A Preoperative Nomogram for Predicting Occult Lymph Node Metastasis in Left Upper Lobe Adenocarcinoma

A Preoperative Nomogram for Predicting Occult Lymph Node Metastasis in Left Upper Lobe 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 Preoperative Nomogram for Predicting Occult Lymph Node Metastasis in Left Upper Lobe Adenocarcinoma Weijie Zhu, Defeng Luo, Kunsong Su, Yu Han, Qiduo Yu, Hongxiang Feng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7467805/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Non-small cell lung cancer (NSCLC), particularly lung adenocarcinoma, remains a leading cause of cancer-related mortality worldwide. Despite advancements in preoperative imaging techniques, occult lymph node metastases (OLNM) continue to pose a significant diagnostic challenge—especially in tumors located in the left upper lobe (LUL), owing to their complex anatomical structure and unique patterns of lymphatic spread. This study aims to develop a preoperative predictive model for OLNM in patients with clinical stage IA (cIA) LUL adenocarcinoma, with the goal of improving risk stratification and informing individualized treatment strategies. Methods : A retrospective cohort study was conducted involving 452 patients diagnosed with cIA LUL adenocarcinoma who underwent surgical resection between 2018 and 2022. Clinical, radiological, and pathological data were collected, including tumor location, tumor size, mean computed tomography (CT) attenuation value, and serum carcinoembryonic antigen (CEA) levels. Univariate and multivariate logistic regression analyses were used to identify independent predictors of OLNM. A predictive nomogram was subsequently developed and validated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). Results: OLNM was detected in 12.4% of patients. Multivariate analysis identified central tumor location (odds ratio [OR] = 10.38, p < 0.001), CT tumor size ≥ 2.05 cm (OR = 3.65, p < 0.001), mean CT attenuation value ≥ -19.80 Hounsfield units (HU) (OR = 1.01, p < 0.001), and elevated CEA levels ≥ 2.45 ng/ml (OR = 1.19, p = 0.001) as independent preoperative predictors of OLNM. The nomogram demonstrated excellent discriminative performance (area under the curve [AUC] = 0.935) and clinical utility, facilitating individualized risk assessment. Conclusion: This study proposes a lobe-specific predictive model for OLNM in patients with LUL adenocarcinoma, incorporating anatomical, radiological, and serological parameters. The resulting nomogram enhances preoperative risk stratification and supports the development of tailored surgical and adjuvant treatment strategies. These findings underscore the importance of lobe-specific considerations in NSCLC management and may contribute to improved clinical outcomes through more precise therapeutic decision-making. Non-small cell lung cancer left upper lobe occult lymph node metastasis predictive model nomogram preoperative staging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases[ 1 ]. Among NSCLC subtypes, lung adenocarcinoma is the most prevalent and is increasingly diagnosed at early stages due to the widespread adoption of Computed Tomography (CT) screening[ 2 , 3 ]. Surgical resection is the cornerstone of treatment for clinical stage IA disease, yet a significant clinical challenge persists: a subset of patients, presumed to be node-negative based on preoperative imaging, are found to harbor occult lymph node metastases (OLNM) upon postoperative pathologic examination[ 4 ]. This discrepancy between clinical and pathological staging has profound prognostic implications, as nodal involvement is a well-established determinant of survival and a critical factor in adjuvant therapy decision-making [ 5 ]. The left upper lobe (LUL) presents distinct surgical and anatomical challenges. As the largest lobe in the human lung, the LUL exhibits complex anatomy, including proximity to critical structures such as the pulmonary artery trunk and aortic arch[ 6 ]. Systematic lymph node dissection (SLND), essential for accurate pathologic staging[ 7 ], carries heightened risks during LUL procedures, particularly recurrent laryngeal nerve (RLN) palsy or injury[ 8 , 9 ]. This complication occurs most frequently during SLND for LUL tumors, likely due to the proximity of the left RLN—which loops around the aorta—to stations 5 and 6 lymph nodes[ 10 ]. Preoperative evaluation of tumor clinical staging is crucial for guiding surgical strategy and predicting prognosis[ 11 , 12 ]. Although imaging serves as a vital tool for preoperative staging, it has significant limitations; even highly sensitive and specific modalities such as Positron emission tomography/computed tomography (PET/CT) struggle to distinguish metastatic lymph nodes from inflammatory ones[ 13 ]. Furthermore, the unique anatomical features of the LUL may create imaging blind spots[ 14 ], potentially obscuring enlarged lymph nodes and resulting in preoperative staging underestimation. Emerging evidence suggests that lymphatic drainage patterns vary significantly across lung lobes. The presence of subpleural lymphatic pathways allows tumors to bypass traditional nodal sequences, resulting in skip lymph node metastases[ 15 ]. Notably, LUL tumors exhibit a higher frequency of skip metastases, particularly in station 5 lymph nodes[ 16 ]. Despite these findings, current staging protocols and surgical guidelines lack lobe-specific recommendations, potentially overlooking critical risk factors unique to the LUL. Most predictive models for OLNM are derived from studies pooling data across all pulmonary lobes, which may obscure lobe-specific risk factors. This oversight is particularly problematic for LUL tumors, given their anatomical and biological distinctiveness. To address this gap, the present study analyzes a homogeneous cohort of clinical stage IA (cIA) LUL adenocarcinomas to identify preoperative predictors of OLNM. By focusing exclusively on LUL tumors, we aim to develop a clinically applicable predictive model tailored to this subgroup, ultimately improving staging accuracy and informing personalized treatment strategies. MATERIAL AND METHODS Study Population This retrospective study enrolled patients with NSCLC located in the LUL who underwent surgical resection at our institution between January 2018 and May 2022. The inclusion criteria were as follows: (1) preoperative diagnosis of a tumor in the LUL; (2) tumor size ≤ 3 cm; (3) no evidence of lymph node metastasis on preoperative imaging and no prior lymph node biopsy; and (4) surgical resection with postoperative pathological confirmation of invasive lung adenocarcinoma. Exclusion criteria comprised: (1) patients who received neoadjuvant therapy; (2) patients deemed ineligible for surgery due to comorbidities (e.g., cardiovascular or cerebrovascular diseases) or poor general condition; (3) pathological diagnosis of non-invasive adenocarcinoma; and (4) multiple primary lung adenocarcinomas. All patients were staged according to the 9th edition of the TNM classification system by the American Joint Committee on Cancer. Histopathological classification followed the International Association for the Study of Lung Cancer / American Thoracic Society / European Respiratory Society multidisciplinary criteria. The patient selection process is illustrated in Fig. 1 . This study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Institutional Review Board of China-Japan Friendship Hospital (Approval No. ZRJY2023-QM34). Due to the retrospective nature of the research involving anonymized clinical data, the ethics committee waived the requirement for informed consent. Clinical information A total of 461 patients with pathologically confirmed invasive adenocarcinoma (postoperative diagnosis) were included in this study. Comprehensive clinical data were collected and analyzed, including: (1) Baseline characteristics: gender, age, body mass index (BMI), and preoperative comorbidities; (2) Surgical parameters: operative approach, intraoperative blood loss, and postoperative complications and so on; (3) Laboratory findings: serum tumor markers (carcinoembryonic antigen [CEA] and Cyfra21-1 levels); (4) Radiological features: Tumor size and location, Consolidation-to-tumor ratio (CTR), Mean CT attenuation value, Morphological characteristics (lobulation, spiculation and so on); (5) Pathological outcomes: final histopathological staging, lymph node metastasis status, pleural invasion, high-grade patterns, lymphovascular invasion (LVI), Spread through air spaces (STAS). Data quality control measures included removal of statistical outliers and imputation of missing values. CTR is defined as the ratio of the largest solid diameter to the largest tumor diameter, with a value of 0 representing a pure ground-glass opacity and a value of 1 indicating a pure solid nodule[ 17 ]. Preoperative Evaluation and Surgical Management Preoperative assessment of lymph node status and distant metastases was conducted by board-certified radiologists specializing in thoracic oncology. All surgical procedures were performed by fellowship-trained thoracic surgeons with extensive experience in oncologic resections. Intraoperatively, the tumor was completely resected and SLND was routinely performed. Pathological Examination All specimens were sent to the Department of Pathology for pathomorphological examination, and patients were subsequently stratified into two groups (nodal positive and nodal negative) based on final pathological findings Statistical Analysis Initial comparative analysis between node-positive and node-negative groups was performed for all clinical variables. Continuous variables were reported as median with interquartile range (IQR), while categorical variables were presented as frequencies with percentages. Univariate logistic regression was conducted with OLNM as the primary outcome to screen potential predictors, excluding surgical variables. The discriminative ability of each variable was evaluated using the area under the receiver operating characteristic curve (AUC). Variables demonstrating both statistical significance (P 0.65) were selected for subsequent multivariate analysis. A bidirectional stepwise multivariate logistic regression was employed to identify independent predictors of OLNM. Multicollinearity was assessed using variance inflation factors (VIF), with VIF ≤ 5 indicating acceptable absence of covariance among variables in the final model. Based on the multivariate analysis results, we constructed a predictive nomogram for OLNM. The model's performance was comprehensively evaluated through receiver operating characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) to assess the discriminative power, calibration, and clinical utility. Optimal cutoff values for continuous predictors were determined using the Youden index to maximize the sum of sensitivity and