Comparative Prognostic Value of L1-SMI and T4-PMI in Esophageal Cancer Radical Resection

Comparative Prognostic Value of L1-SMI and T4-PMI in Esophageal Cancer Radical Resection | 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 Comparative Prognostic Value of L1-SMI and T4-PMI in Esophageal Cancer Radical Resection Peishuang Wang, Juan Zhang, Zhan Zhang, Chuanbin Wang, Haiou Zhou, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9139366/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Sarcopenia is increasingly recognized as a predictor of poor postoperative outcomes in cancer patients. However, the comparative value of pectoralis muscle index at the T4 level (T4-PMI) and skeletal muscle index at the L1 level (L1-SMI) in assessing sarcopenia and predicting outcomes in esophageal cancer remains unclear. This study aimed to compare the associations of T4-PMI and L1-SMI with postoperative mechanical ventilation and long-term survival following radical esophagectomy. Methods A retrospective analysis was conducted on 184 patients who underwent radical esophagectomy between 2023 and 2025. Preoperative CT images were used to measure T4-PMI and L1-SMI, and sarcopenia was defined based on gender-specific fifth-percentile cutoffs. Primary outcomes included duration of ICU mechanical ventilation, ICU and hospital length of stay (LOS), hospitalization costs, and overall survival. Cox regression models were used to evaluate survival predictors. Results The cohort was predominantly male (73.4%) with a median age of 74 years. T4-PMI and L1-SMI were moderately correlated (r = 0.657) but demonstrated poor agreement, suggesting they are not interchangeable. T4-sarcopenia was not associated with any postoperative outcomes. In contrast, L1-sarcopenia was linked to longer mechanical ventilation, increased ICU and hospital LOS, and higher costs (all P < 0.05). Multivariate analysis identified L1-sarcopenia (HR = 2.297, P = 0.030) and pathological type (other types, HR = 7.346, P = 0.003) as independent predictors of poor survival. Higher measured/predicted PEFR (%) was a protective factor (HR = 0.984, P = 0.018). Conclusions L1-SMI is a more robust predictor than T4-PMI of prolonged mechanical ventilation and poor survival in esophageal cancer patients. It may serve as a clinically useful marker to guide perioperative risk assessment and management. Skeletal Muscle Index Pectoralis Muscle Index Esophageal Cancer Sarcopenia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Sarcopenia refers to the widespread and progressive loss of skeletal muscle function and mass, reflecting the physical function of malignant tumor patients in a state of potential frailty [ 1 ] . Multiple studies have confirmed that sarcopenia is closely associated with poor postoperative prognosis in malignant tumor patients, which is specifically manifested as prolonged mechanical ventilation time, increased incidence of postoperative complications, prolonged ICU and total length of stay, increased risk of death, as well as decreased tolerance to radiotherapy and chemotherapy and increased adverse reactions [ 2 – 6 ] . Computed tomography (CT) is currently an important assessment tool for the local precise quantification of skeletal muscle, and the skeletal muscle area measured by CT at the third lumbar vertebra (L3) level is regarded as the gold standard for sarcopenia assessment. However, in Chinese clinical practice, esophageal cancer patients only routinely undergo chest CT examination before surgery, and the scanning range usually only covers up to the first lumbar vertebra (L1) level, making it impossible to obtain imaging data at the L3 level, thus making it difficult to use the L3 skeletal muscle area for preoperative sarcopenia assessment. The pectoralis muscle at the fourth thoracic vertebra (T4) level and the skeletal muscle at the L1 level can both be clearly displayed by chest CT, and measuring their cross-sectional area (CSA) does not require additional radiation exposure. Studies by Annarita Pecchi and other scholars have confirmed that quantitative indicators of pectoralis muscle mass can be used for the assessment and diagnosis of sarcopenia, and in breast cancer patients, pectoralis muscle measurement results can also be used to estimate the total body skeletal muscle mass [ 7 – 8 ] . In addition, for non-small cell lung cancer patients undergoing tumor treatment, the analysis of skeletal muscle mass at the L1 level based on routine chest CT scans is considered a better alternative to the L3 level, and the trunk skeletal muscle group at the L1 level is significantly associated with poor survival rate [ 9 – 10 ] . A CT analysis based on the US healthy population further showed that the skeletal muscle cross-sectional area at the L1 and L3 levels has a good correlation, and the study also proposed a skeletal muscle cutoff value for sarcopenia applicable to the L1 level [ 11 ] . At present, there are no relevant research reports at home and abroad on the impact of T4-PMI or L1-SMI on adverse postoperative outcomes in esophageal cancer patients. This study intends to compare and analyze the differences between T4-PMI and L1-SMI, use them as sarcopenia assessment indicators, and for the first time explore their impact on mechanical ventilation time and survival outcomes after radical esophagectomy, in order to screen out a more suitable sarcopenia indicator for evaluating the prognosis of patients undergoing radical esophagectomy. 2. Materials and Methods 2.1 Study Objects and Data Collection This study protocol was approved by the Ethics Committee of Anhui Cancer Hospital (approval number: 2026-LLYJ-WZ-0004). Based on the retrospective design of this study, the requirement for written informed consent from patients was waived after review by the Ethics Committee. All procedures of the study strictly followed the principles and regulations of the Declaration of Helsinki. This retrospective study consecutively enrolled patients with clinical TNM stage Ⅰ-Ⅳ esophageal cancer who underwent radical esophagectomy in our hospital between January 2023 and March 2025 (Fig. 1), and conducted a retrospective analysis of prospectively collected and standardized clinical data. Chest CT images and other relevant examination and test data within 3 weeks before surgery were collected and sorted out. The specific clinical data are as follows: ① Demographic characteristics: gender, age, body mass index (BMI); ② Preoperative lung function indicators: forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC), peak expiratory flow rate (PEFR), percentage of measured/predicted peak expiratory flow rate (Meas/Pred PEFR, %); ③ Preoperative peripheral blood laboratory indicators: albumin, hemoglobin; ④ Pathological data: histological type and pathological stage determined according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system [ 12 ] ; ⑤ Surgical method: all patients underwent combined laparoscopic and open surgery; ⑥ Early postoperative assessment: Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) within 24 hours after transfer to the intensive care unit (ICU); ⑦ Postoperative outcome indicators: postoperative complications based on Clavien-Dindo classification (grade Ⅱ and above), postoperative mechanical ventilation time after transfer to ICU, ICU length of stay, total length of stay, total hospitalization costs, and postoperative survival time (defined as the time from the date of surgery to the time of death from any cause or the last follow-up time). 2.2 CT Image Analysis Chest CT scan images used for tumor diagnosis and treatment plan formulation within 2 weeks before surgery were selected for body composition analysis. CT examination was performed using a 64-slice spiral CT scanner with a 5 mm slice thickness. Axial CT images in DICOM format were exported from the hospital electronic medical record system, and Slice-O-Matic software v5.0 (Tomovision, Montreal, Quebec, Canada) was used for cross-sectional muscle area measurement and analysis. The measurement levels and indicator definitions are as follows: ① T4 level measurement: on the cross-sectional image at the level of the transverse process of the fourth thoracic vertebra, identify and measure the CSA of the bilateral pectoralis major and pectoralis minor muscles, with the unit of cm²; for the T4 level, since the position of the arms can lead to significant differences in chest wall muscle tissue, images with arms placed at the side of the body are excluded. ② L1 level measurement: on the cross-sectional image at the level of the first lumbar vertebra where the bilateral vertebral transverse processes are clearly displayed, measure the cross-sectional area of all skeletal muscles, including psoas major, erector spinae, quadratus lumborum, transversus abdominis, external oblique abdominis, internal oblique abdominis, and rectus abdominis. Muscle tissue quantification: A preset Hounsfield unit threshold was used for tissue definition, where the threshold range for skeletal muscle was − 29 to 150 HU, based on which the target muscle area was calculated (Fig. 2 ). Manual correction was performed when the image boundary was blurred. All measurements were independently completed by two researchers who had received unified training and were unaware of the patients' clinical data. Finally, the average of the two measurement results was used for subsequent analysis. The muscle cross-sectional area measured at each level was normalized by dividing by the patient's body mass index (BMI) to calculate T4-PMI and L1-SMI, respectively. 2.3 Sarcopenia Grouping Criteria Due to the different distributions of skeletal muscle index between men and women, patients were divided into sarcopenia group and non-sarcopenia group according to the gender-specific cutoff values of the lowest quintile (T4: male 1.29, female 0.75; L1: male 3.81, female 2.66) [ 13 ] . Finally, in the T4 grouping: 37 cases in the sarcopenia group and 145 cases in the non-sarcopenia group; in the L1 grouping: 37 cases in the sarcopenia group and 147 cases in the non-sarcopenia group. 3. Statistical Analysis All statistical analyses in this study were performed using R language (version 4.4.3) in the RStudio environment. Baseline data analysis: categorical variables were expressed as frequency (percentage) [n (%)], and intergroup comparison was performed using χ² test or Fisher's exact test (when the theoretical frequency < 5). For continuous variables, those conforming to normal distribution were expressed as mean ± standard deviation (mean ± SD), and intergroup comparison was performed using independent samples t-test; those not conforming to normal distribution were expressed as median (interquartile range) [median (IQR)], and intergroup comparison was performed using Mann-Whitney U test. Correlation and consistency analysis: Pearson correlation coefficient was used to evaluate the linear correlation strength between T4-PMI and L1-SMI; the internal consistency between the two was tested by Cronbach's α coefficient, which ranges from 0 to 1, and the closer to 1, the better the consistency. At the same time, a Bland-Altman plot was drawn to evaluate the fixed bias between the two: the mean of T4-PMI and L1-SMI was taken as the X-axis, and the percentage of the difference between the two was taken as the Y-axis to analyze whether the difference showed a constant trend; proportional bias was evaluated by Pittman variance difference test. Survival analysis: Follow-up time was calculated from the date of surgery to the patient's death, last follow-up, or cutoff date (November 23, 2025). Survival curves were drawn using the Kaplan-Meier method, and intergroup comparison was performed using the log-rank test. The Cox proportional hazards regression model was used for prognostic factor analysis: first, all potential factors were included in univariate analysis, variables with P < 0.1 were included in the multivariate model, variables were screened by backward stepwise regression, and finally the hazard ratio (HR) and 95% confidence interval (CI) of each independent risk factor were calculated. The Schoenfeld residual test was used to test the Cox proportional hazards assumption. 