancer Res 10(1):430\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/tlcr-20-1026\u003c/span\u003e\u003cspan address=\"10.21037/tlcr-20-1026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattes MD, Weber WA, Foster A, Moshchinsky AB, Ahsanuddin S, Zhang Z, Shi W, Rizk NP, Wu AJ, Ashamalla H, Rimner A (2015) A Predictive Model for Lymph Node Involvement with Malignancy on PET/CT in Non-Small-Cell Lung Cancer. J Thorac Oncol 10(8):1207\u0026ndash;1212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JTO.0000000000000601\u003c/span\u003e\u003cspan address=\"10.1097/JTO.0000000000000601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyasaka Y, Suzuki K, Takamochi K, Matsunaga T, Oh S (2013) The maximum standardized uptake value of fluorodeoxyglucose positron emission tomography of the primary tumour is a good predictor of pathological nodal involvement in clinical N0 non-small-cell lung cancer. Eur J Cardiothorac Surg 44(1):83\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ejcts/ezs604\u003c/span\u003e\u003cspan address=\"10.1093/ejcts/ezs604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon Y, Kim KS, Lee KY, Sung SW, Kim YK, Park JK (2016) Clinicopathologic Factors Associated With Occult Lymph Node Metastasis in Patients With Clinically Diagnosed N0 Lung Adenocarcinoma. Ann Thorac Surg 101(5):1928\u0026ndash;1935. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.athoracsur.2015.11.056\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2015.11.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie P, Yang G, Wang N, Yan L, Miao W, Duan Y, Wang Y, Gong A, Zhao Y, Wu J, Zhang C, Wang M, Cui J, Yu M, Li D, Sun Y, Wang Y, Wang Z (2021) Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma. Eur J Nucl Med Mol Imaging 48(1):217\u0026ndash;230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00259-020-04747-5\u003c/span\u003e\u003cspan address=\"10.1007/s00259-020-04747-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRan J, Cao R, Cai J, Yu T, Zhao D, Wang Z (2021) Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature. Front Oncol 11:585942. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2021.585942\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.585942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaghjiani RG, Takahashi Y, Eguchi T, Lu S, Kameda K, Tano Z, Dozier J, Tan KS, Jones DR, Travis WD, Adusumilli PS (2020) Tumor Spread Through Air Spaces Is a Predictor of Occult Lymph Node Metastasis in Clinical Stage IA Lung Adenocarcinoma. J Thorac Oncol 15(5):792\u0026ndash;802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtho.2020.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2020.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Yu Y, Xu W, Lv X, Zhang Y, Liu M (2021) Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study. BMC Cancer 21(1):653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-021-08410-6\u003c/span\u003e\u003cspan address=\"10.1186/s12885-021-08410-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhou N, Zhu R, Li X, Sun Z, Gao Y, Liu W, Meng C, Ge Y, Bai C, Li T, Liu H (2021) Circulating activated immune cells as a potential blood biomarkers of non-small cell lung cancer occurrence and progression. BMC Pulm Med 21(1):282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12890-021-01636-x\u003c/span\u003e\u003cspan address=\"10.1186/s12890-021-01636-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei B, Jin X, Lu G, Zhao T, Xue H, Zhang Y (2023) A novel nomogram to predict lymph node metastasis in cT1 non-small-cell lung cancer based on PET/CT and peripheral blood cell parameters. BMC Pulm Med 23(1):44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12890-023-02341-7\u003c/span\u003e\u003cspan address=\"10.1186/s12890-023-02341-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Wang R, Shen X, Pan Y, Cheng C, Li Y, Shen L, Zhang Y, Li H, Zheng D, Ye T, Zheng S, Sun Y, Chen H (2016) Minor Components of Micropapillary and Solid Subtypes in Lung Adenocarcinoma are Predictors of Lymph Node Metastasis and Poor Prognosis. Ann Surg Oncol 23(6):2099\u0026ndash;2105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1245/s10434-015-5043-9\u003c/span\u003e\u003cspan address=\"10.1245/s10434-015-5043-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao ZR, Situ DR, Lau RWH, Mok TSK, Chen GG, Underwood MJ, Ng CSH (2017) Comparison of Segmentectomy and Lobectomy in Stage IA Adenocarcinomas. J Thorac Oncol 12(5):890\u0026ndash;896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtho.2017.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2017.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong Y, Cai C, Chen T, Gui H, Deng J, Yang M, Yu B, Song Y, Wang T, Sun X, Shi J, Chen Y, Xie D, Chen C, She Y (2023) PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer. Nat Commun 14(1):7513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-42811-4\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-42811-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Overview of FDG-PET/CT in diagnosing LNM (n = 132)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.704%;\"\u003e\n \u003cp\u003eSensitivity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.787%;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;% (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.426%;\"\u003e\n \u003cp\u003ePositive predictive\u003c/p\u003e\n \u003cp\u003evalue % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003eNegative predictive\u003c/p\u003e\n \u003cp\u003evalue % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003eAccuracy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.148%;\"\u003e\n \u003cp\u003eN1 LNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.704%;\"\u003e\n \u003cp\u003e34.6(9/26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.787%;\"\u003e\n \u003cp\u003e91.5(97/106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.426%;\"\u003e\n \u003cp\u003e50(9/18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e85.1(97/114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e80.3(106/132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.148%;\"\u003e\n \u003cp\u003eN2 LNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.704%;\"\u003e\n \u003cp\u003e50(9/18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.787%;\"\u003e\n \u003cp\u003e99.1(113/114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.426%;\"\u003e\n \u003cp\u003e90(9/10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e92.6(113/122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e92.4(122/132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.148%;\"\u003e\n \u003cp\u003eOverall LNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.704%;\"\u003e\n \u003cp\u003e45.2(14/31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.787%;\"\u003e\n \u003cp\u003e90.1(91/101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.426%;\"\u003e\n \u003cp\u003e58.3(14/24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e84.3(91/108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e79.5(105/132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LNM, lymph nodal metastasis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eClinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003eLN(-) cohort\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003eLN(+) cohort (n=31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eAge, years (Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e64(37-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e66(34-77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eFemale/Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e61(60.4)/40(39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e17(54.8)/14(45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eSmoking history, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eNever/Ever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e83(82.2)/18(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e29(93.5)/2(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003ePLR (Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e109.3(8.7-318.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e119.6(43.1-293.