A Preoperative Risk Scoring System for Survival Prediction in Clinical Stage IB Lung Adenocarcinoma: A Multicenter Study

A Preoperative Risk Scoring System for Survival Prediction in Clinical Stage IB Lung Adenocarcinoma: A Multicenter Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Preoperative Risk Scoring System for Survival Prediction in Clinical Stage IB Lung Adenocarcinoma: A Multicenter Study Kotaro Murakami, Tetsuya Isaka, Takuya Nagashima, Hiroyuki Adachi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7173846/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Clinical stage (c-stage) IB lung adenocarcinoma (LUAD) presents variable survival outcomes, and the prognostic significance of factors such as ground-glass opacity components and positron emission tomography (PET) metrics remains unclear. Despite recent advances, no preoperative scoring model has been established to stratify risk in this subgroup. We aimed to identify preoperative prognostic factors in c-stage IB LUAD and develop a simple scoring system for predicting overall survival (OS). Methods We retrospectively analyzed data from 245 patients with c-stage IB LUAD who underwent lobectomy at three institutions between 2010 and 2020. Cox regression analysis was performed to identify independent preoperative prognostic factors for OS. A risk score was developed by assigning points to each factor based on the regression coefficients. Patients were then stratified into four risk groups based on the total score. Results Multivariate analysis identified smoking history (regression coefficient: 0.98; hazard ratio [HR]: 2.68; 95% confidence interval [CI]: 1.13–6.33; p = 0.025), elevated serum carcinoembryonic antigen (CEA) levels (regression coefficient: 1.06; HR: 2.89; 95%CI: 1.42–5.91; p = 0.004), and high maximum standardized uptake value (SUVmax) on PET (regression coefficient: 1.04; HR: 2.84; 95%CI: 1.16–6.98; p = 0.023) as independent factors of poor prognosis. A scoring system was established by assigning one point to each factor. Patients were stratified into four risk groups: low (score 0, n = 41), moderate (score 1, n = 84), moderately high (score 2, n = 77), and extremely high (score 3, n = 43). Five-year OS rates were 100.0%, 89.3%, 74.0%, and 52.1%, respectively ( p < 0.001). The prognostic model demonstrated good predictive performance (area under the curve [AUC], 0.738; 95%CI, 0.661–0.815) and concordance index (AUC, 0.753; 95%CI, 0.682–0.824). Notably, patients with a score of 0 showed low-grade tumors and favorable prognosis, whereas those with a score of 3 had more aggressive pathological characteristics and significantly worse outcomes. Conclusions We developed and validated a simple preoperative scoring system using smoking history, serum CEA level, and tumor SUVmax to predict prognosis in c-stage IB LUAD. This model provides a practical tool for risk stratification and may support individualized treatment decisions, including the consideration of induction therapy in selected cases. Lung adenocarcinoma Clinical stage IB Overall survival Scoring system Preoperative risk Figures Figure 1 Figure 2 Background Lung cancer remains a leading cause of morbidity and mortality worldwide. Among lung cancer cases, over 60% of patients are diagnosed with lung adenocarcinoma (LUAD), the most common histological subtype, particularly in Japan [ 1 ]. The Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) staging system is the only established tool for stratifying recurrence risk in lung cancer [ 2 ]. Recently, ground-glass opacity (GGO) components and the maximum standardized uptake value (SUVmax) on positron emission tomography (PET) have emerged as important prognostic factors in clinical stage (c-stage) I LUAD [ 3 – 5 ]. Following the TNM classification update to the 8th edition, several studies have explored preoperative prognostic factors in c-stage IA non-small cell lung cancer (NSCLC). Hattori et al. [ 6 ] reported that the presence of a GGO component in stage IA NSCLC is associated with favorable prognosis. Similarly, a large meta-analysis of patients with c-stage IA NSCLC reported that part-solid tumors had a better prognosis than pure-solid tumors [ 7 ]. Additional studies have also identified PET imaging as a significant prognostic tool in this population [ 8 , 9 ]. While numerous studies have investigated prognostic factors in c-stage IA NSCLC, the prognostic relevance of GGO components and PET metrics in c-stage IB NSCLC remains unclear. Moreover, to our knowledge, no study has specifically analyzed the prognostic significance of preoperative factors such as GGO components or SUVmax in c-stage IB NSCLC (TNM 8th edition), nor developed a risk stratification model for this subgroup. Lobectomy with lymph node dissection is the standard treatment for c-stage IB lung cancer [ 10 ]. However, the 5-year overall survival (OS) rate for patients with c-stage IB (T2aN0M0) NSCLC remains unsatisfactory, ranging from 68–71.5% [ 11 , 12 ]. In recent years, several trials have evaluated the efficacy of neoadjuvant chemotherapy and immunotherapy, showing some therapeutic benefit [ 13 , 14 ]. However, these trials included only a limited number of patients with c-stage IB (TNM 8th edition), highlighting the need for further studies to identify eligible candidates. Notably, a subset of patients with c-stage IB may benefit from preoperative treatment. Identifying and scoring prognostic factors could enable better risk stratification, guiding clinical decision-making, such as optimizing treatment selection and identifying candidates for induction therapy. Preoperative risk stratification may help tailor individualized treatment strategies to improve patient outcomes. In this study, we included GGO components and SUVmax, which have been established as preoperative prognostic factors for c-stage IA, to assess their prognostic significance in c-stage IB LUAD. However, their relevance in this subgroup remains unclear. Importantly, GGO components are specific to LUAD, whereas non-adenocarcinomas typically present as pure-solid tumors. Furthermore, SUVmax has been reported to be significantly higher in non-LUADs compared to LUADs [ 15 ]. Therefore, the present study focused exclusively on patients with c-stage IB LUAD. This study aimed to identify preoperative prognostic factors for c-stage IB LUAD and develop a simple scoring tool for preoperative risk prediction. Methods Patients A retrospective chart review was performed using our prospectively maintained database to identify patients who underwent surgical resection for primary LUAD at Kanagawa Cancer Center, Tokyo Medical University, and Hiroshima University Hospital between January 2010 and December 2020. The study was approved by the Institutional Review Board of Kanagawa Cancer Center (24 Eki, 54), with a waiver for written informed consent. Medical record data were updated as of May 2023 and extracted based on clinicopathological features and treatment histories. Patients without documented clinical or radiographic disease progression were censored at the last follow-up visit. Those with non-adenocarcinoma or those who underwent sublobar resection (wedge resection or segmentectomy) were excluded. All included patients underwent preoperative high-resolution computed tomography (CT) and fluorodeoxyglucose-PET/CT (FDG-PET/CT). Disease staging was determined according to the 8th edition of the UICC TNM classification for lung and pleural tumors [ 2 ]. Complete resection was defined as no evidence of residual cancer, either macroscopically or microscopically. Patient Follow-Up Follow-up evaluations included physical examination, chest radiography, chest CT, and blood tests, including relevant tumor markers. Follow-up chest CT scans were performed every 6–12 months. If recurrence was suspected, additional diagnostic evaluations were conducted, including CT scans of the chest and abdomen, brain magnetic resonance imaging (MRI), and FDG-PET/CT. FDG-PET/CT Assessment FDG-PET/CT scans were performed using one of the following integrated three-dimensional PET/CT scanners: Discovery MI (GE Healthcare, Little Chalfont, United Kingdom), Aquiduo (Toshiba Medical Systems Corporation, Tochigi, Japan), or Biograph Sensation 16 (Siemens Healthcare, Erlangen, Germany). The SUVmax values were standardized across the three institutions using the method proposed by Nakayama et al. [ 16 ]. Radiologists at each institution independently determined the original SUVmax values. Pathological Assessment All collected surgical specimens were fixed in 10% formalin and embedded in paraffin DNA was analyzed for epidermal growth factor receptor (EGFR) mutations using the Cobas® EGFR Mutation Test version 2 (Cobas; Roche Diagnostics, Basel, Switzerland) [ 17 ]. Tumor differentiation was classified as well differentiated (minimally invasive adenocarcinoma or lepidic), moderately differentiated (acinar or papillary), or poorly differentiated (solid or micropapillary) adenocarcinoma [ 18 ]. Statistical Analyses Relapse-free survival (RFS) and OS were estimated using the Kaplan–Meier method, with differences in survival rates assessed by log-rank tests. RFS was defined as the time from surgery to recurrence or death from any cause, while OS was defined as the time from surgery to death or last follow-up for censored patients (those without adverse events during the last observation period). Cutoff values for variables were determined using receiver operating characteristic (ROC) curve analysis with RFS as the outcome, except for age (set at 75 years, the standard for the late elderly population) and serum carcinoembryonic antigen (CEA) (set at 5 ng/mL, the upper limit of normal). Survival curves were constructed using the Kaplan–Meier method. Univariate and multivariate analyses were performed using a Cox proportional hazards regression model, incorporating age, sex, smoking history, serum CEA level, CT tumor consolidation size, presence of pure solid tumors on imaging, and SUVmax value. Smoking history was