specificity, facilitating clinical application. All analyses were performed using R statistical software (version 4.4.3). RESULT Patient Characteristics A total of 452 patients with clinical stage IA LUL lung adenocarcinoma were included in the study. Among them, 56 patients (12.4%) were pathologically confirmed to have OLNM (pN + group), while the remaining 396 patients (87.6%) were node-negative (pN- group). The baseline characteristics of the two groups are summarized in Table 1 .Significant differences were observed between the pN + and pN- groups in several variables, including high-grade patterns (96.4% vs. 32.8%, p < 0.001), STAS (44.6% vs. 13.9%, p < 0.001), LVI (37.5% vs. 2.5%, p < 0.001), pleural invasion (57.1% vs. 16.7%, p < 0.001), CTR ≥ 50% (98.2% vs. 53.8%, p < 0.001), Location (42.9% vs. 8.3%, p < 0.001), and mean CT value (32.58 vs. -220.34, p < 0.001). Furthermore, the spiculated sign (78.6% vs 52.0%, p < 0.001) and lobulation sign (71.4% vs. 44.7%, p < 0.001) were significantly more prevalent in the pN + group. There were no statistically significant differences between the two groups in terms of age, gender, preoperative comorbidities, BMI, postoperative complications and so on. Table 1 Characteristics between two groups. Variables [ 1 ] Total (n = 452) pN-(n = 396) pN+(n = 56) p -value Gender 0.680 female 267 (59.1) 232 (58.6) 35 (62.5) male 185 (40.9) 164 (41.4) 21 (37.5) Diabetes Mellitus 64 (14.2) 56 (14.1) 8 (14.3) 1.000 COPD 6 (1.3) 4 (1.0) 2 (3.6) 0.345 Atherosclerosis 35 (7.7) 31 (7.8) 4 (7.1) 1.000 High-grade patterns 184 (40.7) 130 (32.8) 54 (96.4) < 0.001 STAS 80 (17.7) 55 (13.9) 25 (44.6) < 0.001 LVI 31 (6.9) 10 (2.5) 21 (37.5) < 0.001 Pleural invasion 98 (21.7) 66 (16.7) 32 (57.1) < 0.001 CTR < 0.001 < 50% 184 (40.7) 183 (46.2) 1 (1.8) ≥ 50% 268 (59.3) 213 (53.8) 55 (98.2) Location < 0.001 central 395 (87.4) 363 (91.7) 32 (57.1) peripheral 57 (12.6) 33 (8.3) 24 (42.9) Adjacent pleura 266 (58.8) 222 (56.1) 44 (78.6) 0.002 Spiculated sign 250 (55.3) 206 (52.0) 44 (78.6) < 0.001 Lobulation sign 217 (48.0) 177 (44.7) 40 (71.4) < 0.001 Irregular 408 (90.3) 364 (91.9) 44 (78.6) 0.004 Pleural Indentation Sign 228 (50.4) 190 (48.0) 38 (67.9) 0.008 Vacuole Sign 157 (34.7) 142 (35.9) 15 (26.8) 0.236 Vascular penetration 427 (94.5) 373 (94.2) 54 (96.4) 0.709 Age (year) 62.00 [55.00, 68.00] 62.00 [54.75, 68.00] 62.50 [56.50, 68.25] 0.580 BMI (kg/㎡) 24.22 [22.23, 26.54] 24.19 [22.31, 26.43] 24.86 [21.98, 26.93] 0.577 CEA (ng/ml) 2.24 [1.41, 3.42] 2.08 [1.35, 3.32] 3.28 [2.41, 7.05] < 0.001 Cyfra21-1 (ng/ml) 2.40 [1.81, 3.10] 2.40 [1.81, 3.06] 2.40 [1.85, 3.27] 0.508 CT size (cm) 1.60 [1.30, 2.00] 1.50 [1.20, 2.00] 2.10 [1.60, 2.50] < 0.001 Mean CT value (HU) -186.32 [-417.32, 20.13] -220.34 [-451.02, -28.28] 32.58 [21.31, 44.33] < 0.001 Duration of tube placement(day) 3.00 [3.00, 5.00] 3.00 [3.00, 5.00] 4.00 [3.00, 5.00] 0.400 Intraoperative bleeding(ml) 30.00 [20.00, 50.00] 30.00 [20.00, 50.00] 25.00 [20.00, 50.00] 0.852 Conversion to open 7 (1.5) 6 (1.5) 1 (1.8) 1.000 Complication none 419 (92.7) 364 (91.9) 55 (98.2) 0.347 arrhythmia 16 (3.5) 16 (4.0) 0 (0.0) pneumothorax 13 (2.9) 12 (3.0) 1 (1.8) hemorrhage 4 (0.9) 4 (1.0) 0 (0.0) Surgical Procedures wedge resection 61 (13.5) 59 (14.9) 2 (3.6) < 0.001 segmentectomy 142 (31.4) 135 (34.1) 7 (12.5) lobectomy 249 (55.1) 202 (51.0) 47 (83.9) [ 1 ]: Continuous data are expressed as median (interquartile range) and categorical data are expressed as numbers (percentages). COPD: Chronic Obstructive Pulmonary Disease; LVI: lymphovascular invasion; STAS: spread through air spaces; CTR: consolidation-tumor ratio; BMI: body mass index; CEA: carcinoembryonic antigen; CT: computed tomography. Univariate and Multivariate Analysis Univariate logistic regression analysis identified 14 variables significantly associated with OLNM (p 0.65 (Table 2 ). These included high-grade pattern (OR = 55.25, 95% CI: 13.26–230.11, AUC = 0.818), STAS (OR = 5.00, 95% CI: 2.75–9.10, AUC = 0.654), LVI (OR = 23.16, 95% CI: 10.11–53.05, AUC = 0.675), pleural invasion (OR = 6.67, 95% CI: 3.69–12.05, AUC = 0.702), CTR (OR = 47.25, 95% CI: 6.48–344.51, AUC = 0.722), Location (OR = 8.25, 95% CI: 4.36–15.62, AUC = 0.673), CEA (OR = 1.27, 95% CI: 1.17–1.39, AUC = 0.684), CT size (OR = 5.96, 95% CI: 3.29–10.81, AUC = 0.717), and mean CT value (OR = 1.01, 95% CI: 1.01–1.02, AUC = 0.868). Table 2 Univariate Analysis and Discriminative Performance of Candidate Variables. Variable OR (95%CI) P -value AUC 95%CI(AUC) Gender 0.85 (0.48 ~ 1.51) 0.577 0.4804 0.4120 ~ 0.5489 Diabetes Mellitus 1.01 (0.45 ~ 2.25) 0.977 0.5007 0.4514 ~ 0.5501 COPD 3.62 (0.65 ~ 20.29) 0.142 0.5128 0.4878 ~ 0.5378 Atherosclerosis 0.91 (0.31 ~ 2.67) 0.857 0.4966 0.4601 ~ 0.5331 High-grade patterns 55.25(13.26 ~ 230.11) < .001 0.8180 0.7843 ~ 0.8517 STAS 5.00(2.75 ~ 9.10) < .001 0.6538 0.5859 ~ 0.7216 LVI 23.16(10.11 ~ 53.05) < .001 0.6749 0.6104 ~ 0.7393 Pleural 6.67(3.69 ~ 12.05) < .001 0.7024 0.6345 ~ 0.7703 BMI 1.01 (0.93 ~ 1.10) 0.807 0.5230 0.4369 ~ 0.6091 CTR 47.25 (6.48 ~ 344.51) < .001 0.7221 0.6920 ~ 0.7523 Location 8.25 (4.36 ~ 15.62) < .001 0.6726 0.6058 ~ 0.7394 Adjacent pleura 2.87 (1.47 ~ 5.61) 0.002 0.6126 0.5531 ~ 0.6720 Spiculated sign 3.38 (1.73 ~ 6.69) < .001 0.6328 0.5732 ~ 0.6923 Lobulation sign 3.09 (1.68 ~ 5.71) < .001 0.6337 0.5691 ~ 0.6982 Irregular 0.32 (0.15 ~ 0.67) 0.002 0.4333 0.3774 ~ 0.4891 Pleural Indentation Sign 2.29 (1.26 ~ 4.15) 0.006 0.5994 0.5329 ~ 0.6658 Vacuole Sign 0.65 (0.35 ~ 1.22) 0.184 0.4546 0.3915 ~ 0.5178 Vascular penetration 1.66 (0.38 ~ 7.26) 0.497 0.5112 0.4841 ~ 0.5383 Age 1.01 (0.98 ~ 1.04) 0.484 0.5228 0.4448 ~ 0.6009 CEA 1.27 (1.17 ~ 1.39) < .001 0.6836 0.6008 ~ 0.7663 Cyfra21-1 1.17 (0.91 ~ 1.50) 0.220 0.4727 0.3894 ~ 0.5560 CT size 5.96 (3.29 ~ 10.81) < .001 0.7165 0.6373 ~ 0.7957 Mean CT value 1.01 (1.01 ~ 1.02) < .001 0.8680 0.8321 ~ 0.9040 Variables met criteria (p 0.65) were included in the multivariate model. OR: Odds Ratio; CI: Confidence Interval; COPD: Chronic Obstructive Pulmonary Disease; LVI: lymphovascular invasion; STAS: spread through air spaces; BMI: body mass index; CEA: carcinoembryonic antigen; CTR: consolidation-tumor ratio; CT: computed tomography. In the initial multivariate logistic regression model (Table 3 ), which included postoperative pathological variables, high-grade pattern (OR = 8.92, 95% CI: 1.88–42.37, p = 0.007), LVI (OR = 7.05, 95% CI: 2.35–21.12, p < 0.001), peripheral location (OR = 10.30, 95% CI: 3.67–28.91, p < 0.001), CT size (OR = 2.78, 95% CI: 1.29–6.02, p = 0.009), CEA (OR = 1.15, 95% CI: 1.02–1.30, p = 0.021), and mean CT value (OR = 1.01, 95% CI: 1.003–1.013, p < 0.001) were identified as independent predictors of OLNM. Table 3 Multivariate logistic regression analysis. Variables OR 95%CI P- value High-grade patterns 8.92 1.88 ~ 42.37 0.0069 CEA 1.15 1.02 ~ 1.30 0.0213 LVI 7.05 2.35 ~ 21.12 < .001 Location 10.30 3.67 ~ 28.91 < .001 CT size 2.78 1.29 ~ 6.02 0.0093 Mean CT value 1.01 1.003 ~ 1.013 < .001 OR: Odds Ratio; CI: Confidence Interval; CEA: carcinoembryonic antigen; LVI: lymphovascular invasion; CT: computed tomography. To develop a preoperative predictive model, we excluded postoperative pathological variables (high-grade patterns, LVI, and STAS) and refit the multivariate model using only preoperative variables. The revised model retained CEA (OR = 1.19, 95% CI: 1.07–1.33, p = 0.001), Location (OR = 10.38, 95% CI: 4.22–27.43, p < 0.001), CT size (OR = 3.65, 95% CI: 1.84–7.66, p < 0.001), and mean CT value (OR = 1.01, 95% CI: 1.008–1.018, p < 0.001) as significant predictors. Model Performance and Validation The original model (including pathological variables) demonstrated excellent discriminative ability with an AUC of 0.961 (95% CI: 0.943–0.980). The revised preoperative model also showed strong performance, with an AUC of 0.935 (95% CI: 0.907–0.963) (Fig. 2 ) . The nomogram constructed based on the preoperative model is presented in Fig. 3 . For each parameter, the corresponding point value is determined by drawing a vertical line from the measurement to the upper points scale. These individual point values are then summed to obtain a total score, which is subsequently projected downward to the predicted probability axis to yield the patient's personalized risk estimate ranging from 10–90%. The calibration curve indicated good agreement between predicted and observed probabilities of OLNM, with a mean absolute error of 0.0148 (Fig. 4 ). Decision curve analysis revealed that the nomogram provided a higher net benefit across a wide range of risk thresholds compared to the "treat-all" or "treat-none" strategies (Fig. 5 ). Optimal Cutoff Values Using the Youden index, the optimal cutoff values for continuous predictors were determined as follows: CEA ≥ 2.45 ng/mL (sensitivity: 75%, specificity: 58.6%), CT size ≥ 2.05 cm (sensitivity: 50%, specificity: 86.9%), and mean CT value ≥ -19.80 HU (sensitivity: 94.6%, specificity: 76.5%). DISCUSSION This study represents the first comprehensive effort to develop a preoperative predictive model specifically targeting OLNM in cIA LUL lung adenocarcinoma. Our findings not only validate previously recognized risk factors but also identify distinctive anatomical and biological characteristics of LUL tumors that contribute to their unique metastatic behavior. The strong discriminative performance of our nomogram (AUC = 0.935) underscores its potential utility in enhancing preoperative risk stratification. By providing a more accurate assessment of metastatic risk, this model can inform both surgical decision-making and the selection of adjuvant therapies in this anatomically complex and clinically significant subgroup. The unique anatomical complexity of the LUL necessitates a tailored approach to lymph node assessment. Unlike tumors in other pulmonary lobes, LUL tumors exhibit distinct metastatic patterns, particularly a tendency for skip metastases to station 5 lymph nodes[ 16 ]. This phenomenon may account for the higher-than-expected rate of occult metastases observed in our study (12.4%). This biological behavior, coupled with the technical challenges arising from the vulnerable course of the left recurrent laryngeal nerve near the aortopulmonary window, presents a critical clinical dilemma: conventional imaging modalities frequently fail to detect nodal involvement, while aggressive dissection poses a substantial risk of RLN injury[ 8 , 9 ]. Our findings demonstrate that central tumor location and radiological solidity are not merely anatomical descriptors but significant predictors of occult metastasis, potentially reflecting an invasive tumor phenotype with a propensity for preferential lymphatic spread. The nomogram assists surgeons in balancing oncologic thoroughness with the imperative of functional preservation by quantifying the risk-benefit ratio of lymphadenectomy. Our nomogram addresses two pivotal clinical