Preoperative pectoralis muscle index predicts recurrence and metastasis in early-stage non- small cell lung cancer patients

Preoperative pectoralis muscle index predicts recurrence and metastasis in early-stage non- small cell lung cancer patients | 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 Preoperative pectoralis muscle index predicts recurrence and metastasis in early-stage non- small cell lung cancer patients Zhihui Shi, Lin Wu, Dengke Jiang, Ruiling Yang, Rui Liao, Lizhu Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4661240/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Sarcopenia is a well-established prognostic factor in patients with malignancies, with the muscle index serving as a key parameter in evaluating sarcopenia. However, the relationship between the pectoralis muscle index (PMI) determined by preoperative computed tomography (CT) and recurrence-free survival (RFS), as well as distant metastasis-free survival (DMFS), remains unclear in patients with early-stage non-small cell lung cancer (NSCLC). Methods Consecutive patients who underwent curative-intent resection for stage I to IIIA NSCLC between 2013 and 2018 at a cancer center were retrospectively identified. The Cox proportional hazard model was employed to analyze the correlation between PMI and survival, with subgroup analyses conducted to explore potential heterogeneity among different subgroups. Finally, the relative influence of each parameter was compared using a gradient boosting model (GBM). Results A total of 2110 patients (median (IQR) age 59.00 (52.00, 66.00) years, 1125 (53.32%) males, median follow-up of 64.73 months) were evaluated. Kaplan-Meier survival analysis showed that the RFS rate, DMFS rate, lung metastasis-free survival (MFS) rate, liver MFS rate, brain MFS rate, bone MFS rate, and adrenal MFS rate of patients in the high PMI group were higher than those in the low PMI group, all with P < 0.001. In the multivariable analysis, low PMI is still associated with shorter RFS ( hazard ratio [HR] = 1.34, 95% confidence interval [CI]: (1.10, 1.62), P = 0.004), DMFS (HR = 1.35, 95% CI: (1.11, 1.65), P = 0.003), lung MFS (HR = 1.47, 95% CI (1.19, 1.81), P < 0.001) and bone MFS (HR = 1.38, 95% CI: (1.11, 1.73), P = 0.004). These associations were consistent in subgroup analysis of different gender, age, tumor stage, histologic type, and surgical approach group. Conclusions As an independent predictor of RFS and DMFS in patients with early-stage NSCLC, preoperative CT-based PMI may contribute to further refining the risk stratification of NSCLC. Non-small cell lung carcinoma Pectoralis muscles Body composition Recurrence Neoplasm metastasis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction According to 2020 data, lung cancer accounts for 18% of all cancer deaths worldwide, ranking first among all cancers[ 1 ]. As the main treatment for early-stage lung cancer, surgical resection can effectively prolong the survival period of patients. However, some patients still experience recurrence and metastasis after surgery, indicating a poor prognosis. Therefore, early and accurate prediction of the risk of recurrence and metastasis after lung cancer resection is crucial for the long-term survival of patients. Sarcopenia is commonly described as a progressive and generalized disorder of skeletal muscle, reflecting a decline in the patient's physical functioning[ 2 ]. In certain chronic diseases and tumors, such as colorectal cancer[ 3 ], breast cancer[ 4 ], pancreatic cancer[ 5 ], hepatocellular carcinoma[ 6 ], and cardiovascular disease (CVD)[ 7 ], the adverse prognostic effects of sarcopenia have been evaluated. The effect of sarcopenia on the survival of lung cancer patients has also been proven[ 8 ]. Computed tomography (CT) can accurately quantify information such as the morphology, distribution, and radiodensity of muscle tissue, and are considered an effective tool for assessing the quantity and quality of muscle in the human body[ 9 ]. Currently, images of the third lumbar spine (L3) obtained by CT scans are considered the gold standard for non-invasive muscle assessment[ 10 ]. Nevertheless, routine scanning protocols for lung cancer patients typically only include chest and upper abdominal examinations and do not include scans of L3[ 11 ]. For some patients with early-stage lung cancer, the method of assessing the muscular status of the body by skeletal muscle parameters at the L3 level is undoubtedly limited, while expanding the scanning range will exacerbate the financial burden and increase the radiation dose. Hence, the use of chest CT scans to obtain pectoralis muscle parameters may be the best option for assessing muscle status in lung cancer patients. Previously, certain studies have investigated the relationship between pectoralis muscle parameters derived from CT scans and the prognosis of lung cancer patients. But, these studies primarily concentrated on examining the correlation between pectoralis muscle index (PMI) or pectoralis muscle radiodensity (PMD) and overall survival [ 12 , 13 ], postoperative complications[ 14 ], and length of hospital stay[ 15 ], without delving into metastasis and recurrence. Our study aimed to evaluate the association between pectoralis muscle index (PMI) derived from CT scans before surgery and recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and metastasis-free survival at specific distant metastatic sites in patients with early-stage non-small cell lung cancer (NSCLC). 2. Methods 2.1 Ethics approval and informed consent The study was carried out in accordance with the Declaration of Helsinki (revised in 2013). It was approved by the ethical council of the cancer hospital(KYLX2022055, 20220512), and the requirement for informed consent was waived as the study was retrospective. To ensure confidentiality, all patient data collected from the survey were made anonymous. 2.2 Study sample We retrospectively collected consecutive patients who underwent curative surgical resection for stage I–IIIA NSCLC between January 2013 and December 2018 at a single tertiary cancer hospital. The following inclusion criteria were applied to determine eligibility: (a) patients with pathologic stage I–IIIA NSCLC; (b) patients at diagnosis were no less than 18 years old; (c) patients who underwent curative surgical resection for NSCLC. Exclusion criteria included: (a) the preoperative CT scan images were incomplete; (b) lost to follow-up; (c) neoadjuvant therapy; (d) history of malignancy; (e) the interval between the last chest CT examination before surgery and the operation exceeded 30 days. 2.3 Data collection Clinical data were collected from inpatient and outpatient records containing demographics (gender, age, height, weight, body mass index (BMI)), smoking histories, dates and types of surgery, tumor stages, histologic type (adenocarcinoma vs non-adenocarcinoma), adjuvant chemotherapy, presence of emphysema, pleural invasion and data of survival including recurrence and metastasis. Distant metastases are defined as recurrence of disease in distant organs and/or tissues confirmed by imaging studies or pathological examination of tissue samples. BMI (kg/m 2 ) was calculated by dividing weight (kg) with height squared (m 2 ). 2.4 Body composition analysis All patients’ preoperative CT scans were examined at first. Unenhanced CT images with an 8-mm slice thickness obtained by spiral 64-detector CT were used for muscle segmentation. The radiologists used Slice-OMatic Software (version 6.0) to manually segmented the area of the pectoralis muscle, including the pectoralis major and pectoralis minor muscle, on the unenhanced CT imaging at 4th thoracic vertebra (T4) level. According to previous studies, the HU thresholds were set from − 29 to + 150 for the pectoralis muscle [ 16 , 17 ]. Two radiologists with three years of diagnostic experience, who were blinded to the clinical data, independently measured the pectoralis muscle area. When the correlation coefficient between the measurements of these two radiologists was less than 0.90, a third radiologist with more than 13 years of diagnostic experience measured the pectoralis muscle area again, and the measurement result was considered final. Otherwise, the final result was the mean of the two measurements. The pectoralis muscle area (cm 2 ) was normalized for height (m) squared and reported as pectoralis muscle index (cm 2 /m 2 ). Since there is no standard cut-off values for PMI, we stratified patients' PMI using sex-specific X-tile (version 3.6.1) cut-off values. 2.5 Patient outcomes The outcomes of this study were the recurrence-free survival (RFS) and the distant metastasis-free survival (DMFS). Recurrence-free survival was defined as the time interval from surgery until the occurrence of the first recurrence/metastasis, death from any cause, or the date of the last follow-up. Distant metastasis-free survival was calculated from the date of surgery to the date of first distant metastasis or the date of last follow-up or the date of death from any cause, whichever came first. 2.6 Statistical analysis According to the results of the Kolmogorov-Smirnov test for a normal distribution, for normally distributed continuous variables, we defined means and standard deviations (SD) and performed independent two-sample t tests. For categorical variables, we analyzed the number and percentage of patients and used chi-square tests or Fisher’s exact test. Kaplan-Meier curves were generated and compared the differences between the groups using the log-rank test. Univariable and multivariable Cox regression analyses were performed to identify the independent prognostic factors for recurrence-free survival and metastasis-free survival. We used two models: model 1 adjusted for age, gender, BMI; model 2 further adjusted for smoking history, tumor stage, histologic type, surgical approach, emphysema, pleural invasion and adjuvant chemotherapy. Subgroup analysis was then performed after stratification according to age, gender, BMI, tumor stage, smoking history, histologic type, and surgical approach to explore the potential causes of heterogeneity, and used the R package “Forestploter” to generate forest plots for visualization of the results of subgroup analyses. Finally, a gradient boosting model (GBM) was used to compare the relative influence of each variable on the prognosis of patients. Data processing and analysis were conducted using the SPSS software (version 25.0) and R software (version 4.3.2). Confidence interval (CI) was set at 95%, and statistical significance was set at P < 0.05. 