included due to its reported association with cancer grade [ 19 ]. Multivariate analysis was performed using the backward elimination method. Statistical significance was set at p < 0.05. All statistical analyses were performed using EZR on R Commander, version 1.30 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (R Foundation for Statistical Computing, Vienna, Austria). Development of the Scoring System A risk-scoring tool was developed to predict the prognosis of patients with c-stage IB LUAD. Each independent preoperative predictor was assigned a point value based on regression coefficients derived from the multivariate analysis. Patients were categorized into four risk groups: low (score 0), moderate (score 1), moderately high (score 2), and extremely high (score 3). The accuracy of the scoring system was evaluated using ROC analysis and the concordance index (c-index) [ 20 ]. Results A total of 4,670 consecutive patients with primary lung cancer underwent complete surgical resection. Among the 2,488 patients who underwent lobectomy, 245 with c-stage IB disease were included in this study (Fig. 1 ). The median follow-up time for survivors was 45.0 months (range, 0.5–133.3). Table 1 summarizes the patient characteristics. Among all patients, 55.1% had a history of smoking, and 31.8% of had an elevated preoperative serum CEA level. The median tumor SUVmax was 4.7 (range: 0.0–34.7), with 36.3% of patients having pathological stage (p-stage) II–III disease. Multivariable analysis identified elevated CEA levels (hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.46–3.51; p < 0.001) and SUVmax (HR, 4.60; 95% CI, 2.48–8.55; p < 0.001) as independent predictors of RFS (Table 2 ). For OS, significant factors included smoking history (HR, 2.68; 95% CI, 1.13–6.33; p = 0.025), elevated CEA levels (HR, 2.89; 95% CI, 1.42–5.91; p = 0.004), and SUVmax (HR, 2.84; 95% CI, 1.16–6.98; p = 0.023) (Table 3 ). The cutoff value for SUVmax was determined to be 3.7 (area under the curve [AUC], 0.713; 95% CI, 0.648–0.779) based on ROC curve analysis with RFS as the outcome. Table 1 Patient Characteristics (n = 245) Variables Total (n = 245) Preoperative findings Median age, years (range) 71.0 (34–93) Sex, male (%) 139 (56.7) Smoking history, yes (%) 135 (55.1) Serum CEA level, (%) ≦ 5 ng/mL 167 (68.2) > 5 ng/mL 78 (31.8) Median CT tumor size, cm (range) 3.6 (3.04–8.4) Median CT tumor consolidation size, cm (range) 3.4 (3.04–4.0) Radiological tumor component, (%) pure solid tumor 175 (71.4) part solid tumor 70 (28.6) Median SUVmax (range) 4.7 (0.0–34.7) Postoperative and pathological findings Tumor differentiation, (%) well 38 (15.5) moderate 165 (67.3) poor 42 (17.2) Median pathological invasive tumor size, cm (range) 3.1 (0.2–12.6) Lymphatic permeation, positive (%) 80 (32.7) Vascular invasion, positive (%) 111 (45.3) Visceral pleural invasion, positive (%) 89 (36.3) Nodal metastasis, (%) pN0 191 (78.0) pN1 32 (13.0) pN2 22 (9.0) Pathological stage in the eighth edition, (%) IA 75 (30.6) IB 81 (33.1) II-III 89 (36.3) EGFR mutation, (%) positive 118 (55.9) wild type 93 (44.1) NA 34 Recurrence, yes (%) 70 (28.6) Cause of death lung cancer recurrence 20 other causes 17 CEA, carcinoembryonic antigen; CT, computed tomography; EGFR, epidermal growth factor receptor; SUVmax, maximum standardized uptake value Table 2 Univariable and multivariable analyses for relapse-free survival in all patients with clinical stage IB adenocarcinoma. Univariable analysis Multivariable analysis Raw coefficients Hazard ratio (95% CI) p -value Adjusted coefficients Hazard ratio (95% CI) p -value Age, ≥ 75 years 0.34 1.41 (0.91–2.18) 0.124 Sex, male 0.22 1.25 (0.80–1.95) 0.327 Smoking history, yes 0.22 1.25 (0.80–1.94) 0.322 Serum CEA level, > 5 ng/mL 0.98 2.67 (1.73–4.13) < 0.001 0.82 2.26 (1.46–3.51) < 0.001 CT tumor consolidation size 0.17 1.19 (0.57–2.49) 0.643 Radiological tumor component, pure solid tumor 0.45 1.57 (0.93–2.65) 0.093 SUVmax, ≥ 3.7 1.63 5.10 (2.76–9.43) < 0.001 1.53 4.60 (2.48–8.55) < 0.001 CEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value; CI: confidence interval Table 3 Univariable and multivariable analyses for overall survival in all patients with clinical stage IB adenocarcinoma. Univariable analysis Multivariable analysis Raw coefficients Hazard ratio (95% CI) p -value Adjusted coefficients Hazard ratio (95% CI) p -value Age, ≥ 75 years 0.61 1.84 (0.94–3.61) 0.076 Sex, male 0.94 2.56 (1.15–5.66) 0.021 Smoking history, yes 1.24 3.46 (1.50–7.99) 0.004 0.98 2.68 (1.13–6.33) 0.025 Serum CEA level, > 5 ng/mL 1.37 3.92 (1.97–7.80) < 0.001 1.06 2.89 (1.42–5.91) 0.004 CT tumor consolidation size 1.00 2.72 (0.88–8.43) 0.083 Radiological tumor component, pure solid tumor 0.14 1.15 (0.54–2.46) 0.722 SUVmax, ≥ 3.7 1.27 3.57 (1.48–8.62) 0.005 1.04 2.84 (1.16–6.98) 0.023 CEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value; CI: confidence interval A risk-scoring system was developed based on independent preoperative predictors of OS identified in the multivariate analysis. The regression coefficients (adjusted coefficients) from Table 3 were rounded to the nearest integer to assign point values to each predictor. The scoring system stratified patients into four risk groups: score 0 (n = 41), score 1 (n = 84), score 2 (n = 77), and score 3 (n = 43). Kaplan–Meier survival curves demonstrated a significant decline in both RFS ( p < 0.001; Fig. 2 A) and OS ( p < 0.001; Fig. 2 B) as the risk score increased. The 5-year OS rates were 100.0%, 89.3%, 74.0%, and 52.1% for risk scores of 0, 1, 2, and 3, respectively. Table 4 summarizes patient characteristics stratified by risk score. Patients with a risk score of 0 had significantly better tumor differentiation (36.6%, p < 0.001), a smaller median pathological invasive tumor size (2.1 cm, p < 0.001), and lower rates of vascular invasion (14.6%) and visceral pleural invasion (VPI) (4.9%) (both p < 0.001). Only one patient (2.4%) in this group had pathological lymph node metastasis ( p = 0.037). Furthermore, in this group, the median SUVmax was 2.0, and 61.0% of the patients were p-stage IA. In contrast, patients with a risk score of 3 exhibited larger pathological invasive tumor size (median value: 3.5, p < 0.001), higher SUVmax (median value: 10.1, p < 0.001), and more frequent vascular invasion (65.1%) and VPI (51.2%; both p < 0.001), with 53.5% of patients having p-stage II–III disease. Table 4 Patient characteristics stratified by the prognostic scoring system. Variables (n = 245) Score 0 Low (n = 41) Score 1 Moderate (n = 84) Score 2 Moderately high (n = 77) Score 3 Extremely high (n = 43) p -value Preoperative findings Median age, years (range) 71.0 (47–84) 71.5 (43–93) 72.0 (47–89) 70.0 (34–90) 0.993 Sex, male (%) 32 (78.0) 44 (52.4) 22 (28.6) 35 (81.4) < 0.001 Smoking history, yes (%) 0 (0.0) 36 (42.9) 56 (72.7) 43 (100.0) < 0.001 Serum CEA level, ≥ 5 ng/mL (%) 0 (0.0) 8 (9.5) 30 (39.0) 43 (100.0) < 0.001 Median CT tumor size, cm (range) 3.7 (3.1–5.4) 3.5 (3.1–6.4) 3.4 (3.04–5.1) 3.7 (3.1–8.4) 0.029 Median CT tumor consolidation size, cm (range) 3.44 (3.06–4.0) 3.3 (3.04–4.0) 3.4 (3.04–4.0) 3.7 (3.1–4.0) < 0.001 Radiological tumor component, (%) < 0.001 pure solid tumor 22 (53.7) 52 (61.9) 62 (80.5) 39 (90.7) part solid tumor 19 (46.3) 32 (38.1) 15 (19.5) 4 (9.3) Median SUVmax (range) 2.0 (0.75–3.65) 3.5 (0.0–20.8) 7.5 (1.7–30.9) 10.1 (3.7–34.7) < 0.001 Postoperative and pathological findings Tumor differentiation, (%) < 0.001 well 15 (36.6) 13 (15.5) 7 (9.1) 3 (7.0) moderate 26 (63.4) 65 (77.4) 45 (58.4) 29 (67.4) poor 0 (0) 6 (7.1) 25 (32.5) 11 (25.6) Median pathological invasive tumor size, cm (range) 2.1 (0.4–4.5) 2.9 (0.2–6.8) 3.2 (1.2–6.0) 3.5 (0.8–12.6) < 0.001 Lymphatic permeation, positive (%) 7 (17.1) 30 (35.7) 27 (35.1) 16 (37.2) 0.124 Vascular invasion, positive (%) 6 (14.6) 30 (35.7) 47 (61.0) 28 (65.1) < 0.001 Visceral pleural invasion, positive (%) 2 (4.9) 26 (31.0) 33 (42.9) 22 (51.2) < 0.001 Nodal metastasis, (%) 0.037 pN0 40 (97.6) 66 (78.6) 54 (70.1) 31 (72.1) pN1 0 (0.0) 10 (11.9) 14 (18.2) 8 (18.6) pN2 1 (2.4) 8 (9.5) 9 (11.7) 4 (9.3) Pathological stage in the 8th edition, (%) < 0.001 IA 25 (61.0) 29 (34.5) 16 (20.8) 5 (11.6) IB 11 (26.8) 28 (33.3) 27 (35.1) 15 (34.9) II-III 5 (12.2) 27 (32.1) 34 (44.2) 23 (53.5) Recurrence, yes (%) 3 (7.3) 20 (23.8) 30 (39.0) 17 (39.5) < 0.001 Cause of death lung cancer 0 6 5 8 other causes 0 2 6 7 CEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value This proposed preoperative prognostic scoring system demonstrated favorable predictive performance, based on the ROC curve (AUC, 0.738; 95% CI, 0.661–0.815) and c-index (AUC, 0.753; 95% CI, 0.682–0.824). Discussion This study aimed to identify preoperative prognostic factors in c-stage IB LUAD and develop a simple, yet effective scoring system for preoperative prognostic prediction. In addition to SUVmax, smoking history and elevated CEA levels were identified as significant preoperative factors for prognosis. Interestingly, while the presence of GGO components is a well-established favorable prognostic factor in c-stage IA, it was not associated with prognosis in this cohort of patients with c-stage IB. By incorporating smoking history, elevated CEA levels, and SUVmax, we developed a scoring system that effectively stratified postoperative outcomes. The validity of this scoring system was confirmed, highlighting the heterogeneous nature of c-stage IB LUAD and the challenges in accurately predicting patient outcomes. To our knowledge, this is the first study to evaluate preoperative prognostic factors and propose a scoring system specifically designed for c-stage IB LUAD (TNM 8th edition) following complete resection. The relationship between tobacco smoking and lung cancer development is well-established, with recent studies showing that smokers have a significantly poorer prognosis than non-smokers, particularly in early-stage LUAD [ 21 ]. Sakao et al. [ 19 ] reported that cigarette smoking is associated with the carcinogenesis of moderately to