challenges in the management of LUL adenocarcinoma. First, it provides a data-driven framework for optimizing the extent of lymphadenectomy by balancing the need for accurate pathological staging with the risk of RLN injury. For high-risk patients—particularly those with centrally located tumors exhibiting solid radiographic characteristics and elevated CEA levels—our model supports comprehensive lymph node dissection, despite the associated technical challenges and increased risk of RLN injury. This recommendation is especially relevant for lymph node stations 5 and 6 (aortopulmonary window), where the anatomical proximity of the left RLN has traditionally led to more conservative surgical approaches[ 10 ]. The course of the left RLN, which loops around the aortic arch from anterior to posterior, along with its well-documented anatomical variability[ 18 , 19 ], further complicates surgical intervention in this region. Given that RLN injury is a common complication that can substantially impair quality of life[ 20 ], our model offers objective, risk-based criteria to justify aggressive lymphadenectomy only when it is oncologically justified. Conversely, in low-risk patients presenting with peripheral, ground-glass opacity–predominant tumors, the nomogram supports a strategy of limited nodal sampling. This approach minimizes surgical morbidity without compromising oncologic efficacy. It enables precise identification of patients who are most likely to benefit from extensive nodal evaluation, while sparing low-risk individuals from the potential complications associated with unnecessary radical lymphadenectomy. Second—and perhaps more importantly—our nomogram facilitates the identification of a clinically significant subset of stage IA patients who, despite being classified as "low risk" under conventional TNM staging criteria, may in fact benefit from adjuvant therapy. This insight is particularly relevant given that current clinical guidelines generally do not recommend adjuvant treatment for patients with stage IA disease[ 21 ]. Our data indicate that approximately 12.4% of cIA LUL adenocarcinomas harbor occult nodal metastases. These patients likely constitute a high-risk group that could derive substantial benefit from early systemic therapy. The capacity to preoperatively identify such individuals using routinely accessible clinical and radiological parameters holds the potential to reshape existing treatment paradigms. It enables the initiation of adjuvant therapies at a stage when micrometastatic disease is not yet clinically evident, thereby improving the likelihood of therapeutic success. This approach is particularly timely in the current era of increasingly effective targeted therapies and immunotherapies, where early intervention—especially in molecularly selected populations—has been associated with improved clinical outcomes. The association between tumor center location and lymph node metastasis was first reported by Ketchedjian et al.[ 22 ]. While varying definitions of “central” tumor location exist, our study adopts the criteria established by the American College of Chest Physicians[ 23 ], which defines centrally located tumors as those situated within the inner third of the hemithorax. It has been proposed that the primary tumor site may influence the metastatic pattern of NSCLC[ 24 ], with central lung adenocarcinomas demonstrating a higher propensity for regional lymph node metastasis compared to peripheral tumors—a trend that is associated with poorer prognosis[ 25 ] and aligns with our findings. Furthermore, tumor location appears to correlate with specific genetic alterations in lung cancer. For instance, an increased incidence of ALK rearrangements has been reported in centrally located tumors, which are also characterized by a higher frequency of lymph node involvement[ 26 , 27 ]. These observations underscore the critical importance of early and accurate assessment of lymph node metastasis in patients with lung cancer, particularly those presenting with central tumor location. The radiological features identified in our study—mean CT attenuation (OR = 1.01) and tumor size (OR = 3.65)—are reflective of the underlying biological aggressiveness of LUL adenocarcinomas. Imaging modalities play a pivotal role in the diagnosis and management of lung cancer, offering non-invasive yet informative assessments that complement histopathological analysis. In particular, CT excels at characterizing suspicious pulmonary lesions by providing critical prognostic indicators such as nodule size, anatomical location, and morphological attributes, all of which significantly inform therapeutic decision-making[ 28 ].Recent studies have reinforced the prognostic relevance of these radiological characteristics, demonstrating that increased lesion size and higher tissue density are consistently associated with more aggressive tumor behavior[ 29 ]. Our findings further support this evidence, showing that tumors with elevated radiodensity—indicative of a predominant solid component—and larger dimensions are more strongly associated with adverse pathological features suggestive of malignancy. These associations highlight the value of preoperative imaging not only for diagnostic purposes but also for risk stratification and treatment planning. Elevated preoperative CEA levels (OR = 1.19) provide an additional layer of risk stratification, suggesting the involvement of systemic tumor–host interactions in the development of OLNM. CEA is a well-established biomarker associated with tumor burden and biological aggressiveness, and its elevation may indicate the presence of micrometastatic disease or an immunosuppressive tumor microenvironment that facilitates lymphatic dissemination[ 30 , 31 ]. When combined with radiological parameters, CEA contributes to the construction of a multidimensional risk profile that transcends the limitations of traditional anatomical staging. This integrative approach offers a more comprehensive and clinically relevant assessment of metastatic potential, thereby enhancing the precision of treatment planning. While this study offers valuable insights, several limitations should be acknowledged. First, its retrospective design inherently restricts the ability to establish causal relationships, and despite the application of rigorous inclusion criteria, the potential for selection bias remains. Second, although imaging was consistently utilized for staging, its limited spatial resolution—particularly in assessing the aortopulmonary window, a region of critical importance for left upper lobe tumors—may have led to an underestimation of central tumor prevalence and nodal involvement. Third, the single-institution nature of the study, while beneficial in maintaining consistency in surgical and pathological protocols, may limit the generalizability of our findings to broader clinical settings with varying patient demographics and treatment practices. Fourth, the absence of comprehensive molecular characterization restricts deeper biological interpretation, particularly with regard to tumor heterogeneity and the potential influence of genetic alterations on metastatic behavior. Although the study period (2018–2022) reflects contemporary clinical practice, ongoing advancements in imaging technologies and molecular diagnostics may soon surpass some of the predictive parameters employed in our model. Finally, while our analysis focused on anatomical and radiological predictors, emerging biomarkers—such as circulating tumor DNA or specific driver mutations—were not incorporated, representing an area for future research and potential model refinement. Despite these limitations, our study establishes a foundation for lobe-specific risk stratification in LUL adenocarcinoma. The clinical relevance of our findings is reinforced by the reliance on routinely available preoperative parameters, enhancing the feasibility of implementation across diverse clinical settings. As the field of personalized medicine continues to advance, the integration of anatomically and biologically informed models such as ours is poised to play an increasingly pivotal role in guiding individualized treatment strategies for patients with lung cancer. CONCLUSION In summary, this study presents a lobe-specific predictive model for OLNM in LUL adenocarcinoma, highlighting the combined influence of anatomical, radiological, and biological factors. By moving beyond the traditional “one-size-fits-all” approach to staging, our findings support the development of personalized surgical and adjuvant strategies tailored to the distinct risk profile of LUL tumors. This model not only improves preoperative risk stratification but also reinforces the need for lobe-specific guidelines in lung cancer management, with the potential to enhance clinical outcomes in this anatomically and biologically unique subgroup. Abbreviations NSCLC = Non-Small Cell Lung Cancer LUL = The left upper lobe OLNM = occult lymph node metastases cIA = Clinical Stage IA CT = Computed Tomography CEA = Carcinoembryonic Antigen PET/CT = Positron Emission Tomography/Computed Tomography SLND = Systematic lymph node dissection RLN = recurrent laryngeal nerve COPD = Chronic Obstructive Pulmonary Disease LVI = Lymphovascular Invasion STAS = Spread Through Air Spaces CTR = Consolidation-Tumor Ratio BMI = Body Mass Index CI = Confidence Intervals OR = Odds Ratio IQR = Interquartile Range Declarations Ethics declarations Ethics approval and consent to participate All procedures adhered to the principles outlined in the Declaration of Helsinki. The study protocol and related documents were approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (file number: 2024-KY-015), and the need for informed consent was waived. Consent for publication Not Applicable Competing interests The authors declare no competing interests. Clinical trial number: MR-11-24-018343 Funding Statement : This work was supported by the Elite Medical Professionals Project of China-Japan Friendship Hospital (ZRJY2023-QM34), the National High Level Hospital Clinical Research Funding, and the Special Fund (2060204) of the State Key Laboratory of Respiratory Health and Multimorbidity (2024-QZZZ-05). Data Availability Statement : The data underlying this article will be shared on reasonable request to the corresponding author. Author information Authors and Affiliations Department of General Thoracic Surgery, China-Japan Friendship Hospital, No. 2 Yinghua East Road, Chaoyang District, Beijing, 100029, China. China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. Weijie Zhu, Defeng Luo, Kunsong Su, Yu Han, Qiduo Yu, Hongxiang Feng, Chaoyang Liang Contributions W.Z. and C.L conceived and designed the study. D.L. was involved in data acquisition and wrote the manuscript. K.S., Y.H., Q.Y., H.F. and C.L. analyzed the data. W.Z. and C.L revised the manuscript. All authors read and approved the final manuscript. Corresponding author Correspondence to Chaoyang Liang . Acknowledgements None. References Nasim F, Sabath BF, Eapen GA. Lung Cancer . Med Clin North Am 2019; 103 :463-73. Hutchinson BD, Shroff GS, Truong MT, Ko JP. Spectrum of Lung Adenocarcinoma . Semin Ultrasound CT MR 2019; 40 :255-64. Suzuki K. Whack-a-mole strategy for multifocal ground glass opacities of the lung . J Thorac Dis 2017; 9 :S201-s07. Li JX, Feng GY, He KL, Li GS, Gao X, Yan GQ et al. Preoperative prediction of occult lymph node metastasis in patients with non-small cell lung cancer: a simple and widely applicable model . BMC Pulm Med 2024; 24 :557. 