3. Results 3.1 Baseline characteristics of study sample Of 2811 patients with stage I–IIIA lung cancer and treated with curative surgical resection, we excluded 360 patients whose preoperative CT scan images were incomplete, 72 patients who were lost to follow-up, 6 patients with neoadjuvant therapy, 183 patients with a history of other malignancies and 80 patients whose interval between the last pre-surgery chest CT examination and the operation was more than 30 days (Supplementary Fig.S1). Finally, 2110 patients were included in this study. Among the 2110 patients included, 1125 (53.32%) were males and 985 (46.68%) were females, with a median (IQR) age 59.00 (52.00–66.00) years. The PMI was found to be higher in male patients compared to female patients, with median (IQR) PMI values of 11.68 (9.99, 13.74) cm2 /m2 for males and 8.84 (7.48, 10.51) cm2 /m2 for females (Table 1). Table 1 : Demographic characteristics of study sample. Variable Patients(n =2110) Age(y)a 59.00 (52.00, 66.00) Age category <65 years ≥65years 1482 (70.24) 628 (29.96) Gender Male Female 1125 (53.32) 985 (46.68) BMI(kg/m2 )a 22.27 (20.76, 24.61) BMI category <18.5kg/m2, underweight 18.5-25.0kg/m2, normal weight ≥25.0kg/m2, above normal weight 1566 (74.22) 110 (5.21) 434 (20.57) Smoking history Never smoker Current or former smoker Unknown 1265 (59.95) 837 (39.67) 8 (0.38) Tumor stage I stage II stage IIIA stage 1325 (62.80) 374 (17.73) 411 (19.48) Histologic type Adenocarcinoma Non-adenocarcinoma 1656 (78.48) 454 (21.52) Surgical approach Thoracotomy Thoracosocope 762 (36. 11) 1348 (63.89) Emphysema Yes No Unknown 145 (6.87) 1964 (93.08) 1 (0.05) Pleural invasion Yes No Unknown 354 (16.78) 1750 (82.94) 6 (0.28) Adjuvant chemotherapy Yes No 895 (42.42) 1215 (57.58) PMA (cm2)a All patients Male Female 27.41 (21.87, 34. 14) 32.74 (28.11, 38.57) 22.11 (18.66, 26.09) PMI (cm2/m2)a All patients Male Female 10.35 (8.46, 12.49) 11.68 (9.99, 13.74) 8.84 (7.48, 10.51) SMA (cm2)a All patients Male Female 114.90 (96.42, 139.30) 137.60 (123.45, 151.20) 96.31 (86.80, 105.95) SMI (cm2/m2)a All patients Male Female 43.72 (37.72, 49.97) 38.53 (34.75, 42.85) 48.79 (43.88, 54.20) Note: Except where indicated, data are numbers of patients, with percentages in parentheses. BMI = body mass index, PMA = pectoralis muscle area, PMI = pectoralis muscle index, SMA = pectoralis muscle area , SMI = skeletal muscle index. a Numbers are medians, with interquartile ranges in parentheses. 3.2 Relationship between pectoralis muscle index and DMFS Using X-tile software to find the optimal cutoff values of PMI and dividing PMI into high PMI and low PMI groups ( Table 2 ) . Finally, there were 889 (90.25%) females and 853 (75.82%) males in the high PMI group. Kaplan-Meier analysis showed low PMI is associated with lower distant metastasis-free survival rate, lung metastasis-free survival (MFS) rate, liver MFS rate, bone MFS rate, brain MFS rate and adrenal MFS rate, which log-rank test P values are all less than 0.001 ( Figure 1.A-F ) . Univariate Cox regression analysis revealed that PMI was associated with DMFS and the metastasis-free survival at five specific metastatic sites, all with P < 0.001. In the multivariable analysis, PMI was the independent predictor for the DMFS (model 1: hazard ratio [HR] = 1.25, 95% CI: [1.02, 1.52], P = 0.03; model 2: HR = 1.35, 95% CI: [1.11, 1.65], P = 0.003), lung MFS (model 1: HR = 1.36, 95% CI: [1.11, 1.68], P = 0.004; model 2: HR = 1.47, 95% CI: [1.19, 1.81]; P < 0.001) and bone MFS (model 1: HR = 1.26, 95% CI: [1.01, 1.56], P = 0.04; model 2: HR = 1.38, 95% CI: [1.11, 1.73]; P = 0.004) ( Table 3 ) . Table 2 : Segmentation of pectoralis muscle area on CT images and the sex-specific cut-off values of pectoralis muscle index. Segmentation of pectoralis muscle area on CT images Gender Cut-off values Male Female Pectoralis muscle index (cm2/m2) 9.90 6.30 Note ： (A) The pectoralis muscle area was measured at the level of the 4th thoracic vertebra transverse process. (B) Segmentation of the pectoralis major and minor muscle (red area). Table 3 : Univariable and multivariable analyses of pectoralis muscle index for metastasis-free survival. Variable Event Univariate analysis Multivariate analysis(M1)a Multivariate analysis(M2)b HR(95% CI) P . value HR(95% CI) P . value HR(95% CI) P . value DMFS 625 (29.62%) 1.45 (1.20, 1.76) <0.001 1.25 (1.02, 1.52) 0.03 1.35 (1.11, 1.65) 0.003 Lung MFS 540 (25.59%) 1.66 (1.36, 2.02) <0.001 1.36 (1.11, 1.68) 0.004 1.47 (1.19, 1.81) <0.001 Liver MFS 466 (22.09%) 1.57 (1.27, 1.95) <0.001 1.22 (0.97, 1.53) 0.09 1.31 (1.05, 1.66) 0.02 Bone MFS 494 (23.41%) 1.56 (1.27, 1.92) <0.001 1.26 (1.01, 1.56) 0.04 1.38 (1.11, 1.73) 0.004 Brain MFS 518 (24.55%) 1.48 (1.20, 1.81) <0.001 1.20 (0.96, 1.49) 0.10 1.32 (1.06, 1.64) 0.01 Adrenal MFS 470 (22.27%) 1.55 (1.25, 1.92) <0.001 1.21 (0.96, 1.51) 0.10 1.31 (1.04, 1.66) 0.02 Note: HR = hazard ratio, CI = confidence interval, DMFS = distant metastasis-free survival, MFS = distant metastasis-free survival. a Multivariate analysis (M1) was adjusted for age category, gender, BMI category. b Multivariate analysis (M2) was adjusted for multivariate analysis (M1) plus smoking history, tumor stage, histologic stage, surgical approach, emphysema, pleural invasion and adjuvant chemotherapy. 3.3 Relationship between pectoralis muscle index and RFS Kaplan-Meier analysis demonstrated a reduced recurrence-free survival rate and a reduced overall survival (OS) rate in patients with a low PMI, with all P values less than 0.001 ( Figure 1.G-H ) . Univariable Cox regression analysis revealed that PMI was associated with patients’ RFS. In the multivariable analysis, PMI (model 1: HR = 1.24, 95% CI: [1.02, 1.50], P = 0.03; model 2: HR = 1.34, 95% CI: [1.10, 1.62], P = 0.004) remained an independent predictive parameter of RFS ( Table 4 ). In addition, we discussed the relationship between PMI and OS. The results of univariable Cox proportional hazards regression analyses showed a correlation between PMI (HR = 1.58, 95% CI: [1.28, 1.96], P < 0.001) and OS. After adjusting for other confounding factors, a strong correlation between PMI and OS persisted (model 1: HR = 1.22, 95% CI: [0.97, 1.53], P = 0.08; model 2: HR = 1.33, 95% CI: [1.06, 1.68], P = 0.01) ( Supplementary Table S1 ). Table 4 : Univariable and multivariable analyses of clinical and pectoralis muscle index for recurrence-free survival. Variable Univariate analysis Multivariate analysis(M1)a Multivariate analysis(M2)b HR(95% CI) P . value HR(95% CI) P . value HR(95% CI) P . value Age category <65 years Ref Ref ≥65 years 1.23 (1.05, 1.44) 0.01 1.07 (0.90, 1.26) 0.45 Gender Female Ref Ref Male 1.59 (1.36, 1.85) <0.001 1.39 (1.10, 1.76) 0.01 BMI category 18.5-25.0kg/m2, normal weight Ref Ref <18.5kg/m2, underweight 1.46 (1.08, 1.97) 0.01 1.44 (1.06, 1.97) 0.02 ≥25.0kg/m2, above normal weight 0.97 (0.80, 1. 17) 0.75 1.04 (0.85, 1.26) 0.70 Smoking history Never smoker Ref Ref Ref Current or former smoker 1.41 (1.21, 1.65) <0.001 1.02 (0.82, 1.27) 0.86 0.80 (0.65, 1.01) 0.06 Tumor stage I stage Ref Ref Ref II stage 3.06 (2.52, 3.71) <0.001 2.91 (2.39, 3.54) <0.001 2.57 (2.07, 3. 19) <0.001 IIIA stage 4.47 (3.74, 5.33) <0.001 4.30 (3.60, 5. 14) <0.001 3.67 (2.99, 4.51) <0.001 Histologic type Adenocarcinoma Ref Ref Ref Non-adenocarcinoma 1.64 (1.38, 1.94) <0.001 1.40 (1.17, 1.68) <0.001 0.85 (0.70, 1.03) 0.10 Emphysema Yes Ref Ref Ref No 0.74 (0.56, 0.98) 0.04 0.88 (0.66, 1. 16) 0.36 0.80 (0.60, 1.06) 0.11 Surgical approach Thoracotomy Ref Ref Ref Thoracoscope 0.41 (0.35, 0.47) <0.001 0.44 (0.37, 0.51) <0.001 0.67 (0.56, 0.80) <0.001 Adjuvant chemotherapy Yes Ref Ref Ref No 0.57 (0.49, 0.66) <0.001 0.59 (0.50, 0.69) <0.001 0.91 (0.77, 1.07) 0.26 Pleural invasion Yes Ref Ref Ref No 0.72 (0.60, 0.87) 0.001 0.76 (0.63, 0.92) 0.005 0.90 (0.74, 1. 10) 0.30 PMI (cm2/m2) High Ref Ref Low 1.44 (1.20, 1.73) <0.001 1.24 (1.02, 1.50) 0.03 1.34 (1.10, 1.62) 0.004 Note: BMI = body mass index, HR = hazard ratio, CI = confidence interval, PMI = pectoralis muscle index. a Multivariate analysis (M1) was adjusted for age category, gender, BMI category. b Multivariate analysis (M2) was adjusted for multivariate analysis (M1) plus smoking history, tumor stage, histologic stage, surgical approach, emphysema, pleural invasion and adjuvant chemotherapy. 3.4 Subgroup analysis The results of the subgroup analysis showed that, for the four endpoints, patients with low PMI had a higher hazard ratio in most subgroups, similar to the general population. The baseline characteristics of the patients and the PMI did not show significant interaction (all P > 0.05) ( Figure 2 ) . 3.5 Gradient boosting model for comparison of parameters The factors with P < 0.05 obtained in model 1 of the multivariate Cox regression analysis were included in the gradient boosting model (GBM) analysis (Table 4, Supplementary Table S2) . GBM demonstrated that the impact of PMI on RFS, lung metastasis-free survival and bone metastasis-free survival was second only to tumor stage and surgical approach, and was higher than BMI ( Figure 3 ) . 