poorly differentiated LUAD, including papillary, acinar, or solid component subtypes. CEA is widely recognized as a valuable biomarker for diagnosing and monitoring lung cancer prognosis [ 22 ]. A retrospective study found that elevated CEA levels are associated with poorer survival outcomes and serve as a risk factor for occult regional lymph node metastasis in patients with stage I NSCLC undergoing surgical resection [ 23 ]. Moreover, previous studies have shown that preoperative SUVmax in the primary tumor of patients with c-stage I disease is associated with disease-free survival and OS [ 4 , 5 ]. In this study, as in previous reports, preoperative smoking history, elevated CEA levels, and high SUVmax were significant prognostic factors. Therefore, incorporating these factors into a scoring system provides a practical and effective tool for predicting prognosis in c-stage IB LUAD. Indeed, patients classified into higher risk groups by our scoring system exhibited larger pathological invasive tumor sizes and higher rates of poor tumor differentiation, vascular invasion, VPI, and lymph node metastasis. Additionally, while the presence of a GGO component is a well-established favorable prognostic factor in c-stage IA, it does not significantly influence prognosis in c-stage IB disease [ 3 , 6 ]. Aokage et al. [ 3 ] reported that approximately 20% of c-stage IB LUAD cases with a GGO component are classified as invasive solid-predominant adenocarcinoma and that the presence of the GGO component was not attributable to the prognosis of this cancer subtype. This may be because, at this stage, the increasing tumor diameter and associated malignant progression outweigh the potential prognostic benefits of a GGO component. Previously reported 5-year OS rates for patients with c-stage IB (T2aN0M0) NSCLC range from 68–71.5% [ 11 , 12 ]. While the previous studies did not stratify prognosis based on preoperative factors, our study successfully demonstrated that preoperative factors, such as smoking history, CEA levels, and SUVmax values, effectively stratify postoperative outcomes in c-stage IB LUAD. Notably, patients with a risk score of 0 had a 5-year OS rate of 100%, comparable to that of c-stage IA1 reported in previous studies [ 11 , 12 ]. Importantly, none of these patients (with a score of 0 on the present scoring system) showed pathologically poor tumor differentiation, and they exhibited lower frequencies of lymphatic permeation, vascular invasion, and VPI (17.1%, 14.6%, and 4.9%, respectively). These results suggest that this scoring system is effective in discriminating low-grade tumors in patients with c-stage IB LUAD. Furthermore, only one patient (2.4%) in this group had lymph node metastasis, supporting the possibility of reduced lymph node dissection. In contrast, patients with a risk score of 3 had significantly worse outcomes, with a 5-year OS rate of 52.1%, equivalent to c-stage IIB–IIIA reported in previous studies [ 11 , 12 ]. This high-risk group was characterized by greater pathological invasive tumor size, more frequent vascular invasion, VPI, and a higher proportion (53.5%) of p-stage II–III disease. Considering their poor prognosis, these patients could potentially benefit from neoadjuvant therapy. Future prospective multicenter validation of this preoperative risk scoring system is needed to explore strategies to improve survival outcomes in extremely high-risk c-stage IB LUAD under the current TNM classification (8th edition). In summary, this study effectively stratified the pathological grade and prognosis of c-stage IB LUAD based on preoperative factors, highlighting its heterogeneous nature. Incorporating this stratification into ongoing discussions regarding perioperative treatment, including preoperative induction therapy, may help refine clinical decision-making. The limitations of this study include its retrospective nature, which introduces potential bias. The statistical analyses may not have been sufficiently robust to identify certain factors, such as sex, as significant. In addition, the long duration of the study may have contributed to substantial sample heterogeneity. Although the cutoff values for SUVmax were calculated using ROC curves and efforts were made to harmonize SUVmax values by correcting inter-facility errors, discrepancies in SUVmax measurements between institutions may still be a concern. Moreover, no standardized surveillance protocol was established across the three participating institutions, either postoperatively or at recurrence, which could have affected the consistency of data collection and outcomes. Due to the small sample size, this scoring system could not be validated with test data. Further prospective studies with larger, multi-institutional cohorts are needed to validate our findings. Additionally, several other limitations should be acknowledged. First, due to the multicenter nature of this study, detailed quantification of smoking exposure, such as pack-years, could not be uniformly assessed in all patients. As a result, smoking intensity was not rigorously evaluated, and only a binary classification (smoker vs. non-smoker) was adopted. Second, EGFR mutation testing was not performed in all patients. Consequently, EGFR status was excluded from the multivariate analysis, even though it may influence treatment decisions, particularly regarding preoperative therapy. Moreover, the EGFR mutation status was determined by preoperative biopsy in some cases and by surgical specimens in others, which could introduce variability. Future analyses incorporating molecular data are required. Third, although GGO was included as a categorical variable (part-solid vs. pure-solid), the consolidation-to-tumor ratio, a quantitative index used in other studies, was excluded due to potential inter-institutional variability in measurement. Despite these limitations, this study successfully identified preoperative prognostic factors in c-stage IB LUAD, marking an important step toward improving preoperative evaluation and treatment strategies for these cases. Conclusions In conclusion, our findings suggest that smoking history, serum CEA level, and tumor SUVmax are critical determinants of prognosis in patients with c-stage IB LUAD, regardless of solid component size or the presence of a GGO component. The scoring system developed using these preoperative factors, in combination with the TNM classification of lung cancer, may enhance prognostic accuracy and aid in clinical decision-making for patients with c-stage IB LUAD. Abbreviations CEA carcinoembryonic antigen CI confidence interval CT computed tomography EGFR epidermal growth factor receptor FDG-PET/CT fluorodeoxyglucose-PET/computed tomography GGO ground-glass opacity HR hazard ratio HRCT high-resolution computed tomography LUAD lung adenocarcinoma MRI magnetic resonance imaging NSCLC non-small cell lung cancer OS overall survival PET positron emission tomography RFS Relapse-free survival ROC receiver operating characteristic SUVmax maximum standardized uptake value UICC Union for International Cancer Control VPI visceral pleural invasion Declarations Acknowledgements The authors would like to thank Editage for their editorial assistance with a draft of this manuscript and Satista for support with statistical analysis. Conflict of interest The authors declare that they have no competing interests. Authors' contributions The study conception and design were performed by K.M. and T.I.. Data collection and analysis were performed by K.M.. The first draft of the manuscript was written by K.M., and all authors commented on the previous versions of the manuscript. Statistical analyses were performed by K.M.. Figure were prepared and modified by K.M. and T.I.. All authors read and approved the final manuscript. Funding This study did not receive any fund. Data availability statements The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and institutional data sharing policies, but are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was approved by the Institutional Review Board of Kanagawa Cancer Center (24 Eki, 54), with a waiver for written informed consent. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Demographics, safety and quality, and prognostic information in both the seventh and eighth editions of the TNM classification in 18,973 surgical cases of the Japanese joint committee of lung cancer registry database in 2010. J Thorac Oncol. 2019;14(2):212-22. doi: 10.1016/j.jtho.2018.10.002. Chansky K, Detterbeck FC, Nicholson AG, Rusch VW, Vallières E, Groome P, et al. The IASLC lung cancer staging project: external validation of the revision of the TNM stage groupings in the eighth edition of the TNM classification of lung cancer. J Thorac Oncol. 2017;12(7):1109-21. doi: 10.1016/j.jtho.2017.04.011. Forde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, et al. Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer. N Engl J Med. 2022;386(21):1973-85. doi: 10.1056/NEJMoa2202170. Cascone T, Leung CH, Weissferdt A, Pataer A, Carter BW, Godoy MC, et al. Neoadjuvant chemotherapy plus nivolumab with or without ipilimumab in operable non-small cell lung cancer: the phase 2 platform NEOSTAR trial. Nat Med. 2023;29(3):593-604. doi: 10.1038/s41591-022-02189-0. Schuurbiers OC, Meijer TW, Kaanders JH, Looijen-Salamon MG, De Geus-Oei LF, Van Der Drift MA, et al. Glucose metabolism in NSCLC is histology-specific and diverges the prognostic potential of 18FDG-PET for adenocarcinoma and squamous cell carcinoma. J Thorac Oncol. 2014;9(10):1485-93. doi: 10.1097/JTO.0000000000000286. Nakayama H, Okumura S, Daisaki H, Kato Y, Uehara H, Adachi S, et al. Value of integrated positron emission tomography revised using a phantom study to evaluate malignancy grade of lung adenocarcinoma: a multicenter study. Cancer. 2010;116(13):3170-7. doi: 10.1002/cncr.25244. Malapelle U, Sirera R, Jantus-Lewintre E, Reclusa P, Calabuig-Fariñas S, Blasco A et al. Profile of the Roche cobas® EGFR mutation test v2 for non-small cell lung cancer. Expert Rev Mol Diagn. 