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Hoarseness after radical surgery with systematic lymph node dissection for primary lung cancer . Eur J Cardiothorac Surg 2019; 55 :280-85. Taylor O, Boardman G, Bentel J, Laycock A. Discordance between clinical and pathologic staging and the timeliness of care of non-small cell lung cancer patients diagnosed with operable tumors . Asia Pac J Clin Oncol 2023; 19 :706-14. Tsai TM, Liu CY, Lin MW, Hsu HH, Chen JS. Factors Associated with Nodal Upstaging in Clinical T1a-bN0M0 Non-Small Cell Lung Cancers . Cancers (Basel) 2022; 14 . Kang YK, Na KJ, Park J, Kwak N, Lee YS, Choi H et al. Preoperative evaluation of mediastinal lymph nodes in non-small cell lung cancer using [(68)Ga]FAPI-46 PET/CT: a prospective pilot study . Eur J Nucl Med Mol Imaging 2024; 51 :2409-19. Maki R, Miyajima M, Ogura K, Tada M, Takahashi Y, Adachi H et al. Pulmonary vessels and bronchus anatomy of the left upper lobe . Surg Today 2022; 52 :550-58. Hata E, Miyamoto H, Sakao Y. [Investigation into mediastinal lymph node metastasis of lung cancer and rationale for decision of the extent of mediastinal dissection] . Nihon Geka Gakkai Zasshi 1997; 98 :8-15. Ichinose J, Suzuki A, Matsuura Y, Nakao M, Okumura S, Ninomiya H et al. Impact of tumor location and pleural invasion on the frequency of skip hilar lymph node metastasis in lung cancer . J Thorac Dis 2024; 16 :5958-68. He S, Wu Y, Ai P. Consolidation-to-tumor ratio is not a prognostic factor for lung cancer manifesting as radiological part-solid nodules . Asian J Surg 2024; 47 :1063-64. Galetta D, Cesario A, Margaritora S, Granone P. Anomalous intrathoracic left vagus and recurrent laryngeal nerve course . Ann Thorac Surg 2008; 86 :654-5. Henry JF, Audiffret J, Denizot A, Plan M. The nonrecurrent inferior laryngeal nerve: review of 33 cases, including two on the left side . Surgery 1988; 104 :977-84. Yuan SM. Hoarseness subsequent to cardiovascular surgery, intervention, maneuver and endotracheal intubation: the so-called iatrogenic Ortner's (cardiovocal) syndrome . Cardiol J 2012; 19 :560-6. Riely GJ, Wood DE, Ettinger DS, Aisner DL, Akerley W, Bauman JR et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology . J Natl Compr Canc Netw 2024; 22 :249-74. Ketchedjian A, Daly BD, Fernando HC, Florin L, Hunter CJ, Morelli DM et al. Location as an important predictor of lymph node involvement for pulmonary adenocarcinoma . J Thorac Cardiovasc Surg 2006; 132 :544-8. Silvestri GA, Gonzalez AV, Jantz MA, Margolis ML, Gould MK, Tanoue LT et al. Methods for staging non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines . Chest 2013; 143 :e211S-e50S. Shan Q, Li Z, Lin J, Guo J, Han X, Song X et al. Tumor Primary Location May Affect Metastasis Pattern for Patients with Stage IV NSCLC: A Population-Based Study . J Oncol 2020; 2020 :4784701. Liang RB, Yang J, Zeng TS, Long H, Fu JH, Zhang LJ et al. Incidence and Distribution of Lobe-Specific Mediastinal Lymph Node Metastasis in Non-small Cell Lung Cancer: Data from 4511 Resected Cases . Ann Surg Oncol 2018; 25 :3300-07. Kim TH, Woo S, Yoon SH, Halpenny DF, Han S, Suh CH. CT Characteristics of Non-Small Cell Lung Cancer With Anaplastic Lymphoma Kinase Rearrangement: A Systematic Review and Meta-Analysis . AJR Am J Roentgenol 2019; 213 :1059-72. Xie X, Li X, Tang W, Xie P, Tan X. Primary tumor location in lung cancer: the evaluation and administration . Chin Med J (Engl) 2021; 135 :127-36. Meng Y, Liu CL, Cai Q, Shen YY, Chen SQ. Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma . Radiol Med 2019; 124 :8-13. Hashimoto H, Matsumoto J, Murakami M, Hiyama N, Yamaguchi H, Kusakabe M et al. Progressively increasing density of the solid center of a ground-glass nodule in a solitary pulmonary capillary hemangioma: A case report . Pathol Int 2020; 70 :568-73. Zhang ZH, Han YW, Liang H, Wang LM. Prognostic value of serum CYFRA21-1 and CEA for non-small-cell lung cancer . Cancer Med 2015; 4 :1633-8. Moro D, Villemain D, Vuillez JP, Delord CA, Brambilla C. CEA, CYFRA21-1 and SCC in non-small cell lung cancer . Lung Cancer 1995; 13 :169-76. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Oct, 2025 Editor invited by journal 10 Sep, 2025 Editor assigned by journal 08 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 27 Aug, 2025 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-7467805","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533614184,"identity":"48325208-03a5-4290-a54f-78b97ff42f33","order_by":0,"name":"Weijie Zhu","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Zhu","suffix":""},{"id":533614185,"identity":"35c12f6d-2206-45fd-9d6a-449ac122702a","order_by":1,"name":"Defeng Luo","email":"","orcid":"","institution":"China-Japan Friendship 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19:30:23","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125198,"visible":true,"origin":"","legend":"","description":"","filename":"5f2f2bb6696d414f9e03265f02f986411structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/d7f753f11d052061c6b8e73c.xml"},{"id":94226021,"identity":"21e40d1b-e041-4fb3-9af6-e364ccc10276","added_by":"auto","created_at":"2025-10-23 19:38:23","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135471,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/6fc7bc094079b2255a02f6b5.html"},{"id":94226011,"identity":"20ae9fcc-861b-4e86-8bcb-ce1b84bc455d","added_by":"auto","created_at":"2025-10-23 19:38:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45216,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the patient’s selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/d4a124c4a192263043505457.png"},{"id":94225481,"identity":"9028f18a-0c41-416f-acc9-382cd50141a0","added_by":"auto","created_at":"2025-10-23 19:30:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53509,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the risk of occult lymph node metastases. CT: Computed Tomography; CEA: Carcinoembryonic Antigen.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/f35395dedad4f8b32b80f386.png"},{"id":94225479,"identity":"32599bba-60c9-42ee-9af3-f415c0637f09","added_by":"auto","created_at":"2025-10-23 19:30:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33957,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and AUC of the predictive model. ROC: receiver operating characteristic; AUC: area under the curve.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/033260bebfff366219351170.png"},{"id":94225483,"identity":"9fa99061-a98d-46f4-be0a-c3605c67a77d","added_by":"auto","created_at":"2025-10-23 19:30:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62432,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the predictive model. \u0026nbsp;The figure shows that the prediction model has a good predictive ability.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/ac903987c3e6b9a0c92d27b0.png"},{"id":94226014,"identity":"152dd3db-7a64-4044-81a9-2818ff914775","added_by":"auto","created_at":"2025-10-23 19:38:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63348,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the nomogram. DCA: decision curve analysis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/dc363963469ff7d7f4b5958c.png"},{"id":94226754,"identity":"574bb1b7-5cbe-4fdf-97a5-b910dac65b46","added_by":"auto","created_at":"2025-10-23 19:54:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2191416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7467805/v1/f1c78d74-5b0a-4d75-b7c5-d76a2affeb91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Preoperative Nomogram for Predicting Occult Lymph Node Metastasis in Left Upper Lobe Adenocarcinoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among NSCLC subtypes, lung adenocarcinoma is the most prevalent and is increasingly diagnosed at early stages due to the widespread adoption of Computed Tomography (CT) screening[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Surgical resection is the cornerstone of treatment for clinical stage IA disease, yet a significant clinical challenge persists: a subset of patients, presumed to be node-negative based on preoperative imaging, are found to harbor occult lymph node metastases (OLNM) upon postoperative pathologic examination[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cb\u003eThis discrepancy between clinical and pathological staging has profound prognostic implications, as nodal involvement is a well-established determinant of survival and a critical factor in adjuvant therapy decision-making\u003c/b\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe left upper lobe (LUL) presents distinct surgical and anatomical challenges. As the largest lobe in the human lung, the LUL exhibits complex anatomy, including proximity to critical structures such as the pulmonary artery trunk and aortic arch[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Systematic lymph node dissection (SLND), essential for accurate pathologic staging[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], carries heightened risks during LUL procedures, particularly recurrent laryngeal nerve (RLN) palsy or injury[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This complication occurs most frequently during SLND for LUL tumors, likely due to the proximity of the left RLN\u0026mdash;which loops around the aorta\u0026mdash;to stations 5 and 6 lymph nodes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePreoperative evaluation of tumor clinical staging is crucial for guiding surgical strategy and predicting prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although imaging serves as a vital tool for preoperative staging, it has significant limitations; even highly sensitive and specific modalities such as Positron emission tomography/computed tomography (PET/CT) struggle to distinguish metastatic lymph nodes from inflammatory ones[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, the unique anatomical features of the LUL may create imaging blind spots[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], potentially obscuring enlarged lymph nodes and resulting in preoperative staging underestimation.