4. Discussion The adverse impact of sarcopenia on the prognosis of cancer patients has been demonstrated by multiple studies[ 18 ]. Currently, CT images at the L3 level are considered the gold standard for non-invasive assessment of human muscle tissue[ 10 ]. Skeletal muscle index (SMI) derived from L3 level CT images has been confirmed to be an independent prognostic factor affecting the survival of patients with various tumors[ 19 – 22 ]. However, its application still has certain limitations in certain patient groups, such as lung cancer patients. Addressing this concern, the European Working Group on Sarcopenia in Older People (EWGSOP) put forward suggestions for finding specific regional thresholds to evaluate skeletal muscle quality to achieve accurate prediction of efficacy[ 10 ]. Can the T4 pectoralis muscle index (T4-PMI) serve as a substitute for the L3 skeletal muscle index (L3-SMI) to assess overall muscle mass in the body and facilitate the evaluation of muscle status in lung cancer patients? We conducted a correlation analysis between T4-PMI and L3-SMI. Pearson's correlation analysis revealed a positive correlation between T4-PMI and L3-SMI (Pearson’s Rho = 0.63, P < 0.001) ( Fig. 4 ) . In previous studies, other researchers have reported similar Pearson’s Rho values[ 23 , 24 ]. Obviously, there is a certain correlation between PMI and SMI, suggesting that the information provided by the skeletal muscle index at the L3 level may be substituted by the pectoralis muscle index at the T4 level. However, this is only a preliminary exploration, and further in-depth research is needed to assess the effectiveness of the T4-PMI in evaluating skeletal muscle in cancer patients. Our study investigated the application of preoperative chest muscle index based on CT scans in predicting distant metastasis-free survival (DMFS), recurrence-free survival (RFS), and metastasis-free survival at specific sites, in the patients with early-stage NSCLC patients. We found that a low PMI was an independent predictor of shorter DMFS, RFS, lung metastasis-free survival and bone metastasis-free survival. To our knowledge, this is the earliest study to explore the predictive value of preoperative CT-derived PMI for specific site metastasis survival in lung cancer, and it is also the study with the largest sample size investigating the relationship between CT-based PMI and RFS in lung cancer patients. Tumor recurrence and metastasis directly affect the quality of life and survival period of patients. Early identification of the risk of tumor recurrence and metastasis can help us implement personalized treatment and follow-up strategies for patients, alleviate patients' suffering, and prolong their survival time. The close relationship between L3-SMI and tumor recurrence and metastasis has been verified in various types of cancers, including lung cancer[ 25 ], colorectal cancer[ 26 ], hepatocellular carcinoma[ 27 ], and nasopharyngeal carcinoma[ 28 ]. As a part of the human skeletal muscular system, the relationship between the pectoralis muscle and cancer has been widely concerned. Recently, studies have confirmed the relationship between PMI and distant metastasis-free survival (DFMS) of breast cancer patients[ 29 ]. However, most studies have focused on the relationship between PMI and lung cancer incidence, length of postoperative hospital stay, or overall survival [ 12 – 15 , 30 , 31 ], and have not explored DMFS and RFS in lung cancer patients. In addition, some studies focus on the impact of global skeletal muscles at the T4 level on tumor prognosis[ 32 ]. While this method provides a comprehensive evaluation of the patient's skeletal muscles, variations in the measurement of skeletal muscle area at the T4 level may arise when patients position their arms differently (e.g., above the head or at the sides of the body)[ 33 ]. This variability could limit the generalizability of the study and narrow the clinical applicability of the results. Therefore, our research mainly focuses on exploring the relationship between T4-PMI and the prognosis of lung cancer patients. Furthermore, on the basis that muscle mass is fundamentally correlated with body size, the muscle area can be adjusted for BMI or height squared, there are no clear criteria. In our study, considering that the pectoralis muscle is not an antigravity muscle, we normalized pectoralis muscle area by height squared[ 13 , 34 ]. This study, however, had some limitations. First, our study was retrospective, was performed at a single institution, only included an exclusively Asian population, with an inevitable selection bias. Second, the standard cutoff values for the PMI is not currently available. The applicability of the cutoff values at the level of T4 obtained in this study needs to be verified by multi-center and more large-sample studies. Furthermore, this study solely based on a single value of the pectoralis muscle obtained through chest CT prior to surgery, and the impact of changes in preoperative and postoperative PMI on patient prognosis was not considered. In the future, longitudinal studies on the changes in preoperative and postoperative PMI are necessary. 5. Conclusion In conclusion, our study showed the clinical value of PMI as an independent predictor of the recurrence-free survival and distant metastasis-free survival in early-stage non–small cell lung cancer. The PMI obtained from preoperative CT images based on the T4 thoracic spine level assist in identifying patients at risk of recurrence and metastasis, guiding the clinical treatment of high-risk patients. Abbreviations BMI: Body mass index CI: Confidence interval CT: Computed tomography DMFS: Distant metastasis-free survival GBM: Gradient boosting model HR: Hazard ratio L3: Third lumbar vertebra MFS: Metastasis-free survival NSCLC: Non-small cell lung cancer OS: Overall survival PMA: Pectoralis muscle area PMD: Pectoralis muscle radiodensity PMI: Pectoralis muscle index RFS: Recurrence-free survival SMA: Pectoralis muscle area SMI: Skeletal muscle index T4: Fourth thoracic vertebr Declarations Ethical standards This study was approved by the Ethics Committee of The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital & Yunnan Cancer Center) (KYLX2022055, 20220512) before the commencement of the research, and the requirement for informed consent was waived as the study was retrospective. Consent for publication Not applicable. Data Availability The development cohort datasets generated and/or analysed during the current study are not publicly available [Data containing personal or sensitive information] but are available from the corresponding author [Zhenhui Li, MD & PhD, Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China. Tel: +86-871-68179549; E-mail: [email protected] ] on reasonable request. Conflict of Interest The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. Funding This work was supported by the National Natural Science Foundation of China [82360345] , and the Yunnan Basic Research Project [202301AT070187]. Author contributions Zhihui Shi : Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing – Original Draft; Lin Wu: Investigation, Software, Validation, Formal Analysis;Dengke Jiang: Investigation, Visualization, Writing – Original Draft; Ruiling Yang: Investigation, Visualization; Rui Liao: Investigation, Visualization; Lizhu Liu: Resources, Supervision; Ruimin You: Resources, Data Curation; Yanli Li: Resources, Data Curation; Xingxiang Dong: Visualization, Writing – Review & Editing; Dafu Zhang: Resources, Data Curation; Xuewen Zhang: Conceptualization, Resources, Supervision, Writing – Review & Editing; Xiaobo Chen: Conceptualization, Funding Acquisition, Resources, Supervision, Writing – Review & Editing; Zhenhui Li: Conceptualization, Funding Acquisition, Resources, Supervision, Writing – Review & Editing. Acknowledgements We thank Yunnan Cancer Center for providing valuable data resources for this research. 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Ninomiya, G.; Fujii, T.; Yamada, S.; Yabusaki, N.; Suzuki, K.; Iwata, N.; Kanda, M.; Hayashi, M.; Tanaka, C.; Nakayama, G., et al. Clinical impact of sarcopenia on prognosis in pancreatic ductal adenocarcinoma: A retrospective cohort study. International Journal of Surgery 2017 , 39, 45-51, doi:10.1016/j.ijsu.2017.01.075. Jin, Y.; Ma, X.; Yang, Z.; Zhang, N. Low L3 skeletal muscle index associated with the clinicopathological characteristics and prognosis of ovarian cancer: a meta‐analysis. Journal of Cachexia, Sarcopenia and Muscle 2023 , 14, 697-705, doi:10.1002/jcsm.13175. Kim, E.Y.; Kim, Y.S.; Park, I.; Ahn, H.K.; Cho, E.K.; Jeong, Y.M. Prognostic Significance of CT-Determined Sarcopenia in Patients with Small-Cell Lung Cancer. Journal of Thoracic Oncology 2015 , 10, 1795-1799, doi:10.1097/jto.0000000000000690. Sanders, K.J.C.; Degens, J.H.R.J.; Dingemans, A.-M.C.; Schols, A.M.W.J. Cross-sectional and longitudinal assessment of muscle from regular chest computed tomography scans: L1 and pectoralis muscle compared to L3 as reference in non-small cell lung cancer. International Journal of Chronic Obstructive Pulmonary Disease 2019 , Volume 14, 781-789, doi:10.2147/copd.S194003. Kim, E.Y.; Kim, Y.S.; Park, I.; Ahn, H.K.; Cho, E.K.; Jeong, Y.M.; Kim, J.H. Evaluation of sarcopenia in small-cell lung cancer patients by routine chest CT. Supportive Care in Cancer 2016 , 24, 4721-4726, doi:10.1007/s00520-016-3321-0. Radfar, A.; Kawaguchi, Y.; Hanaoka, J.; Ohshio, Y.; Okamoto, K.; Kaku, R.; Hayashi, K.; Shiratori, T.; Akazawa, A. Sarcopenia increases the risk of post-operative recurrence in patients with non-small cell lung cancer. Plos One 2021 , 16, doi:10.1371/journal.pone.0257594. Tokunaga, R.; Nakagawa, S.; Miyamoto, Y.; Ohuchi, M.; Izumi, D.; Kosumi, K.; Taki, K.; Higashi, T.; Miyata, T.; Yoshida, N., et al. The clinical impact of preoperative body composition differs between male and female colorectal cancer patients. Colorectal Disease 2019 , 22, 62-70, doi:10.1111/codi.14793. Kobayashi, A.; Kaido, T.; Hamaguchi, Y.; Okumura, S.; Shirai, H.; Yao, S.; Kamo, N.; Yagi, S.; Taura, K.; Okajima, H., et al. Impact of Sarcopenic Obesity on Outcomes in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma. Annals of Surgery 2019 , 269, 924-931, doi:10.1097/sla.0000000000002555. Hua, X.; Liao, J.-F.; Huang, X.; Huang, H.-Y.; Wen, W.; Long, Z.-Q.; Guo, L.; Yuan, Z.-Y.; Lin, H.