2017;17(3):209-15. doi: 10.1080/14737159.2017.1288568. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JH, Beasley MB, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10(9):1243-60. doi: 10.1097/JTO.0000000000000630. Sakao Y, Miyamoto H, Oh S, Takahashi N, Inagaki T, Miyasaka Y, et al. The impact of cigarette smoking on prognosis in small adenocarcinomas of the lung: the association between histologic subtype and smoking status. J Thorac Oncol. 2008;3(9):958-62. doi: 10.1097/JTO.0b013e31818396e0. Longato E, Vettoretti M, Di Camillo B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform. 2020;108:103496. doi: 10.1016/j.jbi.2020.103496. Nordquist LT, Simon GR, Cantor A, Alberts WM, Bepler G. Improved survival in never-smokers vs current smokers with primary adenocarcinoma of the lung. Chest. 2004;126(2):347-51. doi: 10.1378/chest.126.2.347 15302716. Molina R, Augé JM, Bosch X, Escudero JM, Viñolas N, Marrades R, et al. Usefulness of serum tumor markers, including progastrin-releasing peptide, in patients with lung cancer: correlation with histology. Tumour Biol. 2009;30(3):121-9. doi: 10.1159/000224628. Okada M, Nishio W, Sakamoto T, Uchino K, Yuki T, Nakagawa A, et al. Prognostic significance of perioperative serum carcinoembryonic antigen in non-small cell lung cancer: analysis of 1,000 consecutive resections for clinical stage I disease. Ann Thorac Surg. 2004;78(1):216-21. doi: 10.1016/j.athoracsur.2004.02.009. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7173846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489865565,"identity":"3e506f78-bf70-4a5b-bbbb-07d599627ca6","order_by":0,"name":"Kotaro Murakami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACCRiDjb35+IcPDAwJMD4+LYwNIAYfz7E0xhkkaZGTyDFj5kFowQ0k23vMH/zcY8PAJpGW9ti2zS6Pn72BEehCvjxcWqR5zhg29jxLY2DjeXzcOLctuViy5wCz5AwGtmJcWoDuMWzgOXAY6P20BOncNubEDTcS2IAuZEtswKOl8Q9IC0OOgbRlWz1hLdJALc1gWzhyzKQZ2w4T1iLZc6xwtswBkF+OJRv2nDueOLPnYLPkDAPcfpE43rzh45sDNgzy7c0HH/woq07sZ28++OFDxTGcIQYD9WBnMIIjEBRRBscSCGmBgj9wVg2xWkbBKBgFo2D4AwAAulV8mDBemAAAAABJRU5ErkJggg==","orcid":"","institution":"Kanagawa Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Kotaro","middleName":"","lastName":"Murakami","suffix":""},{"id":489865567,"identity":"38ded657-2719-4d6a-babe-3b866f9daa54","order_by":1,"name":"Tetsuya Isaka","email":"","orcid":"","institution":"Kanagawa Cancer 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Center","correspondingAuthor":false,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Shigefuku","suffix":""},{"id":489865571,"identity":"0daf1efe-7f93-4138-ba28-c62fa7ed7f85","order_by":5,"name":"Noritake Kikunishi","email":"","orcid":"","institution":"Kanagawa Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Noritake","middleName":"","lastName":"Kikunishi","suffix":""},{"id":489865572,"identity":"54ae4b5e-c2c2-4a8d-96e7-cf9ae586817c","order_by":6,"name":"Naoko Shigeta","email":"","orcid":"","institution":"Kanagawa Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Naoko","middleName":"","lastName":"Shigeta","suffix":""},{"id":489865573,"identity":"a55e0ecf-6607-42db-838f-81fe97d29106","order_by":7,"name":"Yujin Kudo","email":"","orcid":"","institution":"Tokyo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujin","middleName":"","lastName":"Kudo","suffix":""},{"id":489865574,"identity":"a6b9c021-89c3-4f28-840a-26e6be7f44bd","order_by":8,"name":"Yoshihiro Miyata","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Miyata","suffix":""},{"id":489865575,"identity":"f1357906-db36-4d99-967f-87204fa78390","order_by":9,"name":"Morihito Okada","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Morihito","middleName":"","lastName":"Okada","suffix":""},{"id":489865576,"identity":"a841bb67-fb85-4023-a84e-cd2eda14218f","order_by":10,"name":"Norihiko Ikeda","email":"","orcid":"","institution":"Tokyo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Norihiko","middleName":"","lastName":"Ikeda","suffix":""},{"id":489865577,"identity":"b2459698-8a47-4d72-bd47-2a85d2c185f1","order_by":11,"name":"Hiroyuki Ito","email":"","orcid":"","institution":"Kanagawa Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Ito","suffix":""}],"badges":[],"createdAt":"2025-07-21 06:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7173846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7173846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87720591,"identity":"fe6ce12e-d13f-4a9d-b3bf-880c10a230e0","added_by":"auto","created_at":"2025-07-28 09:48:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the study design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-7173846/v1/2d2bce20e4cf1ed99734c7c4.png"},{"id":87721956,"identity":"9b032457-f18f-467e-8c1d-195315424500","added_by":"auto","created_at":"2025-07-28 09:56:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. Kaplan–Meier curves of relapse-free survival (RFS) in patients with clinical stage IB lung adenocarcinoma stratified according to the prognostic scoring system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. Kaplan–Meier curves of overall survival (OS) in patients with clinical stage IB lung adenocarcinoma based on the prognostic scoring system.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-7173846/v1/9066e4c34a16e26ab4342d70.png"},{"id":89457774,"identity":"41b92826-5b8b-4b98-adca-83a7bf722aa6","added_by":"auto","created_at":"2025-08-20 07:17:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7173846/v1/579b815c-9668-4b4b-b79d-a6ba1afdc235.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Preoperative Risk Scoring System for Survival Prediction in Clinical Stage IB Lung Adenocarcinoma: A Multicenter Study","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer remains a leading cause of morbidity and mortality worldwide. Among lung cancer cases, over 60% of patients are diagnosed with lung adenocarcinoma (LUAD), the most common histological subtype, particularly in Japan [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) staging system is the only established tool for stratifying recurrence risk in lung cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recently, ground-glass opacity (GGO) components and the maximum standardized uptake value (SUVmax) on positron emission tomography (PET) have emerged as important prognostic factors in clinical stage (c-stage) I LUAD [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFollowing the TNM classification update to the 8th edition, several studies have explored preoperative prognostic factors in c-stage IA non-small cell lung cancer (NSCLC). Hattori et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that the presence of a GGO component in stage IA NSCLC is associated with favorable prognosis. Similarly, a large meta-analysis of patients with c-stage IA NSCLC reported that part-solid tumors had a better prognosis than pure-solid tumors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additional studies have also identified PET imaging as a significant prognostic tool in this population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While numerous studies have investigated prognostic factors in c-stage IA NSCLC, the prognostic relevance of GGO components and PET metrics in c-stage IB NSCLC remains unclear. Moreover, to our knowledge, no study has specifically analyzed the prognostic significance of preoperative factors such as GGO components or SUVmax in c-stage IB NSCLC (TNM 8th edition), nor developed a risk stratification model for this subgroup.\u003c/p\u003e\u003cp\u003eLobectomy with lymph node dissection is the standard treatment for c-stage IB lung cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the 5-year overall survival (OS) rate for patients with c-stage IB (T2aN0M0) NSCLC remains unsatisfactory, ranging from 68–71.5% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In recent years, several trials have evaluated the efficacy of neoadjuvant chemotherapy and immunotherapy, showing some therapeutic benefit [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, these trials included only a limited number of patients with c-stage IB (TNM 8th edition), highlighting the need for further studies to identify eligible candidates. Notably, a subset of patients with c-stage IB may benefit from preoperative treatment. Identifying and scoring prognostic factors could enable better risk stratification, guiding clinical decision-making, such as optimizing treatment selection and identifying candidates for induction therapy. Preoperative risk stratification may help tailor individualized treatment strategies to improve patient outcomes.\u003c/p\u003e\u003cp\u003eIn this study, we included GGO components and SUVmax, which have been established as preoperative prognostic factors for c-stage IA, to assess their prognostic significance in c-stage IB LUAD. However, their relevance in this subgroup remains unclear. Importantly, GGO components are specific to LUAD, whereas non-adenocarcinomas typically present as pure-solid tumors. Furthermore, SUVmax has been reported to be significantly higher in non-LUADs compared to LUADs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, the present study focused exclusively on patients with c-stage IB LUAD.\u003c/p\u003e\u003cp\u003eThis study aimed to identify preoperative prognostic factors for c-stage IB LUAD and develop a simple scoring tool for preoperative risk prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003cp\u003e A retrospective chart review was performed using our prospectively maintained database to identify patients who underwent surgical resection for primary LUAD at Kanagawa Cancer Center, Tokyo Medical University, and Hiroshima University Hospital between January 2010 and December 2020. The study was approved by the Institutional Review Board of Kanagawa Cancer Center (24 Eki, 54), with a waiver for written informed consent.