\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that lymphatic drainage patterns vary significantly across lung lobes. The presence of subpleural lymphatic pathways allows tumors to bypass traditional nodal sequences, resulting in skip lymph node metastases[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, LUL tumors exhibit a higher frequency of skip metastases, particularly in station 5 lymph nodes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cb\u003e Despite these findings, current staging protocols and surgical guidelines lack lobe-specific recommendations, potentially overlooking critical risk factors unique to the LUL.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMost predictive models for OLNM are derived from studies pooling data across all pulmonary lobes, which may obscure lobe-specific risk factors. This oversight is particularly problematic for LUL tumors, given their anatomical and biological distinctiveness. To address this gap, the present study analyzes a homogeneous cohort of clinical stage IA (cIA) LUL adenocarcinomas to identify preoperative predictors of OLNM. By focusing exclusively on LUL tumors, we aim to develop a clinically applicable predictive model tailored to this subgroup, ultimately improving staging accuracy and informing personalized treatment strategies.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis retrospective study enrolled patients with NSCLC located in the LUL who underwent surgical resection at our institution between January 2018 and May 2022. The inclusion criteria were as follows: (1) preoperative diagnosis of a tumor in the LUL; (2) tumor size\u0026thinsp;\u0026le;\u0026thinsp;3 cm; (3) no evidence of lymph node metastasis on preoperative imaging and no prior lymph node biopsy; and (4) surgical resection with postoperative pathological confirmation of invasive lung adenocarcinoma. Exclusion criteria comprised: (1) patients who received neoadjuvant therapy; (2) patients deemed ineligible for surgery due to comorbidities (e.g., cardiovascular or cerebrovascular diseases) or poor general condition; (3) pathological diagnosis of non-invasive adenocarcinoma; and (4) multiple primary lung adenocarcinomas.\u003c/p\u003e\u003cp\u003e All patients were staged according to the 9th edition of the TNM classification system by the American Joint Committee on Cancer. Histopathological classification followed the International Association for the Study of Lung Cancer / American Thoracic Society / European Respiratory Society multidisciplinary criteria. The patient selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e This study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Institutional Review Board of China-Japan Friendship Hospital (Approval No. ZRJY2023-QM34). Due to the retrospective nature of the research involving anonymized clinical data, the ethics committee waived the requirement for informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical information\u003c/h3\u003e\n\u003cp\u003eA total of 461 patients with pathologically confirmed invasive adenocarcinoma (postoperative diagnosis) were included in this study. Comprehensive clinical data were collected and analyzed, including: (1) Baseline characteristics: gender, age, body mass index (BMI), and preoperative comorbidities; (2) Surgical parameters: operative approach, intraoperative blood loss, and postoperative complications and so on; (3) Laboratory findings: serum tumor markers (carcinoembryonic antigen [CEA] and Cyfra21-1 levels); (4) Radiological features: Tumor size and location, Consolidation-to-tumor ratio (CTR), Mean CT attenuation value, Morphological characteristics (lobulation, spiculation and so on); (5) Pathological outcomes: final histopathological staging, lymph node metastasis status, pleural invasion, high-grade patterns, lymphovascular invasion (LVI), Spread through air spaces (STAS). Data quality control measures included removal of statistical outliers and imputation of missing values.\u003c/p\u003e\u003cp\u003eCTR is defined as the ratio of the largest solid diameter to the largest tumor diameter, with a value of 0 representing a pure ground-glass opacity and a value of 1 indicating a pure solid nodule[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePreoperative Evaluation and Surgical Management\u003c/h3\u003e\n\u003cp\u003ePreoperative assessment of lymph node status and distant metastases was conducted by board-certified radiologists specializing in thoracic oncology. All surgical procedures were performed by fellowship-trained thoracic surgeons with extensive experience in oncologic resections. Intraoperatively, the tumor was completely resected and SLND was routinely performed.\u003c/p\u003e\n\u003ch3\u003ePathological Examination\u003c/h3\u003e\n\u003cp\u003eAll specimens were sent to the Department of Pathology for pathomorphological examination, and patients were subsequently stratified into two groups (nodal positive and nodal negative) based on final pathological findings\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eInitial comparative analysis between node-positive and node-negative groups was performed for all clinical variables. Continuous variables were reported as median with interquartile range (IQR), while categorical variables were presented as frequencies with percentages. Univariate logistic regression was conducted with OLNM as the primary outcome to screen potential predictors, excluding surgical variables. The discriminative ability of each variable was evaluated using the area under the receiver operating characteristic curve (AUC). Variables demonstrating both statistical significance (P \u0026lt; 0.05) and adequate discriminative power (AUC \u0026gt; 0.65) were selected for subsequent multivariate analysis. A bidirectional stepwise multivariate logistic regression was employed to identify independent predictors of OLNM. Multicollinearity was assessed using variance inflation factors (VIF), with VIF ≤ 5 indicating acceptable absence of covariance among variables in the final model. Based on the multivariate analysis results, we constructed a predictive nomogram for OLNM. The model's performance was comprehensively evaluated through receiver operating characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) to assess the discriminative power, calibration, and clinical utility. Optimal cutoff values for continuous predictors were determined using the Youden index to maximize the sum of sensitivity and specificity, facilitating clinical application. All analyses were performed using R statistical software (version 4.4.3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"RESULT","content":"\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 452 patients with clinical stage IA LUL lung adenocarcinoma were included in the study. Among them, 56 patients (12.4%) were pathologically confirmed to have OLNM (pN + group), while the remaining 396 patients (87.6%) were node-negative (pN- group). The baseline characteristics of the two groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.Significant differences were observed between the pN + and pN- groups in several variables, including high-grade patterns (96.4% vs. 32.8%, p \u0026lt; 0.001), STAS (44.6% vs. 13.9%, p \u0026lt; 0.001), LVI (37.5% vs. 2.5%, p \u0026lt; 0.001), pleural invasion (57.1% vs. 16.7%, p \u0026lt; 0.001), CTR ≥ 50% (98.2% vs. 53.8%, p \u0026lt; 0.001), Location (42.9% vs. 8.3%, p \u0026lt; 0.001), and mean CT value (32.58 vs. -220.34, p \u0026lt; 0.001). Furthermore, the spiculated sign (78.6% vs 52.0%, p \u0026lt; 0.001) and lobulation sign (71.4% vs. 44.7%, p \u0026lt; 0.001) were significantly more prevalent in the pN + group. There were no statistically significant differences between the two groups in terms of age, gender, preoperative comorbidities, BMI, postoperative complications and so on.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eCharacteristics between two groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n = 452)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epN-(n = 396)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epN+(n = 56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003e\u003cb\u003eGender\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAtherosclerosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh-grade patterns\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (96.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSTAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePleural invasion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCTR\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e≥ 50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e213 (53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e395 (87.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e363 (91.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eperipheral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdjacent pleura\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e266 (58.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e222 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpiculated sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e206 (52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLobulation sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e177 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIrregular\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e408 (90.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e364 (91.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePleural Indentation Sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228 (50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e190 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38 (67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVacuole Sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e157 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142 (35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVascular penetration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e427 (94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e373 (94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (96.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.00 [55.00, 68.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.00 [54.75, 68.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.50 [56.50, 68.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/㎡)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.22 [22.23, 26.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.19 [22.31, 26.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.86 [21.98, 26.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCEA (ng/ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.24 [1.41, 3.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.08 [1.35, 3.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.28 [2.41, 7.