-X. Sarcopenia is associated with higher toxicity and poor prognosis of nasopharyngeal carcinoma. Therapeutic Advances in Medical Oncology 2020 , 12, doi:10.1177/1758835920947612. Miao, S.; Jia, H.; Huang, W.; Cheng, K.; Zhou, W.; Wang, R. Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model. Cancer Biomark 2024 , 39, 171-185, doi:10.3233/CBM-230219. Gazourian, L.; Durgana, C.S.; Huntley, D.; Rizzo, G.S.; Thedinger, W.B.; Regis, S.M.; Price, L.L.; Pagura, E.J.; Lamb, C.; Rieger-Christ, K., et al. Quantitative Pectoralis Muscle Area is Associated with the Development of Lung Cancer in a Large Lung Cancer Screening Cohort. Lung 2020 , 198, 847-853, doi:10.1007/s00408-020-00388-5. Kinsey, C.M.; San José Estépar, R.; van der Velden, J.; Cole, B.F.; Christiani, D.C.; Washko, G.R. Lower Pectoralis Muscle Area Is Associated with a Worse Overall Survival in Non–Small Cell Lung Cancer. Cancer Epidemiology, Biomarkers & Prevention 2017 , 26, 38-43, doi:10.1158/1055-9965.Epi-15-1067. Grønberg, B.H.; Sjøblom, B.; Wentzel-Larsen, T.; Baracos, V.E.; Hjermstad, M.J.; Aass, N.; Bremnes, R.M.; Fløtten, Ø.; Bye, A.; Jordhøy, M. A comparison of CT based measures of skeletal muscle mass and density from the Th4 and L3 levels in patients with advanced non-small-cell lung cancer. European Journal of Clinical Nutrition 2018 , 73, 1069-1076, doi:10.1038/s41430-018-0325-5. Prakash P, K.M., Digumarthy SR, Shepard JA. Alterations of Anatomic Relationships on Chest Computed Tomography as a Function of Arm Position. 2010 , 34:285–289. Kim, K.M.; Jang, H.C.; Lim, S. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. The Korean Journal of Internal Medicine 2016 , 31, 643-650, doi:10.3904/kjim.2016.015. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.pdf TableS12.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 04 Jul, 2024 Submission checks completed at journal 30 Jun, 2024 First submitted to journal 30 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4661240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322903707,"identity":"7cecc835-c4bd-48b7-b5df-97f8167588e1","order_by":0,"name":"Zhihui Shi","email":"","orcid":"","institution":"the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Shi","suffix":""},{"id":322903708,"identity":"a2542ed5-3646-49cc-85d4-e828da49ac8f","order_by":1,"name":"Lin Wu","email":"","orcid":"","institution":"the Third Affiliated 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuewen","middleName":"","lastName":"Zhang","suffix":""},{"id":322903718,"identity":"e795e604-ec83-40fa-90c8-d3f3984c05ca","order_by":11,"name":"Xiaobo Chen","email":"","orcid":"","institution":"the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Chen","suffix":""},{"id":322903719,"identity":"503f3968-36c4-4c1d-b459-a86b4b70b504","order_by":12,"name":"Zhenhui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYGNiB1gIGNvQ0qdICAFja4Fp5jxGphgGphkEgjVgs7+7MHH3fcSeyTfJa66WYbgxzfjQTGzwX4HZZuOPPMs8Q26bRjt3PbGIwlbyQwS8/Ar+WYNG/bYaCW9DaQlsQNNxLYmHnwamFsk/4L0iJ5HKylnggtzGzSjCAtEmxghyUYENbCxibZ23bYuI0nLe12zjkJoMceNkvj08LPf/yZxM+2w7Lz24+Z3c4ps5HnO5588DM+LehAAogZG0jQMApGwSgYBaMAGwAAud5JCYdc9B0AAAAASUVORK5CYII=","orcid":"","institution":"the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Zhenhui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-30 04:39:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4661240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4661240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61001479,"identity":"54814618-e641-4d37-9495-1655a8b87b7d","added_by":"auto","created_at":"2024-07-24 13:13:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":658002,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis comparing patients in the high PMI group and patients in the low PMI group.\u003cstrong\u003e \u003c/strong\u003eThe Kaplan-Meier analysis of distant metastasis-free survival (A), lung metastasis-free survival (B), liver metastasis-free survival (C), bone metastasis-free survival (D), brain metastasis-free survival (E), adrenal metastasis-free survival (F), recurrence-free survival (G), and overall survival (H) of patients is presented. Survival and number at risk of the high PMI (red) and low PMI group (blue) are plotted in monthly intervals. PMI = pectoralis muscle index. PMI = pectoralis muscle index.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/5bd8a2a5079a9997902a2a39.png"},{"id":61002950,"identity":"691f038b-f50a-4d30-a249-3a0edf9db874","added_by":"auto","created_at":"2024-07-24 13:21:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":584823,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of pectoralis muscle index (PMI) and prognosis. HR = hazard ratio, CI = confidence interval, PMI = pectoralis muscle index.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/389f037afee1f5e6c85ded2f.png"},{"id":61001480,"identity":"7ca35e5a-b203-4e4b-9615-0f27ce8c1110","added_by":"auto","created_at":"2024-07-24 13:13:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106312,"visible":true,"origin":"","legend":"\u003cp\u003eGradient boosting model for relative influences of parameters on recurrence-free survival(a), distant metastasis-free survival(b), lung metastasis-free survival(c) and bone metastasis-free survival(d). BMI = body mass index, PMI = pectoralis muscle index.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/b40c27ca5ee0e9ca2b0602c4.png"},{"id":61002949,"identity":"db976f51-d8a0-4864-81e6-9ead5117c458","added_by":"auto","created_at":"2024-07-24 13:21:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127720,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of the L3 level skeletal muscle index (L3-SMI) and T4 level pectoral muscle index (T4-PMI). Pearson’s Rho = 0.63, P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/f8179ab49ffbd731f42d7e41.png"},{"id":61003880,"identity":"5bbf6cc4-bb2d-41e0-84e5-f4f8a52bb7ce","added_by":"auto","created_at":"2024-07-24 13:29:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2629071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/60559070-6742-4515-9616-4d7484a82671.pdf"},{"id":61002951,"identity":"b9db0e6c-cd55-4b67-b3e4-0cf4f3f20fce","added_by":"auto","created_at":"2024-07-24 13:21:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":73588,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/1439c68d009922eceb57d5c2.pdf"},{"id":61001483,"identity":"6e2196b9-46a3-490b-8e3d-c5163553518a","added_by":"auto","created_at":"2024-07-24 13:13:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22746,"visible":true,"origin":"","legend":"","description":"","filename":"TableS12.docx","url":"https://assets-eu.researchsquare.com/files/rs-4661240/v1/4bca06bc4ac4f701083062cc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative pectoralis muscle index predicts recurrence and metastasis in early-stage non- small cell lung cancer patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to 2020 data, lung cancer accounts for 18% of all cancer deaths worldwide, ranking first among all cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the main treatment for early-stage lung cancer, surgical resection can effectively prolong the survival period of patients. However, some patients still experience recurrence and metastasis after surgery, indicating a poor prognosis. Therefore, early and accurate prediction of the risk of recurrence and metastasis after lung cancer resection is crucial for the long-term survival of patients.\u003c/p\u003e \u003cp\u003eSarcopenia is commonly described as a progressive and generalized disorder of skeletal muscle, reflecting a decline in the patient's physical functioning[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In certain chronic diseases and tumors, such as colorectal cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], breast cancer[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], pancreatic cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], hepatocellular carcinoma[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and cardiovascular disease (CVD)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the adverse prognostic effects of sarcopenia have been evaluated. The effect of sarcopenia on the survival of lung cancer patients has also been proven[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Computed tomography (CT) can accurately quantify information such as the morphology, distribution, and radiodensity of muscle tissue, and are considered an effective tool for assessing the quantity and quality of muscle in the human body[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Currently, images of the third lumbar spine (L3) obtained by CT scans are considered the gold standard for non-invasive muscle assessment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, routine scanning protocols for lung cancer patients typically only include chest and upper abdominal examinations and do not include scans of L3[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For some patients with early-stage lung cancer, the method of assessing the muscular status of the body by skeletal muscle parameters at the L3 level is undoubtedly limited, while expanding the scanning range will exacerbate the financial burden and increase the radiation dose. Hence, the use of chest CT scans to obtain pectoralis muscle parameters may be the best option for assessing muscle status in lung cancer patients.\u003c/p\u003e \u003cp\u003ePreviously, certain studies have investigated the relationship between pectoralis muscle parameters derived from CT scans and the prognosis of lung cancer patients. But, these studies primarily concentrated on examining the correlation between pectoralis muscle index (PMI) or pectoralis muscle radiodensity (PMD) and overall survival [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], postoperative complications[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and length of hospital stay[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], without delving into metastasis and recurrence. Our study aimed to evaluate the association between pectoralis muscle index (PMI) derived from CT scans before surgery and recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and metastasis-free survival at specific distant metastatic sites in patients with early-stage non-small cell lung cancer (NSCLC).