4. Results 4.1 Baseline Clinicopathological Characteristics A total of 632 consecutive patients who underwent radical esophagectomy were enrolled in this study, and the study population was finally determined after strict screening. The exclusion criteria and number of excluded cases are as follows: 409 patients who were not transferred to the intensive care unit (ICU) after surgery; 35 patients who lacked preoperative chest computed tomography (CT) images or complete clinical data; 4 patients with severe postoperative pneumothorax and subcutaneous emphysema due to severe intraoperative thoracic adhesions. In addition, 2 patients were excluded only from pectoralis muscle index-related analysis due to poor CT scan quality (Fig. 1). The final study population included 184 patients, of which 135 were male (73.4%) and 49 were female (26.6%), with a median age of 74 years. The distribution of clinical TNM stages: 58 cases (31.5%) of 0-Ⅰ stage, 66 cases (35.9%) of Ⅱ stage, 47 cases (25.5%) of Ⅲ stage, and 13 cases (7.1%) of Ⅳ stage; among histological types, 166 cases (90.2%) were squamous cell carcinoma. The median overall survival time of all patients was 14 months, with a follow-up time range of 1 to 35 months (Table 1 ). Table 1 also presents the comparison of baseline characteristics between sarcopenia and non-sarcopenia patients based on T4 and L1 groupings. The results showed that the proportion of patients with BMI ≥ 25 kg/m² in the T4 sarcopenia group was significantly higher than that in the T4 non-sarcopenia group (P < 0.05). In the L1 grouping, the average age of the sarcopenia group was significantly higher than that of the non-sarcopenia group, and the preoperative albumin level was significantly lower (both P 0.05). Table 1 Baseline demographic and characteristics of patients with or without sarcopenia. Characteristic Patients(n = 184) T4 (n = 182) L1 (n = 184) T4-Sarcopenia(n = 37;20.3%) T4-NonSarcopenia(n = 145;79.7%) P value L1-Sarcopenia(n = 37;20.1%) L1-NonSarcopenia(n = 147;79.9%) P value Gender 1.00 1.00 male 135(73.4) 27(73.0) 106(73.1) 27(73.0) 108(73.5) female 49(26.6) 10(27.0) 39(26.9) 10(27.0) 39(26.5) Age(year), median (IQR) 74(70-77.25) 74(69–79) 73(70–77) 0.603 75(73–79) 73(69-76.5) 0.012 BMI(kg/m 2 ) ,mean ± SD 23.007 ± 3.489 25.55 ± 2.92 22.36 ± 3.36 < 0.001 23.73 ± 3.37 22.82 ± 3.51 0.157 BMI(kg/m2),category < 0.001 0.506 Underweight (< 18.5) 16(8.7) 0(0.0) 16(11.0) 1(2.7) 15(10.2) Normalweight(18.5 − 22.9) 75(40.7) 7(18.9) 67(46.2) 15(40.5) 60(40.8) Overweight(23 − 24.9) 36(19.6) 8(21.6) 27(18.6) 9(24.3) 27(18.4) Obesity(≥ 25) 57(31.0) 22(59.5) 35(24.1) 12(32.4) 45(30.6) a cTNM stage 0.029 0.052 0–1 58(31.5) 7(18.9) 51(35.2) 7(18.9) 51(34.7) 2 66(35.9) 21(56.8) 45(31.0) 20(54.1) 46(31.3) 3 47(25.5) 6(16.2) 40(27.6) 7(18.9) 40(27.2) 4 13(7.1) 3(8.1) 9(6.2) 3(8.1) 10(6.8) Anastomotic fistula 0.680 1.00 Yes(%) 28(15.2) 7(18.9) 21(14.5) 6(16.2) 22(15.0) No(%) 156(84.8) 30(81.1) 124(85.5) 31(83.8) 125(85.0) Pathology 1.00 0.498 Squamous cell carcinomas 166(90.2) 34(91.9) 131(90.3) 32(86.5) 134(91.2) Adenocarcinoma 12(6.5) 2(5.4) 9(6.2) 3(8.1) 9(6.1) b Others 6(3.3) 1(2.7) 5(3.4) 2(5.4) 4(2.7) Lung function FEV1/FVC, median (IQR) 87.39 ( 78.81–93.14 ) 88.15(78.38–93.88) 87.32(78.935–93.035) 0.739 87.57(79.853–93.367) 87.385(78.408–93.142) 0.816 PEFR,mean ± SD 4.98 ± 1.88 4.88 ± 2.07 5.01 ± 1.85 0.780 5.16 ± 1.53 4.93 ± 1.97 0.510 Meas/Pred PEFR (%),mean ± SD 74.347 ± 25.686 73.704 ± 27.91 74.567 ± 25.335 0.885 80.861 ± 23.263 72.576 ± 26.13 0.131 Alb(g/L), median (IQR) 42.85 ( 39.8–44.8 ) 43.2(39.8–44.4) 42.8(39.8–44.8) 0.870 40.7(36.6–44.2) 43.3(40.55–44.85) 0.005 Postoperative complication(II/III,IV) 112(60.9)/72(39.1) 27(73.0)/10(27.0) 83(57.2)/62(42.8) 0.119 19(51.4)/18(48.6) 93(63.3)/54(36.7) 0.255 HGB(g/L), median (IQR) 129 ( 114–137 ) 131(114–137) 128(115–137) 0.694 125(108–135) 129(116–138) 0.113 NLR, median (IQR) 2.38 ( 1.67–3.4 ) 2.44(1.75–3.69) 2.25(1.61–3.32) 0.275 2.37(1.89–3.3) 2.41(1.63–3.41) 0.684 APACHE II, median (IQR) 18.5 ( 15–22 ) 18(14–21) 19(16–22) 0.339 18(16–21) 19(15–22) 0.659 IQR: Interquartile range; BMI: body mass index; cTNM: is based on American Joint Committee on Cancer Pathological Tumor-Node-Metastasis Staging System, 8th Edition; dOthers: comprising 4 cases of small cell carcinoma, 1 case of mucinous adenocarcinoma, and 1 case of suppurative inflammation; FEV1/FVC: FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEFR: Peak Expiratory Flow Rate; Meas/Pred PEFR (%): Percentage of Measured/Predicted Peak Expiratory Flow Rate; Alb: albumin; HGB: Hemoglobin; APACHE Ⅱ: Acute Physiology and Chronic Health Evaluation Ⅱ. 4.2 Consistency Analysis between T4-PMI and L1-SMI Pearson correlation analysis of the baseline levels of T4-PMI and L1-SMI showed a moderate positive correlation (r = 0.657, P < 0.001). The reliability analysis results showed that the original Cronbach's α coefficient of T4-PMI and L1-SMI was 0.734, and the standardized α coefficient was 0.793; according to the reliability evaluation standard, α coefficient ≥ 0.7 indicates that the internal consistency of the two reaches an acceptable level. However, the results of Bland-Altman consistency analysis showed that the mean difference between T4-PMI and L1-SMI was − 2.509, indicating a significant systematic bias in the measurement results of the two indicators (T4-PMI was overall lower than L1-SMI); its 95% limits of agreement (LoA) was [-3.621, -1.398], which exceeded the clinically acceptable error limit (Fig. 3 ); further proportional bias analysis by Pittman variance difference test showed P > 0.05, and there was no proportional bias between the two. In summary, although T4-PMI and L1-SMI have a certain correlation, their consistency is poor, and they cannot be directly used interchangeably in clinical practice. 4.3 Correlation Analysis between Sarcopenia and Early Prognosis of Radical Esophagectomy To evaluate the impact of sarcopenia on early postoperative outcomes, the Mann-Whitney U test was used to compare the differences in four indicators (ICU mechanical ventilation time, ICU length of stay, total length of stay, and total hospitalization costs) between sarcopenia and non-sarcopenia patients in T4 and L1 groupings (Table 2 ). The results showed that there were significant statistical differences in all the above early prognostic indicators between the sarcopenia group and the corresponding non-sarcopenia group defined by L1-SMI (P 0.05). Table 2 Comparison of Early Postoperative Outcome Indicators in Sarcopenia Patients Stratified by L1 Skeletal Muscle Index and T4 Pectoralis Muscle Index Characteristic Patients(n = 184) T4 (n = 182) L1 (n = 184) T4-Sarcopenia(n = 37;20.3%) T4-NonSarcopenia(n = 145;79.7%) P value L1-Sarcopenia(n = 37;20.1%) L1-NonSarcopenia(n = 147;79.9%) P value DMV (h), median (IQR) 90.20(46.88–133.10) 89.40(42.00-135.50) 90.80(53.60–131.00) 0.859 99.90(64.00-160.70) 85.80(44.65-118.75) 0.024 LOS-ICU (d), median (IQR) 8.00(6.00–10.00) 8.00(6.00–11.00) 8.00(6.00–10.00) 0.493 8.00(7.00–12.00) 7.00(6.00–9.00) 0.001 TLOS (d), median (IQR) 22.50(19.00–29.00) 20.00(18.00–29.00) 23.00(20.00–29.00) 0.071 27.00(20.00–34.00) 22.00(19.00-28.50) 0.047 Total_cost(RMB), median (IQR) 74624.86(66541.77–86771.00) 79165.94(66672.23-84697.91) 74445.19(66609.45-87770.80) 0.672 79480.89(72309.59-99952.73) 73490.91(65745.24-85919.68) 0.018 IQR: Interquartile range; DMV (h): Duration of Mechanical Ventilation (hours); LOS-ICU (d): Length of Stay-ICU (day); TLOS (d): Total Length of Stay (day); Total_cost (RMB): Total hospitalization costs (RMB). 4.4 Survival analysis Survival analysis was performed using the Kaplan-Meier method to draw survival curves, and the Log-Rank test was used to compare the survival differences between patients in different sarcopenia groups (Fig. 4 ). The results showed that the median survival time of patients in the L1-sarcopenia group was significantly shorter than that in the L1-nonSarcopenia group (Log-Rank χ²=4.753, P = 0.029). In the T4 level grouping, the difference in survival curves between the T4-sarcopenia group and the T4-nonSarcopenia group did not reach statistical significance (Log-Rank χ²=3.567, P = 0.059). Univariate Cox proportional hazards regression analysis showed (Table 3 ) that the percentage of measured/predicted peak expiratory flow rate (PEFR measured/predicted, %), pathological type (adenocarcinoma, other types), T4-sarcopenia, and L1-sarcopenia were all associated with adverse survival outcomes (P < 0.1). Further multivariate Cox proportional hazards regression analysis showed that, in addition to pathology (other types) (HR = 7.346, 95% CI: 1.949–27.688, P = 0.003), sarcopenia defined by L1-SMI was an independent risk factor for long-term survival (HR = 2.297, 95% CI: 1.085–4.863, P = 0.030). In addition, Measured/predicted PEFR (%) was a protective factor affecting patient survival (HR = 0.984, 95%CI: 0.970 ~ 0.997, P = 0.018), suggesting that preoperative improvement of patients' lung function may help optimize survival outcomes (Fig. 5 ). Table 3 Univariate and multivariate analyses according to overall survival Variables Univariate Multivariate HR (95% CI ) P value HR (95% CI ) P value Gender, male 0.673 (0.336–1.348) 0.264 Age 1.004 (0.967–1.042) 0.848 Anastomotic fistula 1.160 (0.563–2.390) 0.688 APACHE II 0.981 (0.926–1.039) 0.509 Albumin 0.947 (0.884–1.013) 0.114 HGB 0.996 (0.982–1.010) 0.544 BMI 0.934 (0.860–1.014) 0.103 Postoperative complications(> II) 1.083 (0.611–1.920) 0.785 FEV1/FVC 0.991(0.967–1.016) 0.471 Measured/predicted PEFR(%) 0.988 (0.976–1.001) 0.068 0.984 (0.970–0.997) 0.018 Pathology(Adenocarcinoma) 2.938(1.313–6.577) 0.009 1.781 (0.535–5.926) 0.347 Pathology( a Others) 3.152 (0.969–10.250) 0.056 7.346 (1.949–27.688) 0.003 T4-Sarcopenia 1.788 (0.970–3.295) 0.063 1.134 (0.527–2.442) 0.748 L1-Sarcopenia 1.971 (1.058–3.674) 0.033 2.297 (1.085–4.863) 0.030 HR: Hazard ratio; APACHE Ⅱ: Acute Physiology and Chronic Health Evaluation Ⅱ; Alb: albumin; HGB, Hemoglobin; BMI, body mass index; FEV1/FVC: FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEFR: Peak Expiratory Flow Rate; Meas/Pred PEFR (%): Percentage of Measured/Predicted Peak Expiratory Flow Rate; eOthers: comprising 4 cases of small cell carcinoma, 1 case of mucinous adenocarcinoma, and 1 case of suppurative inflammation. 5. Discussion This study explored the impact of T4-PMI and L1-SMI on adverse postoperative outcomes in esophageal cancer patients. The main findings are as follows: Sarcopenia diagnosed based on L1-SMI was significantly associated with prolonged mechanical ventilation time, increased length of stay, higher hospitalization costs after radical esophagectomy, and was closely related to poor overall survival (OS) of patients, which could increase the risk of death by more than 2 times; while sarcopenia diagnosed based on T4-PMI had no significant statistical association with the above postoperative adverse outcomes and survival rate. Previous studies have shown that muscle area needs to be corrected by body size parameters (such as body mass index, height squared) to improve assessment accuracy [ 13 ] . Given the inherent correlation between muscle mass and body size, this study used body mass index to correct the T4 pectoralis muscle area and L1 skeletal muscle cross-sectional area respectively to assess sarcopenia. Although relevant studies in Europe and the United States have proposed the cutoff value of L1-SMI [ 14 ] , considering the ethnic differences and age-related changes in Asian populations, this study still used the method of some Asian clinical studies and selected 20% of the gender-specific muscle measurement value as the cutoff value [ 15 ] . The results showed that the overall survival rate of sarcopenia at both T4 and L1 levels was poor, which was similar to the conclusion reported by Uzair M. Jogiat et al. [ 16 ] Multiple studies have confirmed the application value of quantitative pectoralis muscle indicators in sarcopenia assessment: Sung Woo Moon et al. [ 7 ] proposed that quantitative pectoralis muscle mass can be used for the assessment and diagnosis of sarcopenia; Changbo Sun et al. [ 17 ] found that pectoralis muscle index can optimize the postoperative risk stratification of non-small cell lung cancer patients; Annarita Pecchi et al. [ 8 ] reported that in breast cancer patients, pectoralis muscle mass can be used to