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyfra21-1 (ng/ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.40 [1.81, 3.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.40 [1.81, 3.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.40 [1.85, 3.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCT size (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60 [1.30, 2.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.50 [1.20, 2.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10 [1.60, 2.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean CT value (HU)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-186.32 [-417.32, 20.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-220.34 [-451.02, -28.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.58 [21.31, 44.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of tube placement(day)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 [3.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00 [3.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00 [3.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntraoperative bleeding(ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.00 [20.00, 50.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.00 [20.00, 50.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.00 [20.00, 50.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConversion to open\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplication\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e419 (92.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e364 (91.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003earrhythmia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epneumothorax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurgical Procedures\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewedge resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esegmentectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elobectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202 (51.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]: \u003cb\u003eContinuous data are expressed as median (interquartile range) and categorical data are expressed as numbers (percentages). COPD: Chronic Obstructive Pulmonary Disease; LVI: lymphovascular invasion; STAS: spread through air spaces; CTR: consolidation-tumor ratio; BMI: body mass index; CEA: carcinoembryonic antigen; CT: computed tomography.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis identified 14 variables significantly associated with OLNM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 9 variables also exhibited an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.65 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These included high-grade pattern (OR\u0026thinsp;=\u0026thinsp;55.25, 95% CI: 13.26\u0026ndash;230.11, AUC\u0026thinsp;=\u0026thinsp;0.818), STAS (OR\u0026thinsp;=\u0026thinsp;5.00, 95% CI: 2.75\u0026ndash;9.10, AUC\u0026thinsp;=\u0026thinsp;0.654), LVI (OR\u0026thinsp;=\u0026thinsp;23.16, 95% CI: 10.11\u0026ndash;53.05, AUC\u0026thinsp;=\u0026thinsp;0.675), pleural invasion (OR\u0026thinsp;=\u0026thinsp;6.67, 95% CI: 3.69\u0026ndash;12.05, AUC\u0026thinsp;=\u0026thinsp;0.702), CTR (OR\u0026thinsp;=\u0026thinsp;47.25, 95% CI: 6.48\u0026ndash;344.51, AUC\u0026thinsp;=\u0026thinsp;0.722), Location (OR\u0026thinsp;=\u0026thinsp;8.25, 95% CI: 4.36\u0026ndash;15.62, AUC\u0026thinsp;=\u0026thinsp;0.673), CEA (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.17\u0026ndash;1.39, AUC\u0026thinsp;=\u0026thinsp;0.684), CT size (OR\u0026thinsp;=\u0026thinsp;5.96, 95% CI: 3.29\u0026ndash;10.81, AUC\u0026thinsp;=\u0026thinsp;0.717), and mean CT value (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.01\u0026ndash;1.02, AUC\u0026thinsp;=\u0026thinsp;0.868).\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\u003eUnivariate Analysis and Discriminative Performance of Candidate Variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95%CI(AUC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.48\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4120\u0026thinsp;~\u0026thinsp;0.5489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes Mellitus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.45\u0026thinsp;~\u0026thinsp;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4514\u0026thinsp;~\u0026thinsp;0.5501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.62 (0.65\u0026thinsp;~\u0026thinsp;20.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4878\u0026thinsp;~\u0026thinsp;0.5378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAtherosclerosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91 (0.31\u0026thinsp;~\u0026thinsp;2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4601\u0026thinsp;~\u0026thinsp;0.5331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh-grade patterns\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.25(13.26\u0026thinsp;~\u0026thinsp;230.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.8180\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7843\u0026thinsp;~\u0026thinsp;0.8517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSTAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.00(2.75\u0026thinsp;~\u0026thinsp;9.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.6538\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5859\u0026thinsp;~\u0026thinsp;0.7216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.16(10.11\u0026thinsp;~\u0026thinsp;53.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.6749\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6104\u0026thinsp;~\u0026thinsp;0.7393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePleural\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.67(3.69\u0026thinsp;~\u0026thinsp;12.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6345\u0026thinsp;~\u0026thinsp;0.7703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.93\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4369\u0026thinsp;~\u0026thinsp;0.6091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCTR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.25 (6.48\u0026thinsp;~\u0026thinsp;344.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7221\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6920\u0026thinsp;~\u0026thinsp;0.7523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.25 (4.36\u0026thinsp;~\u0026thinsp;15.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.6726\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6058\u0026thinsp;~\u0026thinsp;0.7394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdjacent pleura\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.87 (1.47\u0026thinsp;~\u0026thinsp;5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5531\u0026thinsp;~\u0026thinsp;0.6720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpiculated sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.38 (1.73\u0026thinsp;~\u0026thinsp;6.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5732\u0026thinsp;~\u0026thinsp;0.6923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLobulation sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.09 (1.68\u0026thinsp;~\u0026thinsp;5.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5691\u0026thinsp;~\u0026thinsp;0.6982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIrregular\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.32 (0.15\u0026thinsp;~\u0026thinsp;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3774\u0026thinsp;~\u0026thinsp;0.4891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePleural Indentation Sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.29 (1.26\u0026thinsp;~\u0026thinsp;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5329\u0026thinsp;~\u0026thinsp;0.6658\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVacuole Sign\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.35\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3915\u0026thinsp;~\u0026thinsp;0.5178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVascular penetration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.66 (0.38\u0026thinsp;~\u0026thinsp;7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4841\u0026thinsp;~\u0026thinsp;0.5383\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.98\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4448\u0026thinsp;~\u0026thinsp;0.6009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCEA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.27 (1.17\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.6836\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6008\u0026thinsp;~\u0026thinsp;0.7663\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyfra21-1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17 (0.91\u0026thinsp;~\u0026thinsp;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3894\u0026thinsp;~\u0026thinsp;0.5560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCT size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.96 (3.29\u0026thinsp;~\u0026thinsp;10.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.7165\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6373\u0026thinsp;~\u0026thinsp;0.7957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean CT value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.8680\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8321\u0026thinsp;~\u0026thinsp;0.9040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eVariables met criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.65) were included in the multivariate model. OR: Odds Ratio; CI: Confidence Interval; COPD: Chronic Obstructive Pulmonary Disease; LVI: lymphovascular invasion; STAS: spread through air spaces; BMI: body mass index; CEA: carcinoembryonic antigen; CTR: consolidation-tumor ratio; CT: computed tomography.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the initial multivariate logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which included postoperative pathological variables, high-grade pattern (OR\u0026thinsp;=\u0026thinsp;8.92, 95% CI: 1.88\u0026ndash;42.37, p\u0026thinsp;=\u0026thinsp;0.007), LVI (OR\u0026thinsp;=\u0026thinsp;7.05, 95% CI: 2.35\u0026ndash;21.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), peripheral location (OR\u0026thinsp;=\u0026thinsp;10.30, 95% CI: 3.67\u0026ndash;28.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CT size (OR\u0026thinsp;=\u0026thinsp;2.78, 95% CI: 1.29\u0026ndash;6.02, p\u0026thinsp;=\u0026thinsp;0.009), CEA (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.02\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.021), and mean CT value (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.003\u0026ndash;1.013, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as independent predictors of OLNM.\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\u003eMultivariate logistic regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-grade patterns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.88\u0026thinsp;~\u0026thinsp;42.