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethics approval and informed consent\u003c/h2\u003e \u003cp\u003e The study was carried out in accordance with the Declaration of Helsinki (revised in 2013). It was approved by the ethical council of the cancer hospital(KYLX2022055, 20220512), and the requirement for informed consent was waived as the study was retrospective. To ensure confidentiality, all patient data collected from the survey were made anonymous.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study sample\u003c/h2\u003e \u003cp\u003eWe retrospectively collected consecutive patients who underwent curative surgical resection for stage I\u0026ndash;IIIA NSCLC between January 2013 and December 2018 at a single tertiary cancer hospital. The following inclusion criteria were applied to determine eligibility: (a) patients with pathologic stage I\u0026ndash;IIIA NSCLC; (b) patients at diagnosis were no less than 18 years old; (c) patients who underwent curative surgical resection for NSCLC. Exclusion criteria included: (a) the preoperative CT scan images were incomplete; (b) lost to follow-up; (c) neoadjuvant therapy; (d) history of malignancy; (e) the interval between the last chest CT examination before surgery and the operation exceeded 30 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eClinical data were collected from inpatient and outpatient records containing demographics (gender, age, height, weight, body mass index (BMI)), smoking histories, dates and types of surgery, tumor stages, histologic type (adenocarcinoma vs non-adenocarcinoma), adjuvant chemotherapy, presence of emphysema, pleural invasion and data of survival including recurrence and metastasis. Distant metastases are defined as recurrence of disease in distant organs and/or tissues confirmed by imaging studies or pathological examination of tissue samples. BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated by dividing weight (kg) with height squared (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Body composition analysis\u003c/h2\u003e \u003cp\u003eAll patients\u0026rsquo; preoperative CT scans were examined at first. Unenhanced CT images with an 8-mm slice thickness obtained by spiral 64-detector CT were used for muscle segmentation. The radiologists used Slice-OMatic Software (version 6.0) to manually segmented the area of the pectoralis muscle, including the pectoralis major and pectoralis minor muscle, on the unenhanced CT imaging at 4th thoracic vertebra (T4) level. According to previous studies, the HU thresholds were set from \u0026minus;\u0026thinsp;29 to +\u0026thinsp;150 for the pectoralis muscle [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Two radiologists with three years of diagnostic experience, who were blinded to the clinical data, independently measured the pectoralis muscle area. When the correlation coefficient between the measurements of these two radiologists was less than 0.90, a third radiologist with more than 13 years of diagnostic experience measured the pectoralis muscle area again, and the measurement result was considered final. Otherwise, the final result was the mean of the two measurements. The pectoralis muscle area (cm\u003csup\u003e2\u003c/sup\u003e) was normalized for height (m) squared and reported as pectoralis muscle index (cm\u003csup\u003e2\u003c/sup\u003e /m\u003csup\u003e2\u003c/sup\u003e). Since there is no standard cut-off values for PMI, we stratified patients' PMI using sex-specific X-tile (version 3.6.1) cut-off values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Patient outcomes\u003c/h2\u003e \u003cp\u003eThe outcomes of this study were the recurrence-free survival (RFS) and the distant metastasis-free survival (DMFS). Recurrence-free survival was defined as the time interval from surgery until the occurrence of the first recurrence/metastasis, death from any cause, or the date of the last follow-up. Distant metastasis-free survival was calculated from the date of surgery to the date of first distant metastasis or the date of last follow-up or the date of death from any cause, whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eAccording to the results of the Kolmogorov-Smirnov test for a normal distribution, for normally distributed continuous variables, we defined means and standard deviations (SD) and performed independent two-sample t tests. For categorical variables, we analyzed the number and percentage of patients and used chi-square tests or Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eKaplan-Meier curves were generated and compared the differences between the groups using the log-rank test. Univariable and multivariable Cox regression analyses were performed to identify the independent prognostic factors for recurrence-free survival and metastasis-free survival. We used two models: model 1 adjusted for age, gender, BMI; model 2 further adjusted for smoking history, tumor stage, histologic type, surgical approach, emphysema, pleural invasion and adjuvant chemotherapy. Subgroup analysis was then performed after stratification according to age, gender, BMI, tumor stage, smoking history, histologic type, and surgical approach to explore the potential causes of heterogeneity, and used the R package \u0026ldquo;Forestploter\u0026rdquo; to generate forest plots for visualization of the results of subgroup analyses. Finally, a gradient boosting model (GBM) was used to compare the relative influence of each variable on the prognosis of patients.\u003c/p\u003e \u003cp\u003eData processing and analysis were conducted using the SPSS software (version 25.0) and R software (version 4.3.2). Confidence interval (CI) was set at 95%, and statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of study sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 2811 patients with stage I\u0026ndash;IIIA lung cancer and treated with curative surgical resection, we excluded 360 patients whose preoperative CT scan images were incomplete, 72 patients who were lost to follow-up, 6 patients with neoadjuvant therapy, 183 patients with a history of other malignancies and 80 patients whose interval between the last pre-surgery chest CT examination and the operation was more than 30 days (Supplementary Fig.S1). Finally, 2110 patients were included in this study. Among the 2110 patients included, 1125 (53.32%) were males and 985 (46.68%) were females, with a median (IQR) age 59.00 (52.00\u0026ndash;66.00) years. The PMI was found to be higher in male patients compared to female patients, with median (IQR) PMI values of 11.68 (9.99, 13.74) cm2 /m2 for males and 8.84 (7.48, 10.51) cm2 /m2 for females (Table 1).\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eDemographic characteristics of study sample.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients(n =2110)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAge(y)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e59.00 (52.00,\u0026nbsp;66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003e\u0026lt;65\u0026nbsp;years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge;65years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1482\u0026nbsp;(70.24)\u003c/p\u003e\n \u003cp\u003e628\u0026nbsp;(29.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1125\u0026nbsp;(53.32)\u003c/p\u003e\n \u003cp\u003e985\u0026nbsp;(46.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eBMI(kg/m2\u0026nbsp;)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e22.27 (20.76, 24.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003e\u0026lt;18.5kg/m2, underweight\u003c/p\u003e\n \u003cp\u003e18.5-25.0kg/m2, normal weight\u003c/p\u003e\n \u003cp\u003e\u0026ge;25.0kg/m2, above normal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1566\u0026nbsp;(74.22)\u003c/p\u003e\n \u003cp\u003e110\u0026nbsp;(5.21)\u003c/p\u003e\n \u003cp\u003e434\u0026nbsp;(20.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eNever\u0026nbsp;smoker\u003c/p\u003e\n \u003cp\u003eCurrent or former\u0026nbsp;smoker\u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1265\u0026nbsp;(59.95)\u003c/p\u003e\n \u003cp\u003e837\u0026nbsp;(39.67)\u003c/p\u003e\n \u003cp\u003e8\u0026nbsp;(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eTumor\u0026nbsp;stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eI\u0026nbsp;stage\u003c/p\u003e\n \u003cp\u003eII\u0026nbsp;stage\u003c/p\u003e\n \u003cp\u003eIIIA\u0026nbsp;stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1325\u0026nbsp;(62.80)\u003c/p\u003e\n \u003cp\u003e374\u0026nbsp;(17.73)\u003c/p\u003e\n \u003cp\u003e411\u0026nbsp;(19.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eHistologic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e1656\u0026nbsp;(78.48)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e454\u0026nbsp;(21.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eSurgical approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eThoracotomy \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThoracosocope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e762\u0026nbsp;(36.\u0026nbsp;11)\u003c/p\u003e\n \u003cp\u003e1348\u0026nbsp;(63.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eEmphysema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e145\u0026nbsp;(6.87)\u003c/p\u003e\n \u003cp\u003e1964\u0026nbsp;(93.08)\u003c/p\u003e\n \u003cp\u003e1\u0026nbsp;(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003ePleural invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e354\u0026nbsp;(16.78)\u003c/p\u003e\n \u003cp\u003e1750 (82.94)\u003c/p\u003e\n \u003cp\u003e6\u0026nbsp;(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e895\u0026nbsp;(42.42)\u003c/p\u003e\n \u003cp\u003e1215\u0026nbsp;(57.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003ePMA (cm2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e27.41 (21.87,\u0026nbsp;34.