\u003c/p\u003e\u003cp\u003eMedical record data were updated as of May 2023 and extracted based on clinicopathological features and treatment histories. Patients without documented clinical or radiographic disease progression were censored at the last follow-up visit. Those with non-adenocarcinoma or those who underwent sublobar resection (wedge resection or segmentectomy) were excluded. All included patients underwent preoperative high-resolution computed tomography (CT) and fluorodeoxyglucose-PET/CT (FDG-PET/CT). Disease staging was determined according to the 8th edition of the UICC TNM classification for lung and pleural tumors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Complete resection was defined as no evidence of residual cancer, either macroscopically or microscopically.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Follow-Up\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollow-up evaluations included physical examination, chest radiography, chest CT, and blood tests, including relevant tumor markers. Follow-up chest CT scans were performed every 6–12 months. If recurrence was suspected, additional diagnostic evaluations were conducted, including CT scans of the chest and abdomen, brain magnetic resonance imaging (MRI), and FDG-PET/CT.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFDG-PET/CT Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFDG-PET/CT scans were performed using one of the following integrated three-dimensional PET/CT scanners: Discovery MI (GE Healthcare, Little Chalfont, United Kingdom), Aquiduo (Toshiba Medical Systems Corporation, Tochigi, Japan), or Biograph Sensation 16 (Siemens Healthcare, Erlangen, Germany). The SUVmax values were standardized across the three institutions using the method proposed by Nakayama et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Radiologists at each institution independently determined the original SUVmax values.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePathological Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll collected surgical specimens were fixed in 10% formalin and embedded in paraffin DNA was analyzed for epidermal growth factor receptor (EGFR) mutations using the Cobas® EGFR Mutation Test version 2 (Cobas; Roche Diagnostics, Basel, Switzerland) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tumor differentiation was classified as well differentiated (minimally invasive adenocarcinoma or lepidic), moderately differentiated (acinar or papillary), or poorly differentiated (solid or micropapillary) adenocarcinoma [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRelapse-free survival (RFS) and OS were estimated using the Kaplan–Meier method, with differences in survival rates assessed by log-rank tests. RFS was defined as the time from surgery to recurrence or death from any cause, while OS was defined as the time from surgery to death or last follow-up for censored patients (those without adverse events during the last observation period). Cutoff values for variables were determined using receiver operating characteristic (ROC) curve analysis with RFS as the outcome, except for age (set at 75 years, the standard for the late elderly population) and serum carcinoembryonic antigen (CEA) (set at 5 ng/mL, the upper limit of normal). Survival curves were constructed using the Kaplan–Meier method.\u003c/p\u003e\u003cp\u003eUnivariate and multivariate analyses were performed using a Cox proportional hazards regression model, incorporating age, sex, smoking history, serum CEA level, CT tumor consolidation size, presence of pure solid tumors on imaging, and SUVmax value. Smoking history was included due to its reported association with cancer grade [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Multivariate analysis was performed using the backward elimination method. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using EZR on R Commander, version 1.30 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDevelopment of the Scoring System\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA risk-scoring tool was developed to predict the prognosis of patients with c-stage IB LUAD. Each independent preoperative predictor was assigned a point value based on regression coefficients derived from the multivariate analysis. Patients were categorized into four risk groups: low (score 0), moderate (score 1), moderately high (score 2), and extremely high (score 3). The accuracy of the scoring system was evaluated using ROC analysis and the concordance index (c-index) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4,670 consecutive patients with primary lung cancer underwent complete surgical resection. Among the 2,488 patients who underwent lobectomy, 245 with c-stage IB disease were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median follow-up time for survivors was 45.0 months (range, 0.5\u0026ndash;133.3). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the patient characteristics. Among all patients, 55.1% had a history of smoking, and 31.8% of had an elevated preoperative serum CEA level. The median tumor SUVmax was 4.7 (range: 0.0\u0026ndash;34.7), with 36.3% of patients having pathological stage (p-stage) II\u0026ndash;III disease. Multivariable analysis identified elevated CEA levels (hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.46\u0026ndash;3.51; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SUVmax (HR, 4.60; 95% CI, 2.48\u0026ndash;8.55; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent predictors of RFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For OS, significant factors included smoking history (HR, 2.68; 95% CI, 1.13\u0026ndash;6.33; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), elevated CEA levels (HR, 2.89; 95% CI, 1.42\u0026ndash;5.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and SUVmax (HR, 2.84; 95% CI, 1.16\u0026ndash;6.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cutoff value for SUVmax was determined to be 3.7 (area under the curve [AUC], 0.713; 95% CI, 0.648\u0026ndash;0.779) based on ROC curve analysis with RFS as the outcome.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient Characteristics (n\u0026thinsp;=\u0026thinsp;245)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\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\u003eTotal (n\u0026thinsp;=\u0026thinsp;245)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age, years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0 (34\u0026ndash;93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (56.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history, yes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (55.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA level, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e≦\u0026thinsp;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167 (68.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (31.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian CT tumor size, cm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.6 (3.04\u0026ndash;8.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian CT tumor consolidation size, cm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4 (3.04\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiological tumor component, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epure solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (71.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epart solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (28.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian SUVmax (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7 (0.0\u0026ndash;34.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative and pathological findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor differentiation, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (15.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emoderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (67.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian pathological invasive tumor size, cm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1 (0.2\u0026ndash;12.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphatic permeation, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (32.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular invasion, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (45.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral pleural invasion, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (36.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodal metastasis, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (78.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (13.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (9.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage in the eighth edition, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (30.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (33.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII-III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (36.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGFR mutation, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (55.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewild type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (44.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrence, yes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (28.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCause of death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elung cancer recurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother causes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eCEA, carcinoembryonic antigen; CT, computed tomography; EGFR, epidermal growth factor receptor; SUVmax, maximum standardized uptake value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariable and multivariable analyses for relapse-free survival in all patients with clinical stage IB adenocarcinoma.