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eNLR (Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e1.8(0.1-9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e1.8(0.5-4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eLMR (Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e4(0.5-33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e3.5(1.6-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eTumor size, mm (Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e23(10-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e29(13-61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eTumor side, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eLeft/Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e45(44.6)/56(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e16(51.6)/15(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eTumor location, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eCentral/Peripheral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e14(13.9)/87(86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e11(35.5)/20(64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003ePure solid nodule, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eYes/No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e72(71.3)/29(28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e28(90.3)/3(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eTumor SUVmax value\u003c/p\u003e\n \u003cp\u003e(Median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e2.9(0-17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e7.3(1.7-18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eN1 LN SUVmax value, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003e\u0026ge;2.5/＜2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e9(8.9)/92(91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e9(29)/22(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eN2 LN SUVmax value, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003e\u0026ge;2.5/＜2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e1(1)/100(99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e9(29)/22(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eTumor grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eWell or Moderate/Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e89(88.1)/12(11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e22(71)/9(29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003ePathological subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eAbsent/Present, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eLepidic pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e83(82.2)/18(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e28(90.3)/3(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eAcinar pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e9(8.9)/92(91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e7(22.6)/24(77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003ePapillary pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e27(26.7)/74(73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e9(29)/22(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eMicropapillary pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e45(44.6)/56(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e12(38.7)/19(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eSolid pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e70(69.3)/31(30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e13(41.9)/18(58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.8047%;\"\u003e\n \u003cp\u003eMucinous pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.9331%;\"\u003e\n \u003cp\u003e98(97)/3(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2423%;\"\u003e\n \u003cp\u003e30(96.8)/1(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0199%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. \u0026nbsp;*\u003cem\u003ep\u003c/em\u003e ＜0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eUnivariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 31.657%;\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 28.8904%;\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eTumor size, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.0-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eTumor location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003ePeripheral vs Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.35-8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e1.21-12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.022*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003ePure solid nodule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eNo vs Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.06-13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e0.24-4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eTumor SUVmax value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.12-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e1.0-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eN1 LN SUVmax value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003e＜2.5 vs\u0026nbsp;\u0026ge;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e4.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.49-11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e1.37-20.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eN2 LN SUVmax value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003e＜2.5 vs\u0026nbsp;\u0026ge;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e40.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e4.93-339.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e21.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e2.27-210.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eTumor grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eWell or Moderate vs Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.14-8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e1.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e0.34-6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eSolid pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5458%;\"\u003e\n \u003cp\u003eAbsent vs Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.9143%;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1797%;\"\u003e\n \u003cp\u003e1.34-7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.5631%;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6851%;\"\u003e\n \u003cp\u003e0.71-7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2226%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI: confidence interval; OR: odds ratio. \u0026nbsp;*\u003cem\u003ep\u003c/em\u003e ＜0.05\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Positron emission tomography/computed tomography (PET/CT), lung adenocarcinoma, lymph nodal metastasis, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5267564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5267564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eA precise assessment of lymph nodal status is essential for guiding an individualized treatment plan in lung adenocarcinoma patients. A novel nomogram using easily accessible indicators was developed and validated in this study to predict CT-negative lymph nodal metastasis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBetween September 2020 and December 2023, data from 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection with systemic lymph node dissection or sampling were retrospectively reviewed. Risk factors associated with lymph nodal metastasis were identified using univariable and multivariable logistic regression analyses. Subsequently, a nomogram was developed on basis of these identified parameters. The performance and validity of the nomogram were evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and bootstrap resampling techniques.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour predictors (primary tumor location, primary tumor SUVmax value, N1 lymph node SUVmax, and N2 lymph node SUVmax) were identified and incorporated into the nomogram. The nomogram exhibited notable discrimination, evidenced by an area under the receiver operating characteristic (ROC) curve of 0.825 (95% CI: 0.749\u0026ndash;0.886, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Excellent concordance between the predicted and observed probabilities of lymph nodal involvement was demonstrated by the calibration curve. Furthermore, decision curve analysis indicated a net benefit associated with the use of our nomogram.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram demonstrated efficacy and practicality in predicting CT-negative lymph node metastasis for lung adenocarcinoma patients. It holds potential to offer valuable treatment guidance for clinicians.\u003c/p\u003e","manuscriptTitle":"A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 09:20:26","doi":"10.21203/rs.3.rs-5267564/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":"f9fb9094-0b73-4927-accf-7e48507181e2","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-30T04:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 09:20:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5267564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5267564","identity":"rs-5267564","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}