\u0026nbsp;14)\u003c/p\u003e\n \u003cp\u003e32.74 (28.11,\u0026nbsp;38.57)\u003c/p\u003e\n \u003cp\u003e22.11 (18.66, 26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003ePMI (cm2/m2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAll patients\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e10.35 (8.46,\u0026nbsp;12.49)\u003c/p\u003e\n \u003cp\u003e11.68 (9.99,\u0026nbsp;13.74)\u003c/p\u003e\n \u003cp\u003e8.84 (7.48,\u0026nbsp;10.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eSMA (cm2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAll patients\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e114.90 (96.42,\u0026nbsp;139.30)\u003c/p\u003e\n \u003cp\u003e137.60 (123.45,\u0026nbsp;151.20)\u003c/p\u003e\n \u003cp\u003e96.31 (86.80,\u0026nbsp;105.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eSMI (cm2/m2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.78003696857671%\"\u003e\n \u003cp\u003eAll patients\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.21996303142329%\"\u003e\n \u003cp\u003e43.72 (37.72, 49.97)\u003c/p\u003e\n \u003cp\u003e38.53 (34.75, 42.85)\u003c/p\u003e\n \u003cp\u003e48.79 (43.88,\u0026nbsp;54.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eExcept where indicated, data are numbers of patients, with percentages in parentheses. BMI = body mass index, PMA = pectoralis muscle area, PMI = pectoralis muscle index, SMA = pectoralis muscle area , SMI = skeletal muscle index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea\u0026nbsp;Numbers are medians, with interquartile ranges in parentheses.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship between pectoralis muscle index and DMFS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing X-tile software to find the optimal cutoff values of PMI and dividing PMI into high PMI and low PMI groups \u003cstrong\u003e(\u003cem\u003eTable 2\u003c/em\u003e)\u003c/strong\u003e. Finally, there were 889 (90.25%) females and 853 (75.82%) males in the high PMI group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis showed low PMI is associated with lower distant metastasis-free survival rate, lung metastasis-free survival (MFS) rate, liver MFS rate, bone MFS rate, brain MFS rate and adrenal MFS rate, which log-rank test \u003cem\u003eP\u003c/em\u003e values are all less than 0.001\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003cem\u003eFigure 1.A-F\u003c/em\u003e)\u003c/strong\u003e. Univariate Cox regression analysis revealed that PMI was associated with DMFS and the metastasis-free survival at five specific metastatic sites, all with \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001. In the multivariable analysis, PMI was the independent predictor for the DMFS (model 1: hazard ratio [HR] = 1.25, 95% CI: [1.02, 1.52], \u003cem\u003eP\u003c/em\u003e = 0.03; model 2: HR = 1.35, 95% CI: [1.11, 1.65], \u003cem\u003eP\u003c/em\u003e = 0.003), lung MFS (model 1: HR = 1.36, 95% CI: [1.11, 1.68], \u003cem\u003eP\u003c/em\u003e = 0.004; model 2: HR = 1.47, 95% CI: [1.19, 1.81]; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and bone MFS (model 1: HR = 1.26, 95% CI: [1.01, 1.56], \u003cem\u003eP\u003c/em\u003e = 0.04; model 2: HR = 1.38, 95% CI: [1.11, 1.73]; \u003cem\u003eP\u003c/em\u003e = 0.004) \u003cstrong\u003e(\u003cem\u003eTable 3\u003c/em\u003e)\u003c/strong\u003e. \u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e: Segmentation of pectoralis muscle area on CT images and the sex-specific cut-off values of pectoralis muscle index.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.644549763033176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSegmentation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof pectoralis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;muscle area on CT images\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07266982622433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cimg width=\"226\" 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alt=\"image\" height=\"165\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.644549763033176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u0026nbsp;values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07266982622433%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.2827804107425%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n 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(B) Segmentation of the pectoralis major and minor muscle (red area).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e3\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eUnivariable and multivariable analyses of\u0026nbsp;pectoralis muscle\u0026nbsp;index\u0026nbsp;for\u0026nbsp;metastasis-free\u0026nbsp;survival.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.56281407035176%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.73031825795645%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.12060301507538%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.293132328308207%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis(M1)a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.293132328308207%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis(M2)b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.02908277404922%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.185682326621924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.700223713646533%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.080536912751677%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.476510067114095%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527964205816556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eDMFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e625 (29.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.45 (1.20,\u0026nbsp;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.25 (1.02,\u0026nbsp;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.35 (1.11,\u0026nbsp;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eLung MFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e540 (25.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.66 (1.36,\u0026nbsp;2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.36 (1.11,\u0026nbsp;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.47 (1.19,\u0026nbsp;1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eLiver MFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e466 (22.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.57 (1.27,\u0026nbsp;1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.97,\u0026nbsp;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.31 (1.05,\u0026nbsp;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eBone MFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e494 (23.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.56 (1.27,\u0026nbsp;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (1.01,\u0026nbsp;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 (1.11,\u0026nbsp;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eBrain MFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e518 (24.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.48 (1.20,\u0026nbsp;1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.96,\u0026nbsp;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.32 (1.06,\u0026nbsp;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\" valign=\"top\"\u003e\n \u003cp\u003eAdrenal MFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.709030100334449%\" valign=\"top\"\u003e\n \u003cp\u003e470 (22.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.719063545150501%\" valign=\"top\"\u003e\n \u003cp\u003e1.55 (1.25,\u0026nbsp;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.220735785953178%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.96,\u0026nbsp;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03010033444816%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05351170568562%\" valign=\"top\"\u003e\n \u003cp\u003e1.31 (1.04,\u0026nbsp;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.364548494983278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e HR = hazard ratio, CI = confidence interval, DMFS = distant metastasis-free survival, MFS = distant metastasis-free survival. a Multivariate analysis (M1) was adjusted for age category, gender, BMI category.\u003c/p\u003e\n\u003cp\u003eb\u0026nbsp;Multivariate analysis (M2) was adjusted for multivariate\u0026nbsp;analysis\u0026nbsp;(M1) plus\u0026nbsp;smoking\u0026nbsp;history, tumor\u0026nbsp;stage, histologic\u0026nbsp;stage,\u0026nbsp;surgical\u0026nbsp;approach,\u0026nbsp;emphysema, pleural invasion and adjuvant chemotherapy.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Relationship between pectoralis muscle index and RFS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis demonstrated a reduced recurrence-free survival rate and a reduced overall survival (OS) rate in patients with a low PMI, with all P values less than 0.001 \u003cstrong\u003e(\u003cem\u003eFigure 1.G-H\u003c/em\u003e)\u003c/strong\u003e. \u0026nbsp;Univariable Cox regression analysis revealed that PMI was associated with patients\u0026rsquo; RFS. In the multivariable analysis, PMI (model 1: HR = 1.24, 95% CI: [1.02, 1.50], \u003cem\u003eP\u003c/em\u003e = 0.03; model 2: HR = 1.34, 95% CI: [1.10, 1.62], \u003cem\u003eP\u003c/em\u003e = 0.004) remained an independent predictive parameter of RFS (\u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e). In addition, we discussed the relationship between PMI and OS. The results of univariable Cox proportional hazards regression analyses showed a correlation between PMI (HR = 1.58, 95% CI: [1.28, 1.96], \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and OS. After adjusting for other confounding factors, a strong correlation between PMI and OS persisted (model 1: HR = 1.22, 95% CI: [0.97, 1.53], \u003cem\u003eP\u003c/em\u003e = 0.08; model 2: HR = 1.33, 95% CI: [1.06, 1.68], \u003cem\u003eP\u003c/em\u003e = 0.01) \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eSupplementary\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eTable S1\u003c/em\u003e\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e : Univariable and multivariable analyses of clinical and pectoralis muscle index for recurrence-free survival.