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariable analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaw\u003c/p\u003e\u003cp\u003ecoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u0026ge;\u0026thinsp;75 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.41 (0.91\u0026ndash;2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25 (0.80\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history, yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25 (0.80\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA level, \u0026gt;\u0026thinsp;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.67 (1.73\u0026ndash;4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.26 (1.46\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT tumor consolidation size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.19 (0.57\u0026ndash;2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiological tumor component,\u003c/p\u003e\u003cp\u003epure solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.57 (0.93\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmax, \u0026ge;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.10 (2.76\u0026ndash;9.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.60 (2.48\u0026ndash;8.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value; CI: confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariable and multivariable analyses for overall survival in all patients with clinical stage IB adenocarcinoma.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariable analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaw\u003c/p\u003e\u003cp\u003ecoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted\u003c/p\u003e\u003cp\u003ecoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u0026ge;\u0026thinsp;75 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.84 (0.94\u0026ndash;3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.56 (1.15\u0026ndash;5.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history, yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.46 (1.50\u0026ndash;7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.68 (1.13\u0026ndash;6.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA level, \u0026gt;\u0026thinsp;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.92 (1.97\u0026ndash;7.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.89 (1.42\u0026ndash;5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT tumor consolidation size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.72 (0.88\u0026ndash;8.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiological tumor component,\u003c/p\u003e\u003cp\u003epure solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.15 (0.54\u0026ndash;2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmax, \u0026ge;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.57 (1.48\u0026ndash;8.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.84 (1.16\u0026ndash;6.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value; CI: confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA risk-scoring system was developed based on independent preoperative predictors of OS identified in the multivariate analysis. The regression coefficients (adjusted coefficients) from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were rounded to the nearest integer to assign point values to each predictor. The scoring system stratified patients into four risk groups: score 0 (n\u0026thinsp;=\u0026thinsp;41), score 1 (n\u0026thinsp;=\u0026thinsp;84), score 2 (n\u0026thinsp;=\u0026thinsp;77), and score 3 (n\u0026thinsp;=\u0026thinsp;43). Kaplan\u0026ndash;Meier survival curves demonstrated a significant decline in both RFS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) as the risk score increased. The 5-year OS rates were 100.0%, 89.3%, 74.0%, and 52.1% for risk scores of 0, 1, 2, and 3, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes patient characteristics stratified by risk score. Patients with a risk score of 0 had significantly better tumor differentiation (36.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a smaller median pathological invasive tumor size (2.1 cm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower rates of vascular invasion (14.6%) and visceral pleural invasion (VPI) (4.9%) (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Only one patient (2.4%) in this group had pathological lymph node metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Furthermore, in this group, the median SUVmax was 2.0, and 61.0% of the patients were p-stage IA. In contrast, patients with a risk score of 3 exhibited larger pathological invasive tumor size (median value: 3.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher SUVmax (median value: 10.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more frequent vascular invasion (65.1%) and VPI (51.2%; both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 53.5% of patients having p-stage II\u0026ndash;III disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics stratified by the prognostic scoring system.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables (n\u0026thinsp;=\u0026thinsp;245)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScore 0\u003c/p\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScore 1\u003c/p\u003e\u003cp\u003eModerate\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScore 2\u003c/p\u003e\u003cp\u003eModerately high (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eScore 3\u003c/p\u003e\u003cp\u003eExtremely high (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003ePreoperative findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age, years (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0 (47\u0026ndash;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.5 (43\u0026ndash;93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.0 (47\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.0 (34\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (52.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (81.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history, yes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (72.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA level, \u0026ge;\u0026thinsp;5 ng/mL (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian CT tumor size, cm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.7 (3.1\u0026ndash;5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5 (3.1\u0026ndash;6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4 (3.04\u0026ndash;5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7 (3.1\u0026ndash;8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian CT tumor consolidation size,\u003c/p\u003e\u003cp\u003ecm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.44 (3.06\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3 (3.04\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4 (3.04\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7 (3.1\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiological tumor component, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epure solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (61.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (90.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epart solid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian SUVmax (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0 (0.75\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5 (0.0\u0026ndash;20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.5 (1.7\u0026ndash;30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.1 (3.7\u0026ndash;34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative and pathological findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor differentiation, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emoderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (67.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian pathological invasive tumor size,\u003c/p\u003e\u003cp\u003ecm (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1 (0.4\u0026ndash;4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9 (0.2\u0026ndash;6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (1.2\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (0.8\u0026ndash;12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphatic permeation, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular invasion, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral pleural invasion, positive (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodal metastasis, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage in the 8th edition, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII-III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrence, yes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCause of death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother causes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCEA: carcinoembryonic antigen; CT: computed tomography; SUVmax: maximum standardized uptake value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis proposed preoperative prognostic scoring system demonstrated favorable predictive performance, based on the ROC curve (AUC, 0.738; 95% CI, 0.661\u0026ndash;0.815) and c-index (AUC, 0.753; 95% CI, 0.682\u0026ndash;0.824).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify preoperative prognostic factors in c-stage IB LUAD and develop a simple, yet effective scoring system for preoperative prognostic prediction. In addition to SUVmax, smoking history and elevated CEA levels were identified as significant preoperative factors for prognosis. Interestingly, while the presence of GGO components is a well-established favorable prognostic factor in c-stage IA, it was not associated with prognosis in this cohort of patients with c-stage IB. By incorporating smoking history, elevated CEA levels, and SUVmax, we developed a scoring system that effectively stratified postoperative outcomes. The validity of this scoring system was confirmed, highlighting the heterogeneous nature of c-stage IB LUAD and the challenges in accurately predicting patient outcomes. To our knowledge, this is the first study to evaluate preoperative prognostic factors and propose a scoring system specifically designed for c-stage IB LUAD (TNM 8th edition) following complete resection.