estimate the total body skeletal muscle mass. Based on the above studies, theoretically, T4-PMI may replace the traditional L3-SMI for the assessment of sarcopenia in esophageal cancer patients. However, the results of this study showed that sarcopenia assessed by L1-SMI was significantly associated with prolonged mechanical ventilation time, increased length of stay, higher hospitalization costs, and poor overall survival rate after radical esophagectomy; while T4-PMI had a general correlation with L1-SMI and poor internal consistency, and had no significant association with the above postoperative adverse outcomes, and the association between sarcopenia diagnosed based on T4-PMI and overall survival rate did not reach statistical significance, which was consistent with the research results of Sanders KJC et al. [ 9 ] . From the perspective of anatomical and physiological mechanisms, the pectoralis muscle at the T4 level mainly includes the pectoralis major and pectoralis minor muscles, which are mainly composed of fast-twitch muscle fibers (type II) [ 18 ] . Their main functions are upper limb movement and auxiliary breathing, and they belong to "activity-dependent" muscle groups. Their metabolism is mainly regulated by neuromuscular activity, and the impact of nutritional factors is relatively secondary. Therefore, even with normal nutritional status, immobilization or reduced activity can lead to rapid atrophy of the pectoralis muscle; on the contrary, even with mild malnutrition, regular resistance training can still maintain the volume of the pectoralis muscle. This "activity-dominated" regulatory mode makes it difficult for T4-PMI to accurately reflect the overall nutritional reserve.Unlike the pectoralis muscle at the T4 level, the skeletal muscle at the L1 level, like the skeletal muscle at the traditional gold standard L3 level, belongs to the core trunk muscle group, mainly including the psoas major, erector spinae, etc., which are mainly composed of slow-twitch muscle fibers (type I) [ 19 ] . Their main physiological function is to maintain spinal stability and support body posture, and they belong to "resting metabolic" muscle groups. At the pathophysiological level, their protein synthesis and decomposition processes are directly regulated by the overall nutritional status: when nutrition is sufficient, the body prioritizes maintaining the protein reserve of the core muscle group through insulin-like growth factor-1 (IGF-1) [ 20 ] and amino acid signaling pathways induced by protein intake to ensure the basic functions of the body; when nutrition is insufficient, the body initiates muscle catabolic pathways such as the ubiquitin-proteasome pathway and autophagy pathway to prioritize decomposing the protein of the core muscle group for energy supply [ 19 , 21 ] . Due to the large volume and abundant protein reserve of such muscle groups, they are the "core nutrient reservoir" for the body to cope with energy crises. Therefore, changes in L1-SMI can directly reflect the state of global nutritional and metabolic imbalance. Previous studies have confirmed that the baseline levels of L1-SMI and L3-SMI are strongly correlated (Pearson r = 0.90, Cronbach’s α = 0.859). The pathophysiological basis of this strong correlation is the consistent regulation of global nutritional metabolism [ 9 ] . Based on this, L1-SMI can stably reflect the patient's nutritional reserve status, while T4-PMI is significantly interfered by the activity status and is not suitable as a reliable indicator for nutritional assessment and postoperative prognosis prediction in esophageal cancer patients. Although BMI has been widely used in daily life and clinical practice, it is only a rough indicator for assessing body composition and cannot accurately reflect visceral obesity [ 22 ] . Relevant studies have confirmed that BMI lacks sensitivity in identifying obesity. Skeletal muscle atrophy is a prominent feature of patients with advanced malignant tumors. Some obese patients with high BMI may present with sarcopenic obesity characterized by skeletal muscle loss and increased fat mass [ 23 , 24 ] . In our study, the proportion of patients with BMI ≥ 25 kg/m² in the T4 sarcopenia group was higher than that in the T4 non-sarcopenia group (P 0.1). Obviously, compared with BMI, muscle quality and quantity have higher predictive value for patient mortality. The results of the study showed that in the L1 grouping, the average age of the sarcopenia group was significantly higher than that of the non-sarcopenia group, and the preoperative albumin level was significantly lower (both P < 0.05), which was consistent with the research results of Changbo Sun [ 13 , 25 ] , suggesting that with the increase of age, the prevalence of sarcopenia in esophageal cancer patients increases significantly, and effective preoperative intervention and treatment (such as nutritional support and correction of hypoalbuminemia) may prevent or delay the development of sarcopenia. Sarcopenia reflects the loss of systemic muscle mass and decreased muscle function [ 15 ] . Therefore, sarcopenia patients may have respiratory muscle dysfunction to a certain extent. Peak expiratory flow rate (PEFR) is determined by respiratory muscle strength and is considered a useful parameter reflecting respiratory muscle strength. The results of this study showed that Measured/predicted PEFR is a protective factor for the postoperative survival outcome of esophageal cancer patients, suggesting that improving lung function may help optimize the survival prognosis of patients, which is consistent with the research conclusions of Sung Woo Moon et al. [ 26 , 27 ] . In this study, univariate Cox proportional hazards regression analysis found that pathology (adenocarcinoma) and pathology (other types) were associated with adverse survival outcomes (P < 0.1). Further multivariate Cox regression analysis confirmed that the pathological type of "other" (mainly small cell carcinoma) was an independent poor prognostic factor, which was consistent with previous clinical research results [ 28 ] . However, it should be noted that the sample size of the "other pathological types" subgroup in this study was only 6 cases, accounting for 3.3% of the total sample size, resulting in an extremely wide 95% confidence interval (CI) for the HR value of this subgroup, and the results may have potential bias, which needs to be verified by clinical studies with larger sample sizes. 6. Limitations of the Study First, this study is a single-center retrospective study, and the external validity of the results is limited, which needs to be further verified by large-scale, prospective studies based on Asian populations. Second, this study only included esophageal cancer patients who underwent radical resection, and the distribution of sarcopenia in patients with different pathological stages needs to be further explored. In addition, this study excluded some cases with incomplete clinical data, which may introduce selection bias. At present, there is no consensus on the optimal cutoff values and determination methods of T4-PMI and L1-SMI in Asian populations, and more large-sample clinical studies are needed to optimize them. Finally, the patient follow-up time was limited to 3 years, and there was a lack of 5-year or longer-term overall survival data, which may reduce the representativeness of survival outcome assessment. 7. Conclusions In conclusion, preoperative sarcopenia assessed by L1-SMI is significantly associated with prolonged mechanical ventilation time and adverse survival outcomes in patients after radical esophagectomy. In the future, further studies are needed to explore the clinical effects of longitudinal nutritional intervention or exercise intervention in patients at high risk of sarcopenia, so as to provide new strategies for improving the postoperative prognosis of esophageal cancer patients. Declarations Corresponding author Correspondence to Linlin Zhang. Ethics declarations Ethical approval The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Consent to participate This study was conducted with the approval of the Ethics Committee of Anhui Provincial Cancer Hospital (approval number: 2026-LLYJ-WZ-0004), and the research procedures followed the recommendations of the Declaration of Helsinki.As this was a retrospective study that did not involve the disclosure of patient privacy and posed minimal risk, the requirement for informed consent was waived by the ethics committee. Competing interests The authors declare no competing interests. Funding This research was funded by the 2025 Youth Research Fund of Anhui Provincial Cancer Hospital, grant number 2025YJQN004. Author Contribution (I) Conception and design: Peishuang Wang; (II) Administrative support: Linlin Zhang; (III) Provision of study materials or patients: Juan Zhang, Zhan Zhang; (IV) Collection and assembly of data: Peishuang Wang,Haiou Zhou, Tingting Wang, Kunfeng Sang, Xiaobing Wang, Meng Ling, Xiaoyue Cui, Xiaoxue Zha, Chuanbin Wang; (V) Data analysis and interpretation: Peishuang Wang, Chuanbin Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Acknowledgements Not applicable. References Alfonso J, Cruz-Jentoft Gülistan, Bahat Jürgen, Bauer, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601. https://pubmed.ncbi.nlm.nih.gov/30312372/ . Shachar SS, Williams GR, Hyman B, Muss, et al. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur J Cancer. 2016;57:58–67. https://pubmed.ncbi.nlm.nih.gov/26882087/ . Rohit G, Ganju R, Morse A, Hoover, et al. The impact of sarcopenia on tolerance of radiation and outcome in patients with head and neck cancer receiving chemoradiation. Radiother Oncol. 2019;137:117–24. https://pubmed.ncbi.nlm.nih.gov/31085391/ . Frédéric Pamoukdjian T, Bouillet V, Lévy, et al. Prevalence and predictive value of pre-therapeutic sarcopenia in cancer patients: A systematic review. Clin Nutr. 2018;37(4):1101–13. https://pubmed.ncbi.nlm.nih.gov/28734552/ . Bianca Bignotti A, Cadoni C, Martinoli, et al. Imaging of skeletal muscle in vitamin D deficiency. World J Radiol. 2014;6(4):119–24. https://pubmed.ncbi.nlm.nih.gov/24778774/ . Annarita Pecchi F, Valoriani RC, Costantini, et al. Role of Body Composition in Patients with Resectable Pancreatic Cancer. Nutrients. 2024;16(12):1834. https://pubmed.ncbi.nlm.nih.gov/38931189/ . Moon SW, Lee SH, Woo A, et al. Reference values of skeletal muscle area for diagnosis of sarcopenia using chest computed tomography in Asian general population. J Cachexia Sarcopenia Muscle. 2022;13(2):955–65. https://pubmed.ncbi.nlm.nih.gov/35170229/ . Annarita Pecchi F, Mogavero S, Zanni, et al. Role of Pectoralis Muscle Analysis in Breast Magnetic Resonance Imaging for Body Composition Evaluation Before and After Neoadjuvant Chemotherapy for Breast Cancer. Nutrients. 2025;17(10):1698. https://pubmed.ncbi.nlm.nih.gov/40431438/ . Karin JC, Sanders, Juliette HRJ, Degens, Anne-Marie C, Dingemans, et al. 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. Int J Chron Obstruct Pulmon Dis. 2019;14:781–9. https://pubmed.ncbi.nlm.nih.gov/31040657/ . Changbo S, Anraku M, Karasaki T, et al. Low truncal muscle area on chest computed tomography: a poor prognostic factor for the cure of early-stage non-small-cell lung cancer. Eur J Cardiothorac Surg. 2019;55(3):414–20. https://pubmed.ncbi.nlm.nih.gov/30289481/ . Brian A, Derstine SA, Holcombe, Brian E, Ross, et al. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018;8(1):11369. https://pubmed.ncbi.nlm.nih.gov/30054580/ . Thomas W, Rice H, Ishwaran, Mark K, Ferguson, et al. Cancer of the esophagus and esophagogastric junction: An eighth edition staging primer. J Thorac Oncol. 2017;12(1):36–42. https://pubmed.ncbi.nlm.nih.gov/27810391/ . Changbo S, Anraku M, Kawahara T, et al. Prognostic significance of low pectoralis muscle mass on preoperative chest computed tomography in localized non-small cell lung cancer after curative intent surgery. Lung Cancer. 2020;147:71–6. https://pubmed.ncbi.nlm.nih.gov/32673829/ . Brian A, Derstine SA, Holcombe, Brian E, Ross, et al. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018;8(1):11369. https://pubmed.ncbi.nlm.nih.gov/30054580/ . Alfonso J, Cruz-Jentoft JP, Baeyens, Jürgen M, Bauer, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–23. https://pubmed.ncbi.nlm.nih.gov/20392703/ . Uzair M, Jogiat A, Bédard V, Baracos, et al. Thoracic muscle mass predicts survival among patients with locally advanced esophageal cancer. Clin Nutr. 2025;49:90–7. https://pubmed.ncbi.nlm.nih.gov/40253811/ . Changbo S, Hirata Y, Kawahara T, et al. Diagnosis of Respiratory Sarcopenia for Stratifying Postoperative Risk in Non-Small Cell Lung Cancer. JAMA Surg. 2025;160(1):66–73. https://pubmed.ncbi.nlm.nih.gov/39475952/ . Santos MD, Bezprozvannaya S, McAnally JR, et al. A mechanistic basis of fast myofiber vulnerability to neuromuscular diseases. Cell Rep. 2025;44(7):115959. https://pubmed.ncbi.nlm.nih.gov/40632651/ . Minghong Leng F, Yang J, Zhao, et al. Mitophagy-mediated S1P facilitates muscle adaptive responses to endurance exercise through SPHK1-S1PR1/S1PR2 in slow-twitch myofibers. Autophagy. 2025;21(10):2111–29. https://pubmed.ncbi.nlm.nih.gov/40181214/ . Eduardo DS, Freitas KA, Kras, Lori R, Roust, et al. Lower Muscle Protein Synthesis in Humans with Obesity Concurrent with Lower Expression of Muscle IGF-1 Splice Variants. Obes (Silver Spring). 2023;31(11):2689–98. https://pubmed.ncbi.nlm.nih.gov/37840435/ . XiangSheng Pang P, Zhang XP, Chen, et al. Ubiquitin-proteasome pathway in skeletal muscle atrophy. Front Physiol. 2023;14:1289537. https://pubmed.ncbi.nlm.nih.gov/38046952/ . Jason Rai ET, Pring K, Knight, et al. Sarcopenia is independently associated with poor preoperative physical fitness in patients undergoing colorectal cancer surgery. J Cachexia Sarcopenia Muscle. 2024;15(5):1850–7. https://pubmed.ncbi.nlm.nih.gov/38925534/ . Dónal M, McSweeney S, Raby G, Radhakrishna, et al. Low muscle mass measured at T12 is a prognostic biomarker in unresectable oesophageal cancers receiving chemoradiotherapy. Radiother Oncol. 2023;186:109764. https://pubmed.ncbi.nlm.nih.gov/37385375/ . Leo R, Brown M, Soupashi MS, Yule, et al. Comparison Between Single- and Multi-slice Computed Tomography Body Composition Analysis in Patients With Oesophagogastric Cancer. J Cachexia Sarcopenia Muscle. 2025;16(1):e13673. https://pubmed.ncbi.nlm.nih.gov/39723572/ . Park J-S, Colby M, Seyfi D, et al. Sarcopenia impacts perioperative and survival outcomes after esophagectomy for cancer: a multicenter study. J Gastrointest Surg. 2024;28(6):805–12. https://pubmed.ncbi.nlm.nih.gov/38548573/ . Changbo S, Anraku M, Kawahara T, et al. Respiratory strength and pectoralis muscle mass as measures of sarcopenia: Relation to outcomes in resected non–small cell lung cancer. J Thorac Cardiovasc Surg. 2022;163(3):779–e7872. https://pubmed.ncbi.nlm.nih.gov/33317785/ . Zhang M, Xiong Y, Chen M, et al. Psoas muscle mass index and peak expiratory flow as measures of sarcopenia: relation to outcomes of elderly patients with resectable esophageal cancer. Front Oncol. 2023;13:1303877. https://pubmed.ncbi.nlm.nih.gov/38090498/ . Wu H-X, Chen Y-K, Wang Y-N, et al. Dissecting small cell carcinoma of the esophagus ecosystem by single-cell transcriptomic analysis. Mol Cancer. 2025;24(1):142. https://pubmed.ncbi.nlm.nih.gov/40375239/ . Additional Declarations No competing interests reported. Supplementary Files rawdata.xls rawimage.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 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-9139366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624926890,"identity":"30a538b6-ac1a-4ee3-af6f-a78bbbfed99d","order_by":0,"name":"Peishuang Wang","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Peishuang","middleName":"","lastName":"Wang","suffix":""},{"id":624926891,"identity":"77c6eb41-b7f4-495b-a677-b3efd89d425e","order_by":1,"name":"Juan Zhang","email":"","orcid":"","institution":"University of Science and Technology of 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14:54:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9139366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9139366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107621883,"identity":"07a58254-0b6e-4dc7-9554-7573812b00d7","added_by":"auto","created_at":"2026-04-23 09:44:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103731,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient inclusion and exclusion criteria.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/b7051509b28818e3273eb757.jpg"},{"id":107707367,"identity":"495e2773-42ec-4375-a43f-2107f5a54dea","added_by":"auto","created_at":"2026-04-24 09:20:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88556,"visible":true,"origin":"","legend":"\u003cp\u003eSkeletal muscle area on transverse computed tomography images at (a) pectoralis, (b) first lumbar level.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/d7fc56fcf791a477e685b566.jpg"},{"id":107706183,"identity":"076338a7-21af-4e6a-a9f0-52f85b47658d","added_by":"auto","created_at":"2026-04-24 09:17:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78084,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Pearson correlation analysis of baseline levels of T4-PMI and L1-SMI showed a moderate positive correlation (r=0.657, P\u0026lt;0.001); (b) Bland–Altman plots show differences between T4-PMI and L1-SMI. The upper and lower dashed horizontal lines represent the upper and lower limits of the 95% confidence interval, and the solid middle line represents the average of the differences.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/a717de58bce2bb232f96c63e.jpg"},{"id":107621887,"identity":"efe26f29-5a7c-48d9-b66e-f790c6a4193e","added_by":"auto","created_at":"2026-04-23 09:44:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91095,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves and Log-Rank test: (a) The median survival time of the L1-sarcopenia group was significantly shorter than that of the non-sarcopenia group (χ²=4.753, P=0.029); (b)no statistically significant difference in survival was observed between the T4-sarcopenia and non-sarcopenia groups (χ²=3.567, P=0.059).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/f4c038a0b682ece95c7d85b8.jpg"},{"id":107621888,"identity":"b3964c97-5ac0-42d8-b3eb-d9bbb8005df1","added_by":"auto","created_at":"2026-04-23 09:44:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60408,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariate Cox proportional hazards regression analysis: L1-sarcopenia was an independent risk factor for survival (HR=2.297, 95%CI: 1.085–4.863, P=0.030); measured/predicted PEFR (%) was a protective factor (HR=0.984, 95%CI: 0.970–0.997, P=0.018).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/ce52d46101bbe0a7b847d70a.jpg"},{"id":107869370,"identity":"0ac85ae0-88c8-44c3-b78b-d6c46e4ab631","added_by":"auto","created_at":"2026-04-27 07:36:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/afcec946-2d8f-45d8-9cfc-1e2c6a7e7aca.pdf"},{"id":107621884,"identity":"0e2283f3-8175-407a-95b8-452c504bf799","added_by":"auto","created_at":"2026-04-23 09:44:58","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":274432,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.xls","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/17de2ff4e6cb4a1066158dd6.xls"},{"id":107621886,"identity":"d3e25964-bb11-4bba-853b-d6d491f2ce01","added_by":"auto","created_at":"2026-04-23 09:44:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1063746,"visible":true,"origin":"","legend":"","description":"","filename":"rawimage.docx","url":"https://assets-eu.researchsquare.com/files/rs-9139366/v1/8af34ea7eadafb173bef3631.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Prognostic Value of L1-SMI and T4-PMI in Esophageal Cancer Radical Resection","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSarcopenia refers to the widespread and progressive loss of skeletal muscle function and mass, reflecting the physical function of malignant tumor patients in a state of potential frailty \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Multiple studies have confirmed that sarcopenia is closely associated with poor postoperative prognosis in malignant tumor patients, which is specifically manifested as prolonged mechanical ventilation time, increased incidence of postoperative complications, prolonged ICU and total length of stay, increased risk of death, as well as decreased tolerance to radiotherapy and chemotherapy and increased adverse reactions \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Computed tomography (CT) is currently an important assessment tool for the local precise quantification of skeletal muscle, and the skeletal muscle area measured by CT at the third lumbar vertebra (L3) level is regarded as the gold standard for sarcopenia assessment. However, in Chinese clinical practice, esophageal cancer patients only routinely undergo chest CT examination before surgery, and the scanning range usually only covers up to the first lumbar vertebra (L1) level, making it impossible to obtain imaging data at the L3 level, thus making it difficult to use the L3 skeletal muscle area for preoperative sarcopenia assessment.\u003c/p\u003e \u003cp\u003eThe pectoralis muscle at the fourth thoracic vertebra (T4) level and the skeletal muscle at the L1 level can both be clearly displayed by chest CT, and measuring their cross-sectional area (CSA) does not require additional radiation exposure. Studies by Annarita Pecchi and other scholars have confirmed that quantitative indicators of pectoralis muscle mass can be used for the assessment and diagnosis of sarcopenia, and in breast cancer patients, pectoralis muscle measurement results can also be used to estimate the total body skeletal muscle mass \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In addition, for non-small cell lung cancer patients undergoing tumor treatment, the analysis of skeletal muscle mass at the L1 level based on routine chest CT scans is considered a better alternative to the L3 level, and the trunk skeletal muscle group at the L1 level is significantly associated with poor survival rate \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. A CT analysis based on the US healthy population further showed that the skeletal muscle cross-sectional area at the L1 and L3 levels has a good correlation, and the study also proposed a skeletal muscle cutoff value for sarcopenia applicable to the L1 level \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, there are no relevant research reports at home and abroad on the impact of T4-PMI or L1-SMI on adverse postoperative outcomes in esophageal cancer patients. This study intends to compare and analyze the differences between T4-PMI and L1-SMI, use them as sarcopenia assessment indicators, and for the first time explore their impact on mechanical ventilation time and survival outcomes after radical esophagectomy, in order to screen out a more suitable sarcopenia indicator for evaluating the prognosis of patients undergoing radical esophagectomy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Objects and Data Collection\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e This study protocol was approved by the Ethics Committee of Anhui Cancer Hospital (approval number: 2026-LLYJ-WZ-0004). Based on the retrospective design of this study, the requirement for written informed consent from patients was waived after review by the Ethics Committee. All procedures of the study strictly followed the principles and regulations of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eThis retrospective study consecutively enrolled patients with clinical TNM stage Ⅰ-Ⅳ esophageal cancer who underwent radical esophagectomy in our hospital between January 2023 and March 2025 (Fig.\u0026nbsp;1), and conducted a retrospective analysis of prospectively collected and standardized clinical data. Chest CT images and other relevant examination and test data within 3 weeks before surgery were collected and sorted out. The specific clinical data are as follows: ① Demographic characteristics: gender, age, body mass index (BMI); ② Preoperative lung function indicators: forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC), peak expiratory flow rate (PEFR), percentage of measured/predicted peak expiratory flow rate (Meas/Pred PEFR, %); ③ Preoperative peripheral blood laboratory indicators: albumin, hemoglobin; ④ Pathological data: histological type and pathological stage determined according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e; ⑤ Surgical method: all patients underwent combined laparoscopic and open surgery; ⑥ Early postoperative assessment: Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) within 24 hours after transfer to the intensive care unit (ICU); ⑦ Postoperative outcome indicators: postoperative complications based on Clavien-Dindo classification (grade Ⅱ and above), postoperative mechanical ventilation time after transfer to ICU, ICU length of stay, total length of stay, total hospitalization costs, and postoperative survival time (defined as the time from the date of surgery to the time of death from any cause or the last follow-up time).