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.897196261682243%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.83177570093458%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis(M1)a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.364485981308412%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis(M2)b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.555555555555557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.555555555555555%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.777777777777779%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.05,\u0026nbsp;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.90,\u0026nbsp;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.59 (1.36,\u0026nbsp;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e1.39 (1.10,\u0026nbsp;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003e18.5-25.0kg/m2, normal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;18.5kg/m2, underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.46 (1.08,\u0026nbsp;1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 (1.06,\u0026nbsp;1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;25.0kg/m2, above normal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.80,\u0026nbsp;1.\u0026nbsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.85,\u0026nbsp;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u0026nbsp;smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent or former\u0026nbsp;smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.41 (1.21,\u0026nbsp;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.82,\u0026nbsp;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.65,\u0026nbsp;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eTumor stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eI\u0026nbsp;stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eII\u0026nbsp;stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e3.06 (2.52,\u0026nbsp;3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e2.91 (2.39,\u0026nbsp;3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e2.57 (2.07,\u0026nbsp;3.\u0026nbsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eIIIA stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e4.47 (3.74,\u0026nbsp;5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e4.30 (3.60,\u0026nbsp;5.\u0026nbsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e3.67 (2.99,\u0026nbsp;4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eHistologic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.64 (1.38,\u0026nbsp;1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e1.40 (1.17,\u0026nbsp;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.70,\u0026nbsp;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eEmphysema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 (0.56,\u0026nbsp;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 (0.66,\u0026nbsp;1.\u0026nbsp;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.60,\u0026nbsp;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eSurgical approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eThoracotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eThoracoscope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (0.35,\u0026nbsp;0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 (0.37,\u0026nbsp;0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.67 (0.56,\u0026nbsp;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 (0.49,\u0026nbsp;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e0.59 (0.50,\u0026nbsp;0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.77,\u0026nbsp;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003ePleural invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.60,\u0026nbsp;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.63,\u0026nbsp;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.74,\u0026nbsp;1.\u0026nbsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003ePMI (cm2/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.906542056074766%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.109034267912772%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 (1.20,\u0026nbsp;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.788161993769471%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576323987538942%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 (1.02,\u0026nbsp;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.09968847352025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.264797507788161%\" valign=\"top\"\u003e\n \u003cp\u003e1.34 (1.10,\u0026nbsp;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.255451713395638%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eBMI = body mass index, HR = hazard ratio, CI = confidence interval, PMI = pectoralis muscle index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Multivariate analysis (M1) was adjusted for age category, gender, BMI category.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eMultivariate analysis (M2) was adjusted for multivariate analysis (M1) plus smoking history, tumor stage, histologic stage, surgical approach, emphysema, pleural invasion and adjuvant chemotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the subgroup analysis showed that, for the four endpoints, patients with low PMI had a higher hazard ratio in most subgroups, similar to the general population. The baseline characteristics of the patients and the PMI did not show significant interaction (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05) \u003cstrong\u003e(\u003cem\u003eFigure 2\u003c/em\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Gradient boosting model for comparison of parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe factors with P \u0026lt; 0.05 obtained in model 1 of the multivariate Cox regression analysis were included in the gradient boosting model (GBM) analysis \u003cstrong\u003e\u003cem\u003e(Table 4,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eSupplementary\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eTable S2)\u003c/em\u003e\u003c/strong\u003e. GBM demonstrated that the impact of PMI on RFS, lung metastasis-free survival and bone metastasis-free survival was second only to tumor stage and surgical approach, and was higher than BMI \u003cstrong\u003e(\u003cem\u003eFigure 3\u003c/em\u003e)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe adverse impact of sarcopenia on the prognosis of cancer patients has been demonstrated by multiple studies[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Currently, CT images at the L3 level are considered the gold standard for non-invasive assessment of human muscle tissue[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Skeletal muscle index (SMI) derived from L3 level CT images has been confirmed to be an independent prognostic factor affecting the survival of patients with various tumors[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, its application still has certain limitations in certain patient groups, such as lung cancer patients. Addressing this concern, the European Working Group on Sarcopenia in Older People (EWGSOP) put forward suggestions for finding specific regional thresholds to evaluate skeletal muscle quality to achieve accurate prediction of efficacy[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Can the T4 pectoralis muscle index (T4-PMI) serve as a substitute for the L3 skeletal muscle index (L3-SMI) to assess overall muscle mass in the body and facilitate the evaluation of muscle status in lung cancer patients? We conducted a correlation analysis between T4-PMI and L3-SMI. Pearson's correlation analysis revealed a positive correlation between T4-PMI and L3-SMI (Pearson\u0026rsquo;s Rho\u0026thinsp;=\u0026thinsp;0.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In previous studies, other researchers have reported similar Pearson\u0026rsquo;s Rho values[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Obviously, there is a certain correlation between PMI and SMI, suggesting that the information provided by the skeletal muscle index at the L3 level may be substituted by the pectoralis muscle index at the T4 level. However, this is only a preliminary exploration, and further in-depth research is needed to assess the effectiveness of the T4-PMI in evaluating skeletal muscle in cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur study investigated the application of preoperative chest muscle index based on CT scans in predicting distant metastasis-free survival (DMFS), recurrence-free survival (RFS), and metastasis-free survival at specific sites, in the patients with early-stage NSCLC patients. We found that a low PMI was an independent predictor of shorter DMFS, RFS, lung metastasis-free survival and bone metastasis-free survival. To our knowledge, this is the earliest study to explore the predictive value of preoperative CT-derived PMI for specific site metastasis survival in lung cancer, and it is also the study with the largest sample size investigating the relationship between CT-based PMI and RFS in lung cancer patients.