\u003c/p\u003e\u003cp\u003eThe relationship between tobacco smoking and lung cancer development is well-established, with recent studies showing that smokers have a significantly poorer prognosis than non-smokers, particularly in early-stage LUAD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Sakao et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported that cigarette smoking is associated with the carcinogenesis of moderately to poorly differentiated LUAD, including papillary, acinar, or solid component subtypes. CEA is widely recognized as a valuable biomarker for diagnosing and monitoring lung cancer prognosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A retrospective study found that elevated CEA levels are associated with poorer survival outcomes and serve as a risk factor for occult regional lymph node metastasis in patients with stage I NSCLC undergoing surgical resection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, previous studies have shown that preoperative SUVmax in the primary tumor of patients with c-stage I disease is associated with disease-free survival and OS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this study, as in previous reports, preoperative smoking history, elevated CEA levels, and high SUVmax were significant prognostic factors. Therefore, incorporating these factors into a scoring system provides a practical and effective tool for predicting prognosis in c-stage IB LUAD. Indeed, patients classified into higher risk groups by our scoring system exhibited larger pathological invasive tumor sizes and higher rates of poor tumor differentiation, vascular invasion, VPI, and lymph node metastasis.\u003c/p\u003e\u003cp\u003eAdditionally, while the presence of a GGO component is a well-established favorable prognostic factor in c-stage IA, it does not significantly influence prognosis in c-stage IB disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Aokage et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] reported that approximately 20% of c-stage IB LUAD cases with a GGO component are classified as invasive solid-predominant adenocarcinoma and that the presence of the GGO component was not attributable to the prognosis of this cancer subtype. This may be because, at this stage, the increasing tumor diameter and associated malignant progression outweigh the potential prognostic benefits of a GGO component.\u003c/p\u003e\u003cp\u003ePreviously reported 5-year OS rates for patients with c-stage IB (T2aN0M0) NSCLC range from 68\u0026ndash;71.5% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While the previous studies did not stratify prognosis based on preoperative factors, our study successfully demonstrated that preoperative factors, such as smoking history, CEA levels, and SUVmax values, effectively stratify postoperative outcomes in c-stage IB LUAD. Notably, patients with a risk score of 0 had a 5-year OS rate of 100%, comparable to that of c-stage IA1 reported in previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Importantly, none of these patients (with a score of 0 on the present scoring system) showed pathologically poor tumor differentiation, and they exhibited lower frequencies of lymphatic permeation, vascular invasion, and VPI (17.1%, 14.6%, and 4.9%, respectively). These results suggest that this scoring system is effective in discriminating low-grade tumors in patients with c-stage IB LUAD. Furthermore, only one patient (2.4%) in this group had lymph node metastasis, supporting the possibility of reduced lymph node dissection. In contrast, patients with a risk score of 3 had significantly worse outcomes, with a 5-year OS rate of 52.1%, equivalent to c-stage IIB\u0026ndash;IIIA reported in previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This high-risk group was characterized by greater pathological invasive tumor size, more frequent vascular invasion, VPI, and a higher proportion (53.5%) of p-stage II\u0026ndash;III disease. Considering their poor prognosis, these patients could potentially benefit from neoadjuvant therapy. Future prospective multicenter validation of this preoperative risk scoring system is needed to explore strategies to improve survival outcomes in extremely high-risk c-stage IB LUAD under the current TNM classification (8th edition).\u003c/p\u003e\u003cp\u003eIn summary, this study effectively stratified the pathological grade and prognosis of c-stage IB LUAD based on preoperative factors, highlighting its heterogeneous nature. Incorporating this stratification into ongoing discussions regarding perioperative treatment, including preoperative induction therapy, may help refine clinical decision-making.\u003c/p\u003e\u003cp\u003eThe limitations of this study include its retrospective nature, which introduces potential bias. The statistical analyses may not have been sufficiently robust to identify certain factors, such as sex, as significant. In addition, the long duration of the study may have contributed to substantial sample heterogeneity. Although the cutoff values for SUVmax were calculated using ROC curves and efforts were made to harmonize SUVmax values by correcting inter-facility errors, discrepancies in SUVmax measurements between institutions may still be a concern. Moreover, no standardized surveillance protocol was established across the three participating institutions, either postoperatively or at recurrence, which could have affected the consistency of data collection and outcomes. Due to the small sample size, this scoring system could not be validated with test data. Further prospective studies with larger, multi-institutional cohorts are needed to validate our findings. Additionally, several other limitations should be acknowledged. First, due to the multicenter nature of this study, detailed quantification of smoking exposure, such as pack-years, could not be uniformly assessed in all patients. As a result, smoking intensity was not rigorously evaluated, and only a binary classification (smoker vs. non-smoker) was adopted. Second, EGFR mutation testing was not performed in all patients. Consequently, EGFR status was excluded from the multivariate analysis, even though it may influence treatment decisions, particularly regarding preoperative therapy. Moreover, the EGFR mutation status was determined by preoperative biopsy in some cases and by surgical specimens in others, which could introduce variability. Future analyses incorporating molecular data are required. Third, although GGO was included as a categorical variable (part-solid vs. pure-solid), the consolidation-to-tumor ratio, a quantitative index used in other studies, was excluded due to potential inter-institutional variability in measurement. Despite these limitations, this study successfully identified preoperative prognostic factors in c-stage IB LUAD, marking an important step toward improving preoperative evaluation and treatment strategies for these cases.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that smoking history, serum CEA level, and tumor SUVmax are critical determinants of prognosis in patients with c-stage IB LUAD, regardless of solid component size or the presence of a GGO component. The scoring system developed using these preoperative factors, in combination with the TNM classification of lung cancer, may enhance prognostic accuracy and aid in clinical decision-making for patients with c-stage IB LUAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecarcinoembryonic antigen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecomputed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEGFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eepidermal growth factor receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDG-PET/CT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efluorodeoxyglucose-PET/computed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eground-glass opacity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-resolution computed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elung adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emagnetic resonance imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epositron emission tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelapse-free survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSUVmax\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emaximum standardized uptake value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUICC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnion for International Cancer Control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evisceral pleural invasion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Editage for their editorial assistance with a draft of this manuscript and Satista for support with statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study conception and design were performed by K.M. and T.I.. Data collection and analysis were performed by K.M.. The first draft of the manuscript was written by K.M., and all authors commented on the previous versions of the manuscript. Statistical analyses were performed by K.M.. Figure were prepared and modified by K.M. and T.I.. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and institutional data sharing policies, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board of Kanagawa Cancer Center (24 Eki, 54), with a waiver for written informed consent. This study was conducted in accordance with the principles of the Declaration of Helsinki.\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"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsamura H, Goya T, Koshiishi Y, Sohara Y, Eguchi K, Mori M, et al. A Japanese Lung Cancer Registry study: prognosis of 13,010 resected lung cancers. J Thorac Oncol. 2008;3(1):46-52. doi: 10.1097/JTO.0b013e31815e8577.