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CT Image Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eChest CT scan images used for tumor diagnosis and treatment plan formulation within 2 weeks before surgery were selected for body composition analysis. CT examination was performed using a 64-slice spiral CT scanner with a 5 mm slice thickness. Axial CT images in DICOM format were exported from the hospital electronic medical record system, and Slice-O-Matic software v5.0 (Tomovision, Montreal, Quebec, Canada) was used for cross-sectional muscle area measurement and analysis.\u003c/p\u003e \u003cp\u003eThe measurement levels and indicator definitions are as follows: ① T4 level measurement: on the cross-sectional image at the level of the transverse process of the fourth thoracic vertebra, identify and measure the CSA of the bilateral pectoralis major and pectoralis minor muscles, with the unit of cm\u0026sup2;; for the T4 level, since the position of the arms can lead to significant differences in chest wall muscle tissue, images with arms placed at the side of the body are excluded. ② L1 level measurement: on the cross-sectional image at the level of the first lumbar vertebra where the bilateral vertebral transverse processes are clearly displayed, measure the cross-sectional area of all skeletal muscles, including psoas major, erector spinae, quadratus lumborum, transversus abdominis, external oblique abdominis, internal oblique abdominis, and rectus abdominis.\u003c/p\u003e \u003cp\u003eMuscle tissue quantification: A preset Hounsfield unit threshold was used for tissue definition, where the threshold range for skeletal muscle was \u0026minus;\u0026thinsp;29 to 150 HU, based on which the target muscle area was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Manual correction was performed when the image boundary was blurred. All measurements were independently completed by two researchers who had received unified training and were unaware of the patients' clinical data. Finally, the average of the two measurement results was used for subsequent analysis. The muscle cross-sectional area measured at each level was normalized by dividing by the patient's body mass index (BMI) to calculate T4-PMI and L1-SMI, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sarcopenia Grouping Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDue to the different distributions of skeletal muscle index between men and women, patients were divided into sarcopenia group and non-sarcopenia group according to the gender-specific cutoff values of the lowest quintile (T4: male 1.29, female 0.75; L1: male 3.81, female 2.66) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Finally, in the T4 grouping: 37 cases in the sarcopenia group and 145 cases in the non-sarcopenia group; in the L1 grouping: 37 cases in the sarcopenia group and 147 cases in the non-sarcopenia group.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Statistical Analysis","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll statistical analyses in this study were performed using R language (version 4.4.3) in the RStudio environment. Baseline data analysis: categorical variables were expressed as frequency (percentage) [n (%)], and intergroup comparison was performed using χ\u0026sup2; test or Fisher's exact test (when the theoretical frequency\u0026thinsp;\u0026lt;\u0026thinsp;5). For continuous variables, those conforming to normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and intergroup comparison was performed using independent samples t-test; those not conforming to normal distribution were expressed as median (interquartile range) [median (IQR)], and intergroup comparison was performed using Mann-Whitney U test. Correlation and consistency analysis: Pearson correlation coefficient was used to evaluate the linear correlation strength between T4-PMI and L1-SMI; the internal consistency between the two was tested by Cronbach's α coefficient, which ranges from 0 to 1, and the closer to 1, the better the consistency. At the same time, a Bland-Altman plot was drawn to evaluate the fixed bias between the two: the mean of T4-PMI and L1-SMI was taken as the X-axis, and the percentage of the difference between the two was taken as the Y-axis to analyze whether the difference showed a constant trend; proportional bias was evaluated by Pittman variance difference test. Survival analysis: Follow-up time was calculated from the date of surgery to the patient's death, last follow-up, or cutoff date (November 23, 2025). Survival curves were drawn using the Kaplan-Meier method, and intergroup comparison was performed using the log-rank test. The Cox proportional hazards regression model was used for prognostic factor analysis: first, all potential factors were included in univariate analysis, variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included in the multivariate model, variables were screened by backward stepwise regression, and finally the hazard ratio (HR) and 95% confidence interval (CI) of each independent risk factor were calculated. The Schoenfeld residual test was used to test the Cox proportional hazards assumption.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Baseline Clinicopathological Characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 632 consecutive patients who underwent radical esophagectomy were enrolled in this study, and the study population was finally determined after strict screening. The exclusion criteria and number of excluded cases are as follows: 409 patients who were not transferred to the intensive care unit (ICU) after surgery; 35 patients who lacked preoperative chest computed tomography (CT) images or complete clinical data; 4 patients with severe postoperative pneumothorax and subcutaneous emphysema due to severe intraoperative thoracic adhesions. In addition, 2 patients were excluded only from pectoralis muscle index-related analysis due to poor CT scan quality (Fig.\u0026nbsp;1). The final study population included 184 patients, of which 135 were male (73.4%) and 49 were female (26.6%), with a median age of 74 years. The distribution of clinical TNM stages: 58 cases (31.5%) of 0-Ⅰ stage, 66 cases (35.9%) of Ⅱ stage, 47 cases (25.5%) of Ⅲ stage, and 13 cases (7.1%) of Ⅳ stage; among histological types, 166 cases (90.2%) were squamous cell carcinoma. The median overall survival time of all patients was 14 months, with a follow-up time range of 1 to 35 months (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also presents the comparison of baseline characteristics between sarcopenia and non-sarcopenia patients based on T4 and L1 groupings. The results showed that the proportion of patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; in the T4 sarcopenia group was significantly higher than that in the T4 non-sarcopenia group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the L1 grouping, the average age of the sarcopenia group was significantly higher than that of the non-sarcopenia group, and the preoperative albumin level was significantly lower (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences in age and preoperative albumin level between the two groups in the T4 grouping (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and characteristics of patients with or without sarcopenia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003ePatients(n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eT4 (n\u0026thinsp;=\u0026thinsp;182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eL1 (n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT4-Sarcopenia(n\u0026thinsp;=\u0026thinsp;37;20.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT4-NonSarcopenia(n\u0026thinsp;=\u0026thinsp;145;79.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL1-Sarcopenia(n\u0026thinsp;=\u0026thinsp;37;20.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL1-NonSarcopenia(n\u0026thinsp;=\u0026thinsp;147;79.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106(73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e108(73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e49(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10(27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39(26.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e74(70-77.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74(69\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(70\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75(73\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73(69-76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e) ,mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e23.007\u0026thinsp;\u0026plusmn;\u0026thinsp;3.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2),category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalweight(18.5\u0026thinsp;\u0026minus;\u0026thinsp;22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e75(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60(40.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight(23\u0026thinsp;\u0026minus;\u0026thinsp;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e36(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27(18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity(\u0026ge;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e57(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45(30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003ecTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e58(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e66(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46(31.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e47(25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40(27.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e13(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10(6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnastomotic fistula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e28(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e156(84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124(85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31(83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125(85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinomas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e166(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131(90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e134(91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9(6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e87.39 ( 78.81\u0026ndash;93.14 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.15(78.38\u0026ndash;93.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.32(78.935\u0026ndash;93.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.57(79.853\u0026ndash;93.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.385(78.408\u0026ndash;93.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEFR,mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeas/Pred PEFR (%),mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e74.347\u0026thinsp;\u0026plusmn;\u0026thinsp;25.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.704\u0026thinsp;\u0026plusmn;\u0026thinsp;27.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.567\u0026thinsp;\u0026plusmn;\u0026thinsp;25.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.861\u0026thinsp;\u0026plusmn;\u0026thinsp;23.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.576\u0026thinsp;\u0026plusmn;\u0026thinsp;26.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb(g/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e42.85 ( 39.8\u0026ndash;44.