\u003c/p\u003e \u003cp\u003eTumor recurrence and metastasis directly affect the quality of life and survival period of patients. Early identification of the risk of tumor recurrence and metastasis can help us implement personalized treatment and follow-up strategies for patients, alleviate patients' suffering, and prolong their survival time. The close relationship between L3-SMI and tumor recurrence and metastasis has been verified in various types of cancers, including lung cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], colorectal cancer[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], hepatocellular carcinoma[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and nasopharyngeal carcinoma[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As a part of the human skeletal muscular system, the relationship between the pectoralis muscle and cancer has been widely concerned. Recently, studies have confirmed the relationship between PMI and distant metastasis-free survival (DFMS) of breast cancer patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, most studies have focused on the relationship between PMI and lung cancer incidence, length of postoperative hospital stay, or overall survival [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and have not explored DMFS and RFS in lung cancer patients. In addition, some studies focus on the impact of global skeletal muscles at the T4 level on tumor prognosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While this method provides a comprehensive evaluation of the patient's skeletal muscles, variations in the measurement of skeletal muscle area at the T4 level may arise when patients position their arms differently (e.g., above the head or at the sides of the body)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This variability could limit the generalizability of the study and narrow the clinical applicability of the results. Therefore, our research mainly focuses on exploring the relationship between T4-PMI and the prognosis of lung cancer patients. Furthermore, on the basis that muscle mass is fundamentally correlated with body size, the muscle area can be adjusted for BMI or height squared, there are no clear criteria. In our study, considering that the pectoralis muscle is not an antigravity muscle, we normalized pectoralis muscle area by height squared[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study, however, had some limitations. First, our study was retrospective, was performed at a single institution, only included an exclusively Asian population, with an inevitable selection bias. Second, the standard cutoff values for the PMI is not currently available. The applicability of the cutoff values at the level of T4 obtained in this study needs to be verified by multi-center and more large-sample studies. Furthermore, this study solely based on a single value of the pectoralis muscle obtained through chest CT prior to surgery, and the impact of changes in preoperative and postoperative PMI on patient prognosis was not considered. In the future, longitudinal studies on the changes in preoperative and postoperative PMI are necessary.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study showed the clinical value of PMI as an independent predictor of the recurrence-free survival and distant metastasis-free survival in early-stage non\u0026ndash;small cell lung cancer. The PMI obtained from preoperative CT images based on the T4 thoracic spine level assist in identifying patients at risk of recurrence and metastasis, guiding the clinical treatment of high-risk patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eCT: Computed tomography\u003c/p\u003e\n\u003cp\u003eDMFS: Distant metastasis-free survival\u003c/p\u003e\n\u003cp\u003eGBM: Gradient boosting model\u003c/p\u003e\n\u003cp\u003eHR: Hazard ratio\u003c/p\u003e\n\u003cp\u003eL3: Third lumbar vertebra\u003c/p\u003e\n\u003cp\u003eMFS: Metastasis-free survival\u003c/p\u003e\n\u003cp\u003eNSCLC: Non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eOS: Overall survival\u003c/p\u003e\n\u003cp\u003ePMA: Pectoralis muscle area\u003c/p\u003e\n\u003cp\u003ePMD: Pectoralis muscle radiodensity\u003c/p\u003e\n\u003cp\u003ePMI: Pectoralis muscle index\u003c/p\u003e\n\u003cp\u003eRFS: Recurrence-free survival\u003c/p\u003e\n\u003cp\u003eSMA: Pectoralis muscle area\u003c/p\u003e\n\u003cp\u003eSMI: Skeletal muscle index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT4: Fourth thoracic vertebr\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital \u0026amp; Yunnan Cancer Center) (KYLX2022055, 20220512) before the commencement of the research, and the requirement for informed consent was waived as the study was retrospective.\u0026nbsp;\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\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe development cohort datasets generated and/or analysed during the current study are not publicly available [Data containing personal or sensitive information] but are available from the corresponding author [Zhenhui Li, MD \u0026amp; PhD, Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China. Tel: +86-871-68179549; E-mail: [email protected]] on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [82360345] , and the Yunnan Basic Research Project [202301AT070187].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhihui Shi : Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing \u0026ndash; Original Draft; \u0026nbsp;Lin Wu: Investigation, Software, Validation, Formal Analysis;Dengke Jiang: Investigation, Visualization, Writing \u0026ndash; Original Draft; Ruiling Yang: Investigation, Visualization; Rui Liao: Investigation, Visualization; Lizhu Liu: Resources, Supervision; Ruimin You: Resources, Data Curation; Yanli Li: Resources, Data Curation; Xingxiang Dong: Visualization, Writing \u0026ndash; Review \u0026amp; Editing; Dafu Zhang: Resources, Data Curation; Xuewen Zhang: Conceptualization, \u0026nbsp; Resources, Supervision, Writing \u0026ndash; Review \u0026amp; Editing; Xiaobo Chen: Conceptualization, Funding Acquisition, Resources, Supervision, Writing \u0026ndash; Review \u0026amp; Editing; Zhenhui Li: Conceptualization, Funding Acquisition, Resources, Supervision, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank Yunnan Cancer Center for providing valuable data resources for this research. We are grateful to all the staff who participated in the data collection, thanks for their active cooperation and firm support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians \u003cstrong\u003e2021\u003c/strong\u003e, 71, 209-249, doi:10.3322/caac.21660.\u003c/li\u003e\n\u003cli\u003eHaase, C.B.; Brodersen, J.B.; B\u0026uuml;low, J. Sarcopenia: early prevention or overdiagnosis? Bmj \u003cstrong\u003e2022\u003c/strong\u003e, 10.1136/bmj-2019-052592, doi:10.1136/bmj-2019-052592.\u003c/li\u003e\n\u003cli\u003eXiao, J.; Caan, B.J.; Cespedes Feliciano, E.M.; Meyerhardt, J.A.; Peng, P.D.; Baracos, V.E.; Lee, V.S.; Ely, S.; Gologorsky, R.C.; Weltzien, E., et al. 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Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. The Korean Journal of Internal Medicine \u003cstrong\u003e2016\u003c/strong\u003e, 31, 643-650, doi:10.3904/kjim.2016.015.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung carcinoma, Pectoralis muscles, Body composition, Recurrence, Neoplasm metastasis","lastPublishedDoi":"10.21203/rs.3.rs-4661240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4661240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSarcopenia is a well-established prognostic factor in patients with malignancies, with the muscle index serving as a key parameter in evaluating sarcopenia. However, the relationship between the pectoralis muscle index (PMI) determined by preoperative computed tomography (CT) and recurrence-free survival (RFS), as well as distant metastasis-free survival (DMFS), remains unclear in patients with early-stage non-small cell lung cancer (NSCLC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eConsecutive patients who underwent curative-intent resection for stage I to IIIA NSCLC between 2013 and 2018 at a cancer center were retrospectively identified. The Cox proportional hazard model was employed to analyze the correlation between PMI and survival, with subgroup analyses conducted to explore potential heterogeneity among different subgroups. Finally, the relative influence of each parameter was compared using a gradient boosting model (GBM).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2110 patients (median (IQR) age 59.00 (52.00, 66.00) years, 1125 (53.32%) males, median follow-up of 64.73 months) were evaluated. Kaplan-Meier survival analysis showed that the RFS rate, DMFS rate, lung metastasis-free survival (MFS) rate, liver MFS rate, brain MFS rate, bone MFS rate, and adrenal MFS rate of patients in the high PMI group were higher than those in the low PMI group, all with P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In the multivariable analysis, low PMI is still associated with shorter RFS ( hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.34, 95% confidence interval [CI]: (1.10, 1.62), P\u0026thinsp;=\u0026thinsp;0.004), DMFS (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: (1.11, 1.65), P\u0026thinsp;=\u0026thinsp;0.003), lung MFS (HR\u0026thinsp;=\u0026thinsp;1.47, 95% CI (1.19, 1.81), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and bone MFS (HR\u0026thinsp;=\u0026thinsp;1.38, 95% CI: (1.11, 1.73), P\u0026thinsp;=\u0026thinsp;0.004). These associations were consistent in subgroup analysis of different gender, age, tumor stage, histologic type, and surgical approach group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAs an independent predictor of RFS and DMFS in patients with early-stage NSCLC, preoperative CT-based PMI may contribute to further refining the risk stratification of NSCLC.\u003c/p\u003e","manuscriptTitle":"Preoperative pectoralis muscle index predicts recurrence and metastasis in early-stage non- small cell lung cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 13:13:20","doi":"10.21203/rs.3.rs-4661240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-05T00:23:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-01T02:34:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2024-06-30T04:37:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e90e23e-518e-4f65-81d4-71c220e8a7e0","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-24T13:13:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 13:13:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4661240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4661240","identity":"rs-4661240","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}