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0069\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02\u0026thinsp;~\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0213\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.35\u0026thinsp;~\u0026thinsp;21.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.67\u0026thinsp;~\u0026thinsp;28.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.29\u0026thinsp;~\u0026thinsp;6.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0093\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean CT value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.003\u0026thinsp;~\u0026thinsp;1.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eOR: Odds Ratio; CI: Confidence Interval; CEA: carcinoembryonic antigen; LVI: lymphovascular invasion; CT: computed tomography.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTo develop a preoperative predictive model, we excluded postoperative pathological variables (high-grade patterns, LVI, and STAS) and refit the multivariate model using only preoperative variables.\u003c/b\u003e The revised model retained CEA (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.07\u0026ndash;1.33, p\u0026thinsp;=\u0026thinsp;0.001), Location (OR\u0026thinsp;=\u0026thinsp;10.38, 95% CI: 4.22\u0026ndash;27.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CT size (OR\u0026thinsp;=\u0026thinsp;3.65, 95% CI: 1.84\u0026ndash;7.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mean CT value (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.008\u0026ndash;1.018, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as significant predictors.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eModel Performance and Validation\u003c/h2\u003e\u003cp\u003eThe original model (including pathological variables) demonstrated excellent discriminative ability with an AUC of 0.961 (95% CI: 0.943\u0026ndash;0.980). The revised preoperative model also showed strong performance, with an AUC of 0.935 (95% CI: 0.907\u0026ndash;0.963) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The nomogram constructed based on the preoperative model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For each parameter, the corresponding point value is determined by drawing a vertical line from the measurement to the upper points scale. These individual point values are then summed to obtain a total score, which is subsequently projected downward to the predicted probability axis to yield the patient's personalized risk estimate ranging from 10\u0026ndash;90%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe calibration curve indicated good agreement between predicted and observed probabilities of OLNM, with a mean absolute error of 0.0148 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Decision curve analysis revealed that the nomogram provided a higher net benefit across a wide range of risk thresholds compared to the \"treat-all\" or \"treat-none\" strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eOptimal Cutoff Values\u003c/h2\u003e\u003cp\u003eUsing the Youden index, the optimal cutoff values for continuous predictors were determined as follows: CEA\u0026thinsp;\u0026ge;\u0026thinsp;2.45 ng/mL (sensitivity: 75%, specificity: 58.6%), CT size\u0026thinsp;\u0026ge;\u0026thinsp;2.05 cm (sensitivity: 50%, specificity: 86.9%), and mean CT value \u0026ge; -19.80 HU (sensitivity: 94.6%, specificity: 76.5%).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study represents the first comprehensive effort to develop a preoperative predictive model specifically targeting OLNM in cIA LUL lung adenocarcinoma. Our findings not only validate previously recognized risk factors but also identify distinctive anatomical and biological characteristics of LUL tumors that contribute to their unique metastatic behavior. The strong discriminative performance of our nomogram (AUC\u0026thinsp;=\u0026thinsp;0.935) underscores its potential utility in enhancing preoperative risk stratification. By providing a more accurate assessment of metastatic risk, this model can inform both surgical decision-making and the selection of adjuvant therapies in this anatomically complex and clinically significant subgroup.\u003c/p\u003e\u003cp\u003eThe unique anatomical complexity of the LUL necessitates a tailored approach to lymph node assessment. Unlike tumors in other pulmonary lobes, LUL tumors exhibit distinct metastatic patterns, particularly a tendency for skip metastases to station 5 lymph nodes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This phenomenon may account for the higher-than-expected rate of occult metastases observed in our study (12.4%). This biological behavior, coupled with the technical challenges arising from the vulnerable course of the left recurrent laryngeal nerve near the aortopulmonary window, presents a critical clinical dilemma: conventional imaging modalities frequently fail to detect nodal involvement, while aggressive dissection poses a substantial risk of RLN injury[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our findings demonstrate that central tumor location and radiological solidity are not merely anatomical descriptors but significant predictors of occult metastasis, potentially reflecting an invasive tumor phenotype with a propensity for preferential lymphatic spread. The nomogram assists surgeons in balancing oncologic thoroughness with the imperative of functional preservation by quantifying the risk-benefit ratio of lymphadenectomy.\u003c/p\u003e\u003cp\u003eOur nomogram addresses two pivotal clinical challenges in the management of LUL adenocarcinoma. First, it provides a data-driven framework for optimizing the extent of lymphadenectomy by balancing the need for accurate pathological staging with the risk of RLN injury. For high-risk patients\u0026mdash;particularly those with centrally located tumors exhibiting solid radiographic characteristics and elevated CEA levels\u0026mdash;our model supports comprehensive lymph node dissection, despite the associated technical challenges and increased risk of RLN injury. This recommendation is especially relevant for lymph node stations 5 and 6 (aortopulmonary window), where the anatomical proximity of the left RLN has traditionally led to more conservative surgical approaches[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The course of the left RLN, which loops around the aortic arch from anterior to posterior, along with its well-documented anatomical variability[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], further complicates surgical intervention in this region. Given that RLN injury is a common complication that can substantially impair quality of life[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], our model offers objective, risk-based criteria to justify aggressive lymphadenectomy only when it is oncologically justified.\u003c/p\u003e\u003cp\u003eConversely, in low-risk patients presenting with peripheral, ground-glass opacity\u0026ndash;predominant tumors, the nomogram supports a strategy of limited nodal sampling. This approach minimizes surgical morbidity without compromising oncologic efficacy. It enables precise identification of patients who are most likely to benefit from extensive nodal evaluation, while sparing low-risk individuals from the potential complications associated with unnecessary radical lymphadenectomy.\u003c/p\u003e\u003cp\u003eSecond\u0026mdash;and perhaps more importantly\u0026mdash;our nomogram facilitates the identification of a clinically significant subset of stage IA patients who, despite being classified as \"low risk\" under conventional TNM staging criteria, may in fact benefit from adjuvant therapy. This insight is particularly relevant given that current clinical guidelines generally do not recommend adjuvant treatment for patients with stage IA disease[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our data indicate that approximately 12.4% of cIA LUL adenocarcinomas harbor occult nodal metastases. These patients likely constitute a high-risk group that could derive substantial benefit from early systemic therapy. The capacity to preoperatively identify such individuals using routinely accessible clinical and radiological parameters holds the potential to reshape existing treatment paradigms. It enables the initiation of adjuvant therapies at a stage when micrometastatic disease is not yet clinically evident, thereby improving the likelihood of therapeutic success. This approach is particularly timely in the current era of increasingly effective targeted therapies and immunotherapies, where early intervention\u0026mdash;especially in molecularly selected populations\u0026mdash;has been associated with improved clinical outcomes.\u003c/p\u003e\u003cp\u003eThe association between tumor center location and lymph node metastasis was first reported by Ketchedjian et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While varying definitions of \u0026ldquo;central\u0026rdquo; tumor location exist, our study adopts the criteria established by the American College of Chest Physicians[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which defines centrally located tumors as those situated within the inner third of the hemithorax. It has been proposed that the primary tumor site may influence the metastatic pattern of NSCLC[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], with central lung adenocarcinomas demonstrating a higher propensity for regional lymph node metastasis compared to peripheral tumors\u0026mdash;a trend that is associated with poorer prognosis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and aligns with our findings. Furthermore, tumor location appears to correlate with specific genetic alterations in lung cancer. For instance, an increased incidence of ALK rearrangements has been reported in centrally located tumors, which are also characterized by a higher frequency of lymph node involvement[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These observations underscore the critical importance of early and accurate assessment of lymph node metastasis in patients with lung cancer, particularly those presenting with central tumor location.