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.2(39.8\u0026ndash;44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.8(39.8\u0026ndash;44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.7(36.6\u0026ndash;44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.3(40.55\u0026ndash;44.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complication(II/III,IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e112(60.9)/72(39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(73.0)/10(27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83(57.2)/62(42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19(51.4)/18(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93(63.3)/54(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB(g/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e129 ( 114\u0026ndash;137 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(114\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128(115\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125(108\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e129(116\u0026ndash;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.38 ( 1.67\u0026ndash;3.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44(1.75\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25(1.61\u0026ndash;3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.37(1.89\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.41(1.63\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18.5 ( 15\u0026ndash;22 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(14\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(16\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18(16\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19(15\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIQR: Interquartile range; BMI: body mass index; cTNM: is based on American Joint Committee on Cancer Pathological Tumor-Node-Metastasis Staging System, 8th Edition; dOthers: comprising 4 cases of small cell carcinoma, 1 case of mucinous adenocarcinoma, and 1 case of suppurative inflammation; FEV1/FVC: FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEFR: Peak Expiratory Flow Rate; Meas/Pred PEFR (%): Percentage of Measured/Predicted Peak Expiratory Flow Rate; Alb: albumin; HGB: Hemoglobin; APACHE Ⅱ: Acute Physiology and Chronic Health Evaluation Ⅱ.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Consistency Analysis between T4-PMI and L1-SMI\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePearson correlation analysis of the baseline levels of T4-PMI and L1-SMI showed a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.657, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The reliability analysis results showed that the original Cronbach's α coefficient of T4-PMI and L1-SMI was 0.734, and the standardized α coefficient was 0.793; according to the reliability evaluation standard, α coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.7 indicates that the internal consistency of the two reaches an acceptable level. However, the results of Bland-Altman consistency analysis showed that the mean difference between T4-PMI and L1-SMI was \u0026minus;\u0026thinsp;2.509, indicating a significant systematic bias in the measurement results of the two indicators (T4-PMI was overall lower than L1-SMI); its 95% limits of agreement (LoA) was [-3.621, -1.398], which exceeded the clinically acceptable error limit (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e); further proportional bias analysis by Pittman variance difference test showed P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and there was no proportional bias between the two. In summary, although T4-PMI and L1-SMI have a certain correlation, their consistency is poor, and they cannot be directly used interchangeably in clinical practice.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Correlation Analysis between Sarcopenia and Early Prognosis of Radical Esophagectomy\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo evaluate the impact of sarcopenia on early postoperative outcomes, the Mann-Whitney U test was used to compare the differences in four indicators (ICU mechanical ventilation time, ICU length of stay, total length of stay, and total hospitalization costs) between sarcopenia and non-sarcopenia patients in T4 and L1 groupings (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that there were significant statistical differences in all the above early prognostic indicators between the sarcopenia group and the corresponding non-sarcopenia group defined by L1-SMI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the grouping based on T4-PMI, there were no statistically significant differences in various indicators between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Early Postoperative Outcome Indicators in Sarcopenia Patients Stratified by L1 Skeletal Muscle Index and T4 Pectoralis Muscle Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatients(n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eT4 (n\u0026thinsp;=\u0026thinsp;182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eL1 (n\u0026thinsp;=\u0026thinsp;184)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT4-Sarcopenia(n\u0026thinsp;=\u0026thinsp;37;20.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eT4-NonSarcopenia(n\u0026thinsp;=\u0026thinsp;145;79.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL1-Sarcopenia(n\u0026thinsp;=\u0026thinsp;37;20.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eL1-NonSarcopenia(n\u0026thinsp;=\u0026thinsp;147;79.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMV (h), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.20(46.88\u0026ndash;133.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e89.40(42.00-135.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.80(53.60\u0026ndash;131.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.90(64.00-160.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.80(44.65-118.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS-ICU (d), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.00(6.00\u0026ndash;10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.00(6.00\u0026ndash;11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.00(6.00\u0026ndash;10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.00(7.00\u0026ndash;12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.00(6.00\u0026ndash;9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLOS (d), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.50(19.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.00(18.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.00(20.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.00(20.00\u0026ndash;34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.00(19.00-28.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal_cost(RMB), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74624.86(66541.77\u0026ndash;86771.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e79165.94(66672.23-84697.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74445.19(66609.45-87770.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79480.89(72309.59-99952.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73490.91(65745.24-85919.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIQR: Interquartile range; DMV (h): Duration of Mechanical Ventilation (hours); LOS-ICU (d): Length of Stay-ICU (day); TLOS (d): Total Length of Stay (day); Total_cost (RMB): Total hospitalization costs (RMB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Survival analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSurvival analysis was performed using the Kaplan-Meier method to draw survival curves, and the Log-Rank test was used to compare the survival differences between patients in different sarcopenia groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed that the median survival time of patients in the L1-sarcopenia group was significantly shorter than that in the L1-nonSarcopenia group (Log-Rank χ\u0026sup2;=4.753, P\u0026thinsp;=\u0026thinsp;0.029). In the T4 level grouping, the difference in survival curves between the T4-sarcopenia group and the T4-nonSarcopenia group did not reach statistical significance (Log-Rank χ\u0026sup2;=3.567, P\u0026thinsp;=\u0026thinsp;0.059).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUnivariate Cox proportional hazards regression analysis showed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that the percentage of measured/predicted peak expiratory flow rate (PEFR measured/predicted, %), pathological type (adenocarcinoma, other types), T4-sarcopenia, and L1-sarcopenia were all associated with adverse survival outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Further multivariate Cox proportional hazards regression analysis showed that, in addition to pathology (other types) (HR\u0026thinsp;=\u0026thinsp;7.346, 95% CI: 1.949\u0026ndash;27.688, P\u0026thinsp;=\u0026thinsp;0.003), sarcopenia defined by L1-SMI was an independent risk factor for long-term survival (HR\u0026thinsp;=\u0026thinsp;2.297, 95% CI: 1.085\u0026ndash;4.863, P\u0026thinsp;=\u0026thinsp;0.030). In addition, Measured/predicted PEFR (%) was a protective factor affecting patient survival (HR\u0026thinsp;=\u0026thinsp;0.984, 95%CI: 0.970\u0026thinsp;~\u0026thinsp;0.997, P\u0026thinsp;=\u0026thinsp;0.018), suggesting that preoperative improvement of patients' lung function may help optimize survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses according to overall survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.673 (0.336\u0026ndash;1.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004 (0.967\u0026ndash;1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnastomotic fistula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.160 (0.563\u0026ndash;2.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.981 (0.926\u0026ndash;1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.947 (0.884\u0026ndash;1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.996 (0.982\u0026ndash;1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.934 (0.860\u0026ndash;1.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complications(\u0026gt;\u0026thinsp;II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.083 (0.611\u0026ndash;1.920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991(0.967\u0026ndash;1.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasured/predicted PEFR(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988 (0.976\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984 (0.970\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology(Adenocarcinoma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.938(1.313\u0026ndash;6.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.781 (0.535\u0026ndash;5.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology(\u003csup\u003ea\u003c/sup\u003eOthers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.152 (0.969\u0026ndash;10.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.346 (1.949\u0026ndash;27.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4-Sarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.788 (0.970\u0026ndash;3.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.134 (0.527\u0026ndash;2.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1-Sarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.971 (1.058\u0026ndash;3.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.297 (1.085\u0026ndash;4.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHR: Hazard ratio; APACHE Ⅱ: Acute Physiology and Chronic Health Evaluation Ⅱ; Alb: albumin; HGB, Hemoglobin; BMI, body mass index; FEV1/FVC: FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEFR: Peak Expiratory Flow Rate; Meas/Pred PEFR (%): Percentage of Measured/Predicted Peak Expiratory Flow Rate; eOthers: comprising 4 cases of small cell carcinoma, 1 case of mucinous adenocarcinoma, and 1 case of suppurative inflammation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study explored the impact of T4-PMI and L1-SMI on adverse postoperative outcomes in esophageal cancer patients. The main findings are as follows: Sarcopenia diagnosed based on L1-SMI was significantly associated with prolonged mechanical ventilation time, increased length of stay, higher hospitalization costs after radical esophagectomy, and was closely related to poor overall survival (OS) of patients, which could increase the risk of death by more than 2 times; while sarcopenia diagnosed based on T4-PMI had no significant statistical association with the above postoperative adverse outcomes and survival rate.