\u003c/li\u003e\n\u003cli\u003eGoldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC lung Cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung Cancer. J Thorac Oncol. 2016;11(1):39-51. doi: 10.1016/j.jtho.2015.09.009.\u003c/li\u003e\n\u003cli\u003eAokage K, Miyoshi T, Ishii G, Kusumoto M, Nomura S, Katsumata S, et al. Influence of ground glass opacity and the corresponding pathological findings on survival in patients with clinical stage I non-small cell lung cancer. J Thorac Oncol. 2018;13(4):533-42. doi: 10.1016/j.jtho.2017.11.129.\u003c/li\u003e\n\u003cli\u003eKim H, Goo JM, Paeng JC, Kim YT, Park CM. Evaluation of maximum standardized uptake value at fluorine-18 fluorodeoxyglucose positron emission tomography as a complementary T factor in the eighth edition of lung cancer stage classification. Lung Cancer. 2019;134:151-7. doi: 10.1016/j.lungcan.2019.06.013.\u003c/li\u003e\n\u003cli\u003eKwon W, Howard BA, Herndon JE, Patz EF Jr. FDG uptake on positron emission tomography correlates with survival and time to recurrence in patients with Stage I non-small-cell lung cancer. J Thorac Oncol. 2015;10(6):897-902. doi: 10.1097/JTO.0000000000000534.\u003c/li\u003e\n\u003cli\u003eHattori A, Suzuki K, Takamochi K, Wakabayashi M, Aokage K, Saji H, et al. Prognostic impact of a ground-glass opacity component in clinical stage IA non-small cell lung cancer. J Thorac Cardiovasc Surg. 2021;161(4):1469-80. doi: 10.1016/j.jtcvs.2020.01.107.\u003c/li\u003e\n\u003cli\u003eJiang T, Li M, Lin M, Zhao M, Zhan C, Feng M. Meta-analysis of comparing part-solid and pure-solid tumors in patients with clinical stage IA non-small-cell lung cancer in the eighth edition TNM classification. Cancer Manag Res. 2019;11:2951-61. doi: 10.2147/CMAR.S196613.\u003c/li\u003e\n\u003cli\u003eChou HP, Lin KH, Huang HK, Lin LF, Chen YY, Wu TH, et al. Prognostic value of positron emission tomography in resected stage IA non-small cell lung cancer. Eur Radiol. 2021;31(10):8021-9. doi: 10.1007/s00330-021-07801-4.\u003c/li\u003e\n\u003cli\u003eNakao M, Terauchi T, Oikado K, Sato Y, Hashimoto K, Ichinose J, et al. Distinct prognostic impact of PET findings based on radiological appearance in clinical stage IA lung adenocarcinoma. Clin Lung Cancer. 2023;24(2):107-13. doi: 10.1016/j.cllc.2022.10.007.\u003c/li\u003e\n\u003cli\u003eGinsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. Ann Thorac Surg. 1995;60(3):615-22; discussion 622. doi: 10.1016/0003-4975(95)00537-u.\u003c/li\u003e\n\u003cli\u003eOkami J, Shintani Y, Okumura M, Ito H, Ohtsuka T, Toyooka S, et al. Demographics, safety and quality, and prognostic information in both the seventh and eighth editions of the TNM classification in 18,973 surgical cases of the Japanese joint committee of lung cancer registry database in 2010. J Thorac Oncol. 2019;14(2):212-22. doi: 10.1016/j.jtho.2018.10.002.\u003c/li\u003e\n\u003cli\u003eChansky K, Detterbeck FC, Nicholson AG, Rusch VW, Valli\u0026egrave;res E, Groome P, et al. The IASLC lung cancer staging project: external validation of the revision of the TNM stage groupings in the eighth edition of the TNM classification of lung cancer. J Thorac Oncol. 2017;12(7):1109-21. doi: 10.1016/j.jtho.2017.04.011.\u003c/li\u003e\n\u003cli\u003eForde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, et al. Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer. N Engl J Med. 2022;386(21):1973-85. doi: 10.1056/NEJMoa2202170.\u003c/li\u003e\n\u003cli\u003eCascone T, Leung CH, Weissferdt A, Pataer A, Carter BW, Godoy MC, et al. Neoadjuvant chemotherapy plus nivolumab with or without ipilimumab in operable non-small cell lung cancer: the phase 2 platform NEOSTAR trial. Nat Med. 2023;29(3):593-604. doi: 10.1038/s41591-022-02189-0.\u003c/li\u003e\n\u003cli\u003eSchuurbiers OC, Meijer TW, Kaanders JH, Looijen-Salamon MG, De Geus-Oei LF, Van Der Drift MA, et al. Glucose metabolism in NSCLC is histology-specific and diverges the prognostic potential of 18FDG-PET for adenocarcinoma and squamous cell carcinoma. J Thorac Oncol. 2014;9(10):1485-93. doi: 10.1097/JTO.0000000000000286.\u003c/li\u003e\n\u003cli\u003eNakayama H, Okumura S, Daisaki H, Kato Y, Uehara H, Adachi S, et al. Value of integrated positron emission tomography revised using a phantom study to evaluate malignancy grade of lung adenocarcinoma: a multicenter study. Cancer. 2010;116(13):3170-7. doi: 10.1002/cncr.25244.\u003c/li\u003e\n\u003cli\u003eMalapelle U, Sirera R, Jantus-Lewintre E, Reclusa P, Calabuig-Fari\u0026ntilde;as S, Blasco A et al. Profile of the Roche cobas\u0026reg; EGFR mutation test v2 for non-small cell lung cancer. Expert Rev Mol Diagn. 2017;17(3):209-15. doi: 10.1080/14737159.2017.1288568.\u003c/li\u003e\n\u003cli\u003eTravis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JH, Beasley MB, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10(9):1243-60. doi: 10.1097/JTO.0000000000000630.\u003c/li\u003e\n\u003cli\u003eSakao Y, Miyamoto H, Oh S, Takahashi N, Inagaki T, Miyasaka Y, et al. The impact of cigarette smoking on prognosis in small adenocarcinomas of the lung: the association between histologic subtype and smoking status. J Thorac Oncol. 2008;3(9):958-62. doi: 10.1097/JTO.0b013e31818396e0.\u003c/li\u003e\n\u003cli\u003eLongato E, Vettoretti M, Di Camillo B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform. 2020;108:103496. doi: 10.1016/j.jbi.2020.103496.\u003c/li\u003e\n\u003cli\u003eNordquist LT, Simon GR, Cantor A, Alberts WM, Bepler G. Improved survival in never-smokers vs current smokers with primary adenocarcinoma of the lung. Chest. 2004;126(2):347-51. doi: 10.1378/chest.126.2.347 15302716.\u003c/li\u003e\n\u003cli\u003eMolina R, Aug\u0026eacute; JM, Bosch X, Escudero JM, Vi\u0026ntilde;olas N, Marrades R, et al. Usefulness of serum tumor markers, including progastrin-releasing peptide, in patients with lung cancer: correlation with histology. Tumour Biol. 2009;30(3):121-9. doi: 10.1159/000224628.\u003c/li\u003e\n\u003cli\u003eOkada M, Nishio W, Sakamoto T, Uchino K, Yuki T, Nakagawa A, et al. Prognostic significance of perioperative serum carcinoembryonic antigen in non-small cell lung cancer: analysis of 1,000 consecutive resections for clinical stage I disease. Ann Thorac Surg. 2004;78(1):216-21. doi: 10.1016/j.athoracsur.2004.02.009.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, Clinical stage IB, Overall survival, Scoring system, Preoperative risk","lastPublishedDoi":"10.21203/rs.3.rs-7173846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7173846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eClinical stage (c-stage) IB lung adenocarcinoma (LUAD) presents variable survival outcomes, and the prognostic significance of factors such as ground-glass opacity components and positron emission tomography (PET) metrics remains unclear. Despite recent advances, no preoperative scoring model has been established to stratify risk in this subgroup. We aimed to identify preoperative prognostic factors in c-stage IB LUAD and develop a simple scoring system for predicting overall survival (OS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed data from 245 patients with c-stage IB LUAD who underwent lobectomy at three institutions between 2010 and 2020. Cox regression analysis was performed to identify independent preoperative prognostic factors for OS. A risk score was developed by assigning points to each factor based on the regression coefficients. Patients were then stratified into four risk groups based on the total score.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultivariate analysis identified smoking history (regression coefficient: 0.98; hazard ratio [HR]: 2.68; 95% confidence interval [CI]: 1.13\u0026ndash;6.33; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), elevated serum carcinoembryonic antigen (CEA) levels (regression coefficient: 1.06; HR: 2.89; 95%CI: 1.42\u0026ndash;5.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and high maximum standardized uptake value (SUVmax) on PET (regression coefficient: 1.04; HR: 2.84; 95%CI: 1.16\u0026ndash;6.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) as independent factors of poor prognosis. A scoring system was established by assigning one point to each factor. Patients were stratified into four risk groups: low (score 0, n\u0026thinsp;=\u0026thinsp;41), moderate (score 1, n\u0026thinsp;=\u0026thinsp;84), moderately high (score 2, n\u0026thinsp;=\u0026thinsp;77), and extremely high (score 3, n\u0026thinsp;=\u0026thinsp;43). Five-year OS rates were 100.0%, 89.3%, 74.0%, and 52.1%, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prognostic model demonstrated good predictive performance (area under the curve [AUC], 0.738; 95%CI, 0.661\u0026ndash;0.815) and concordance index (AUC, 0.753; 95%CI, 0.682\u0026ndash;0.824). Notably, patients with a score of 0 showed low-grade tumors and favorable prognosis, whereas those with a score of 3 had more aggressive pathological characteristics and significantly worse outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe developed and validated a simple preoperative scoring system using smoking history, serum CEA level, and tumor SUVmax to predict prognosis in c-stage IB LUAD. This model provides a practical tool for risk stratification and may support individualized treatment decisions, including the consideration of induction therapy in selected cases.\u003c/p\u003e","manuscriptTitle":"A Preoperative Risk Scoring System for Survival Prediction in Clinical Stage IB Lung Adenocarcinoma: A Multicenter Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 09:48:37","doi":"10.21203/rs.3.rs-7173846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86de9c2a-2ba0-4f02-92b1-321f6523cb78","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T07:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 09:48:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7173846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7173846","identity":"rs-7173846","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}