\u003c/p\u003e\u003cp\u003eThe radiological features identified in our study\u0026mdash;mean CT attenuation (OR\u0026thinsp;=\u0026thinsp;1.01) and tumor size (OR\u0026thinsp;=\u0026thinsp;3.65)\u0026mdash;are reflective of the underlying biological aggressiveness of LUL adenocarcinomas. Imaging modalities play a pivotal role in the diagnosis and management of lung cancer, offering non-invasive yet informative assessments that complement histopathological analysis. In particular, CT excels at characterizing suspicious pulmonary lesions by providing critical prognostic indicators such as nodule size, anatomical location, and morphological attributes, all of which significantly inform therapeutic decision-making[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Recent studies have reinforced the prognostic relevance of these radiological characteristics, demonstrating that increased lesion size and higher tissue density are consistently associated with more aggressive tumor behavior[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our findings further support this evidence, showing that tumors with elevated radiodensity\u0026mdash;indicative of a predominant solid component\u0026mdash;and larger dimensions are more strongly associated with adverse pathological features suggestive of malignancy. These associations highlight the value of preoperative imaging not only for diagnostic purposes but also for risk stratification and treatment planning.\u003c/p\u003e\u003cp\u003eElevated preoperative CEA levels (OR\u0026thinsp;=\u0026thinsp;1.19) provide an additional layer of risk stratification, suggesting the involvement of systemic tumor\u0026ndash;host interactions in the development of OLNM. CEA is a well-established biomarker associated with tumor burden and biological aggressiveness, and its elevation may indicate the presence of micrometastatic disease or an immunosuppressive tumor microenvironment that facilitates lymphatic dissemination[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. When combined with radiological parameters, CEA contributes to the construction of a multidimensional risk profile that transcends the limitations of traditional anatomical staging. This integrative approach offers a more comprehensive and clinically relevant assessment of metastatic potential, thereby enhancing the precision of treatment planning.\u003c/p\u003e\u003cp\u003eWhile this study offers valuable insights, several limitations should be acknowledged. First, its retrospective design inherently restricts the ability to establish causal relationships, and despite the application of rigorous inclusion criteria, the potential for selection bias remains. Second, although imaging was consistently utilized for staging, its limited spatial resolution\u0026mdash;particularly in assessing the aortopulmonary window, a region of critical importance for left upper lobe tumors\u0026mdash;may have led to an underestimation of central tumor prevalence and nodal involvement. Third, the single-institution nature of the study, while beneficial in maintaining consistency in surgical and pathological protocols, may limit the generalizability of our findings to broader clinical settings with varying patient demographics and treatment practices. Fourth, the absence of comprehensive molecular characterization restricts deeper biological interpretation, particularly with regard to tumor heterogeneity and the potential influence of genetic alterations on metastatic behavior. Although the study period (2018\u0026ndash;2022) reflects contemporary clinical practice, ongoing advancements in imaging technologies and molecular diagnostics may soon surpass some of the predictive parameters employed in our model. Finally, while our analysis focused on anatomical and radiological predictors, emerging biomarkers\u0026mdash;such as circulating tumor DNA or specific driver mutations\u0026mdash;were not incorporated, representing an area for future research and potential model refinement.\u003c/p\u003e\u003cp\u003eDespite these limitations, our study establishes a foundation for lobe-specific risk stratification in LUL adenocarcinoma. The clinical relevance of our findings is reinforced by the reliance on routinely available preoperative parameters, enhancing the feasibility of implementation across diverse clinical settings. As the field of personalized medicine continues to advance, the integration of anatomically and biologically informed models such as ours is poised to play an increasingly pivotal role in guiding individualized treatment strategies for patients with lung cancer.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, this study presents a lobe-specific predictive model for OLNM in LUL adenocarcinoma, highlighting the combined influence of anatomical, radiological, and biological factors. By moving beyond the traditional \u0026ldquo;one-size-fits-all\u0026rdquo; approach to staging, our findings support the development of personalized surgical and adjuvant strategies tailored to the distinct risk profile of LUL tumors. This model not only improves preoperative risk stratification but also reinforces the need for lobe-specific guidelines in lung cancer management, with the potential to enhance clinical outcomes in this anatomically and biologically unique subgroup.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNSCLC = Non-Small Cell Lung Cancer\u003c/p\u003e\n\u003cp\u003eLUL = The left upper lobe\u003c/p\u003e\n\u003cp\u003eOLNM = occult lymph node metastases\u003c/p\u003e\n\u003cp\u003ecIA = Clinical Stage IA\u003c/p\u003e\n\u003cp\u003eCT = Computed Tomography\u003c/p\u003e\n\u003cp\u003eCEA = Carcinoembryonic Antigen\u003c/p\u003e\n\u003cp\u003ePET/CT = Positron Emission Tomography/Computed Tomography\u003c/p\u003e\n\u003cp\u003eSLND = Systematic lymph node dissection\u003c/p\u003e\n\u003cp\u003eRLN = recurrent laryngeal nerve\u003c/p\u003e\n\u003cp\u003eCOPD = Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eLVI = Lymphovascular Invasion\u003c/p\u003e\n\u003cp\u003eSTAS = Spread Through Air Spaces\u003c/p\u003e\n\u003cp\u003eCTR = Consolidation-Tumor Ratio\u003c/p\u003e\n\u003cp\u003eBMI = Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI = Confidence Intervals\u003c/p\u003e\n\u003cp\u003eOR = Odds Ratio\u003c/p\u003e\n\u003cp\u003eIQR = Interquartile Range\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll procedures adhered to the principles outlined in the Declaration of Helsinki. The study protocol and related documents were approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (file number: 2024-KY-015), and the need for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003eMR-11-24-018343\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e: This work was supported by the Elite Medical Professionals Project of China-Japan Friendship Hospital (ZRJY2023-QM34), the National High Level Hospital Clinical Research Funding, and the Special Fund (2060204) of the State Key Laboratory of Respiratory Health and Multimorbidity (2024-QZZZ-05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of General Thoracic Surgery, China-Japan Friendship Hospital, No. 2 Yinghua East Road, Chaoyang District, Beijing, 100029, China.\u003c/p\u003e\n\u003cp\u003eChina-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College, Beijing, China.\u003c/p\u003e\n\u003cp\u003eWeijie Zhu, Defeng Luo, Kunsong Su, Yu Han, Qiduo Yu, Hongxiang Feng, Chaoyang Liang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.Z. and C.L conceived and designed the study. D.L. was involved in data acquisition and wrote the manuscript. K.S., Y.H., Q.Y., H.F. and C.L. analyzed the data. W.Z. and C.L revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to \u003cstrong\u003eChaoyang Liang\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNasim F, Sabath BF, Eapen GA. \u003cem\u003eLung Cancer\u003c/em\u003e. Med Clin North Am 2019;\u003cstrong\u003e103\u003c/strong\u003e:463-73.\u003c/li\u003e\n\u003cli\u003eHutchinson BD, Shroff GS, Truong MT, Ko JP. \u003cem\u003eSpectrum of Lung Adenocarcinoma\u003c/em\u003e. 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Despite advancements in preoperative imaging techniques, occult lymph node metastases (OLNM) continue to pose a significant diagnostic challenge—especially in tumors located in the left upper lobe (LUL), owing to their complex anatomical structure and unique patterns of lymphatic spread. This study aims to develop a preoperative predictive model for OLNM in patients with clinical stage IA (cIA) LUL adenocarcinoma, with the goal of improving risk stratification and informing individualized treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A retrospective cohort study was conducted involving 452 patients diagnosed with cIA LUL adenocarcinoma who underwent surgical resection between 2018 and 2022. Clinical, radiological, and pathological data were collected, including tumor location, tumor size, mean computed tomography (CT) attenuation value, and serum carcinoembryonic antigen (CEA) levels. Univariate and multivariate logistic regression analyses were used to identify independent predictors of OLNM. A predictive nomogram was subsequently developed and validated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOLNM was detected in 12.4% of patients. Multivariate analysis identified central tumor location (odds ratio [OR] = 10.38, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001), CT tumor size ≥ 2.05 cm (OR = 3.65, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), mean CT attenuation value ≥ -19.80 Hounsfield units (HU) (OR = 1.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and elevated CEA levels ≥ 2.45 ng/ml (OR = 1.19, \u003cem\u003ep\u003c/em\u003e = 0.001) as independent preoperative predictors of OLNM. The nomogram demonstrated excellent discriminative performance (area under the curve [AUC] = 0.935) and clinical utility, facilitating individualized risk assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study proposes a lobe-specific predictive model for OLNM in patients with LUL adenocarcinoma, incorporating anatomical, radiological, and serological parameters. The resulting nomogram enhances preoperative risk stratification and supports the development of tailored surgical and adjuvant treatment strategies. These findings underscore the importance of lobe-specific considerations in NSCLC management and may contribute to improved clinical outcomes through more precise therapeutic decision-making.\u003c/p\u003e","manuscriptTitle":"A Preoperative Nomogram for Predicting Occult Lymph Node Metastasis in Left Upper Lobe Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:30:18","doi":"10.21203/rs.3.rs-7467805/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-10T09:16:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T09:08:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-08T14:03:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-08T14:01:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2025-08-27T04:51:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"191a168b-5a18-4675-b299-98b4ec79dfce","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T19:30:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 19:30:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7467805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7467805","identity":"rs-7467805","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}