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that muscle area needs to be corrected by body size parameters (such as body mass index, height squared) to improve assessment accuracy \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Given the inherent correlation between muscle mass and body size, this study used body mass index to correct the T4 pectoralis muscle area and L1 skeletal muscle cross-sectional area respectively to assess sarcopenia. Although relevant studies in Europe and the United States have proposed the cutoff value of L1-SMI \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, considering the ethnic differences and age-related changes in Asian populations, this study still used the method of some Asian clinical studies and selected 20% of the gender-specific muscle measurement value as the cutoff value \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The results showed that the overall survival rate of sarcopenia at both T4 and L1 levels was poor, which was similar to the conclusion reported by Uzair M. Jogiat et al. \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMultiple studies have confirmed the application value of quantitative pectoralis muscle indicators in sarcopenia assessment: Sung Woo Moon et al. \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e proposed that quantitative pectoralis muscle mass can be used for the assessment and diagnosis of sarcopenia; Changbo Sun et al. \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e found that pectoralis muscle index can optimize the postoperative risk stratification of non-small cell lung cancer patients; Annarita Pecchi et al. \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e reported that in breast cancer patients, pectoralis muscle mass can be used to estimate the total body skeletal muscle mass. Based on the above studies, theoretically, T4-PMI may replace the traditional L3-SMI for the assessment of sarcopenia in esophageal cancer patients. However, the results of this study showed that sarcopenia assessed by L1-SMI was significantly associated with prolonged mechanical ventilation time, increased length of stay, higher hospitalization costs, and poor overall survival rate after radical esophagectomy; while T4-PMI had a general correlation with L1-SMI and poor internal consistency, and had no significant association with the above postoperative adverse outcomes, and the association between sarcopenia diagnosed based on T4-PMI and overall survival rate did not reach statistical significance, which was consistent with the research results of Sanders KJC et al. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom the perspective of anatomical and physiological mechanisms, the pectoralis muscle at the T4 level mainly includes the pectoralis major and pectoralis minor muscles, which are mainly composed of fast-twitch muscle fibers (type II) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Their main functions are upper limb movement and auxiliary breathing, and they belong to \"activity-dependent\" muscle groups. Their metabolism is mainly regulated by neuromuscular activity, and the impact of nutritional factors is relatively secondary. Therefore, even with normal nutritional status, immobilization or reduced activity can lead to rapid atrophy of the pectoralis muscle; on the contrary, even with mild malnutrition, regular resistance training can still maintain the volume of the pectoralis muscle. This \"activity-dominated\" regulatory mode makes it difficult for T4-PMI to accurately reflect the overall nutritional reserve.Unlike the pectoralis muscle at the T4 level, the skeletal muscle at the L1 level, like the skeletal muscle at the traditional gold standard L3 level, belongs to the core trunk muscle group, mainly including the psoas major, erector spinae, etc., which are mainly composed of slow-twitch muscle fibers (type I) \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Their main physiological function is to maintain spinal stability and support body posture, and they belong to \"resting metabolic\" muscle groups. At the pathophysiological level, their protein synthesis and decomposition processes are directly regulated by the overall nutritional status: when nutrition is sufficient, the body prioritizes maintaining the protein reserve of the core muscle group through insulin-like growth factor-1 (IGF-1) \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and amino acid signaling pathways induced by protein intake to ensure the basic functions of the body; when nutrition is insufficient, the body initiates muscle catabolic pathways such as the ubiquitin-proteasome pathway and autophagy pathway to prioritize decomposing the protein of the core muscle group for energy supply \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Due to the large volume and abundant protein reserve of such muscle groups, they are the \"core nutrient reservoir\" for the body to cope with energy crises. Therefore, changes in L1-SMI can directly reflect the state of global nutritional and metabolic imbalance.\u003c/p\u003e \u003cp\u003ePrevious studies have confirmed that the baseline levels of L1-SMI and L3-SMI are strongly correlated (Pearson r\u0026thinsp;=\u0026thinsp;0.90, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.859). The pathophysiological basis of this strong correlation is the consistent regulation of global nutritional metabolism \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Based on this, L1-SMI can stably reflect the patient's nutritional reserve status, while T4-PMI is significantly interfered by the activity status and is not suitable as a reliable indicator for nutritional assessment and postoperative prognosis prediction in esophageal cancer patients.\u003c/p\u003e \u003cp\u003eAlthough BMI has been widely used in daily life and clinical practice, it is only a rough indicator for assessing body composition and cannot accurately reflect visceral obesity \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Relevant studies have confirmed that BMI lacks sensitivity in identifying obesity. Skeletal muscle atrophy is a prominent feature of patients with advanced malignant tumors. Some obese patients with high BMI may present with sarcopenic obesity characterized by skeletal muscle loss and increased fat mass \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In our study, the proportion of patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; in the T4 sarcopenia group was higher than that in the T4 non-sarcopenia group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but further analysis showed that BMI was not associated with adverse survival outcomes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1). Obviously, compared with BMI, muscle quality and quantity have higher predictive value for patient mortality.\u003c/p\u003e \u003cp\u003eThe results of the study showed that in the L1 grouping, the average age of the sarcopenia group was significantly higher than that of the non-sarcopenia group, and the preoperative albumin level was significantly lower (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which was consistent with the research results of Changbo Sun \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, suggesting that with the increase of age, the prevalence of sarcopenia in esophageal cancer patients increases significantly, and effective preoperative intervention and treatment (such as nutritional support and correction of hypoalbuminemia) may prevent or delay the development of sarcopenia.\u003c/p\u003e \u003cp\u003eSarcopenia reflects the loss of systemic muscle mass and decreased muscle function \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, sarcopenia patients may have respiratory muscle dysfunction to a certain extent. Peak expiratory flow rate (PEFR) is determined by respiratory muscle strength and is considered a useful parameter reflecting respiratory muscle strength. The results of this study showed that Measured/predicted PEFR is a protective factor for the postoperative survival outcome of esophageal cancer patients, suggesting that improving lung function may help optimize the survival prognosis of patients, which is consistent with the research conclusions of Sung Woo Moon et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, univariate Cox proportional hazards regression analysis found that pathology (adenocarcinoma) and pathology (other types) were associated with adverse survival outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Further multivariate Cox regression analysis confirmed that the pathological type of \"other\" (mainly small cell carcinoma) was an independent poor prognostic factor, which was consistent with previous clinical research results \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. However, it should be noted that the sample size of the \"other pathological types\" subgroup in this study was only 6 cases, accounting for 3.3% of the total sample size, resulting in an extremely wide 95% confidence interval (CI) for the HR value of this subgroup, and the results may have potential bias, which needs to be verified by clinical studies with larger sample sizes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"6. Limitations of the Study","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFirst, this study is a single-center retrospective study, and the external validity of the results is limited, which needs to be further verified by large-scale, prospective studies based on Asian populations. Second, this study only included esophageal cancer patients who underwent radical resection, and the distribution of sarcopenia in patients with different pathological stages needs to be further explored. In addition, this study excluded some cases with incomplete clinical data, which may introduce selection bias. At present, there is no consensus on the optimal cutoff values and determination methods of T4-PMI and L1-SMI in Asian populations, and more large-sample clinical studies are needed to optimize them. Finally, the patient follow-up time was limited to 3 years, and there was a lack of 5-year or longer-term overall survival data, which may reduce the representativeness of survival outcome assessment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn conclusion, preoperative sarcopenia assessed by L1-SMI is significantly associated with prolonged mechanical ventilation time and adverse survival outcomes in patients after radical esophagectomy. In the future, further studies are needed to explore the clinical effects of longitudinal nutritional intervention or exercise intervention in patients at high risk of sarcopenia, so as to provide new strategies for improving the postoperative prognosis of esophageal cancer patients.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eCorresponding author\u003c/strong\u003e \u003cp\u003eCorrespondence to Linlin Zhang.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eEthics declarations\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted with the approval of the Ethics Committee of Anhui Provincial Cancer Hospital (approval number: 2026-LLYJ-WZ-0004), and the research procedures followed the recommendations of the Declaration of Helsinki.As this was a retrospective study that did not involve the disclosure of patient privacy and posed minimal risk, the requirement for informed consent was waived by the ethics committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the 2025 Youth Research Fund of Anhui Provincial Cancer Hospital, grant number 2025YJQN004.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e(I) Conception and design: Peishuang Wang; (II) Administrative support: Linlin Zhang; (III) Provision of study materials or patients: Juan Zhang, Zhan Zhang; (IV) Collection and assembly of data: Peishuang Wang,Haiou Zhou, Tingting Wang, Kunfeng Sang, Xiaobing Wang, Meng Ling, Xiaoyue Cui, Xiaoxue Zha, Chuanbin Wang; (V) Data analysis and interpretation: Peishuang Wang, Chuanbin Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlfonso J, Cruz-Jentoft G\u0026uuml;listan, Bahat J\u0026uuml;rgen, Bauer, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30312372/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30312372/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShachar SS, Williams GR, Hyman B, Muss, et al. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur J Cancer. 2016;57:58\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/26882087/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/26882087/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohit G, Ganju R, Morse A, Hoover, et al. The impact of sarcopenia on tolerance of radiation and outcome in patients with head and neck cancer receiving chemoradiation. Radiother Oncol. 2019;137:117\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/31085391/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/31085391/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFr\u0026eacute;d\u0026eacute;ric Pamoukdjian T, Bouillet V, L\u0026eacute;vy, et al. Prevalence and predictive value of pre-therapeutic sarcopenia in cancer patients: A systematic review. Clin Nutr. 2018;37(4):1101\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/28734552/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/28734552/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianca Bignotti A, Cadoni C, Martinoli, et al. Imaging of skeletal muscle in vitamin D deficiency. World J Radiol. 2014;6(4):119\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24778774/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/24778774/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnarita Pecchi F, Valoriani RC, Costantini, et al. Role of Body Composition in Patients with Resectable Pancreatic Cancer. Nutrients. 2024;16(12):1834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38931189/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38931189/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon SW, Lee SH, Woo A, et al. Reference values of skeletal muscle area for diagnosis of sarcopenia using chest computed tomography in Asian general population. J Cachexia Sarcopenia Muscle. 2022;13(2):955\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35170229/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35170229/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnarita Pecchi F, Mogavero S, Zanni, et al. Role of Pectoralis Muscle Analysis in Breast Magnetic Resonance Imaging for Body Composition Evaluation Before and After Neoadjuvant Chemotherapy for Breast Cancer. Nutrients. 2025;17(10):1698. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40431438/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40431438/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarin JC, Sanders, Juliette HRJ, Degens, Anne-Marie C, Dingemans, et al. 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. Int J Chron Obstruct Pulmon Dis. 2019;14:781\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/31040657/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/31040657/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChangbo S, Anraku M, Karasaki T, et al. Low truncal muscle area on chest computed tomography: a poor prognostic factor for the cure of early-stage non-small-cell lung cancer. Eur J Cardiothorac Surg. 2019;55(3):414\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30289481/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30289481/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrian A, Derstine SA, Holcombe, Brian E, Ross, et al. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018;8(1):11369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30054580/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30054580/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas W, Rice H, Ishwaran, Mark K, Ferguson, et al. Cancer of the esophagus and esophagogastric junction: An eighth edition staging primer. J Thorac Oncol. 2017;12(1):36\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27810391/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27810391/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChangbo S, Anraku M, Kawahara T, et al. Prognostic significance of low pectoralis muscle mass on preoperative chest computed tomography in localized non-small cell lung cancer after curative intent surgery. Lung Cancer. 2020;147:71\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/32673829/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/32673829/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrian A, Derstine SA, Holcombe, Brian E, Ross, et al. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018;8(1):11369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30054580/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30054580/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfonso J, Cruz-Jentoft JP, Baeyens, J\u0026uuml;rgen M, Bauer, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20392703/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20392703/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUzair M, Jogiat A, B\u0026eacute;dard V, Baracos, et al. Thoracic muscle mass predicts survival among patients with locally advanced esophageal cancer. Clin Nutr. 2025;49:90\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40253811/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40253811/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChangbo S, Hirata Y, Kawahara T, et al. Diagnosis of Respiratory Sarcopenia for Stratifying Postoperative Risk in Non-Small Cell Lung Cancer. JAMA Surg. 2025;160(1):66\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/39475952/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/39475952/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos MD, Bezprozvannaya S, McAnally JR, et al. A mechanistic basis of fast myofiber vulnerability to neuromuscular diseases. Cell Rep. 2025;44(7):115959. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40632651/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40632651/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinghong Leng F, Yang J, Zhao, et al. Mitophagy-mediated S1P facilitates muscle adaptive responses to endurance exercise through SPHK1-S1PR1/S1PR2 in slow-twitch myofibers. Autophagy. 2025;21(10):2111\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40181214/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40181214/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEduardo DS, Freitas KA, Kras, Lori R, Roust, et al. Lower Muscle Protein Synthesis in Humans with Obesity Concurrent with Lower Expression of Muscle IGF-1 Splice Variants. Obes (Silver Spring). 2023;31(11):2689\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/37840435/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/37840435/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiangSheng Pang P, Zhang XP, Chen, et al. Ubiquitin-proteasome pathway in skeletal muscle atrophy. Front Physiol. 2023;14:1289537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38046952/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38046952/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJason Rai ET, Pring K, Knight, et al. Sarcopenia is independently associated with poor preoperative physical fitness in patients undergoing colorectal cancer surgery. J Cachexia Sarcopenia Muscle. 2024;15(5):1850\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38925534/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38925534/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026oacute;nal M, McSweeney S, Raby G, Radhakrishna, et al. Low muscle mass measured at T12 is a prognostic biomarker in unresectable oesophageal cancers receiving chemoradiotherapy. Radiother Oncol. 2023;186:109764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/37385375/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/37385375/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeo R, Brown M, Soupashi MS, Yule, et al. Comparison Between Single- and Multi-slice Computed Tomography Body Composition Analysis in Patients With Oesophagogastric Cancer. J Cachexia Sarcopenia Muscle. 2025;16(1):e13673. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/39723572/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/39723572/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark J-S, Colby M, Seyfi D, et al. Sarcopenia impacts perioperative and survival outcomes after esophagectomy for cancer: a multicenter study. J Gastrointest Surg. 2024;28(6):805\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38548573/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38548573/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChangbo S, Anraku M, Kawahara T, et al. Respiratory strength and pectoralis muscle mass as measures of sarcopenia: Relation to outcomes in resected non\u0026ndash;small cell lung cancer. J Thorac Cardiovasc Surg. 2022;163(3):779\u0026ndash;e7872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33317785/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33317785/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Xiong Y, Chen M, et al. Psoas muscle mass index and peak expiratory flow as measures of sarcopenia: relation to outcomes of elderly patients with resectable esophageal cancer. Front Oncol. 2023;13:1303877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/38090498/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/38090498/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H-X, Chen Y-K, Wang Y-N, et al. Dissecting small cell carcinoma of the esophagus ecosystem by single-cell transcriptomic analysis. Mol Cancer. 2025;24(1):142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40375239/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40375239/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Skeletal Muscle Index, Pectoralis Muscle Index, Esophageal Cancer, Sarcopenia","lastPublishedDoi":"10.21203/rs.3.rs-9139366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9139366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSarcopenia is increasingly recognized as a predictor of poor postoperative outcomes in cancer patients. However, the comparative value of pectoralis muscle index at the T4 level (T4-PMI) and skeletal muscle index at the L1 level (L1-SMI) in assessing sarcopenia and predicting outcomes in esophageal cancer remains unclear. This study aimed to compare the associations of T4-PMI and L1-SMI with postoperative mechanical ventilation and long-term survival following radical esophagectomy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 184 patients who underwent radical esophagectomy between 2023 and 2025. Preoperative CT images were used to measure T4-PMI and L1-SMI, and sarcopenia was defined based on gender-specific fifth-percentile cutoffs. Primary outcomes included duration of ICU mechanical ventilation, ICU and hospital length of stay (LOS), hospitalization costs, and overall survival. Cox regression models were used to evaluate survival predictors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe cohort was predominantly male (73.4%) with a median age of 74 years. T4-PMI and L1-SMI were moderately correlated (r\u0026thinsp;=\u0026thinsp;0.657) but demonstrated poor agreement, suggesting they are not interchangeable. T4-sarcopenia was not associated with any postoperative outcomes. In contrast, L1-sarcopenia was linked to longer mechanical ventilation, increased ICU and hospital LOS, and higher costs (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis identified L1-sarcopenia (HR\u0026thinsp;=\u0026thinsp;2.297, P\u0026thinsp;=\u0026thinsp;0.030) and pathological type (other types, HR\u0026thinsp;=\u0026thinsp;7.346, P\u0026thinsp;=\u0026thinsp;0.003) as independent predictors of poor survival. Higher measured/predicted PEFR (%) was a protective factor (HR\u0026thinsp;=\u0026thinsp;0.984, P\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eL1-SMI is a more robust predictor than T4-PMI of prolonged mechanical ventilation and poor survival in esophageal cancer patients. It may serve as a clinically useful marker to guide perioperative risk assessment and management.\u003c/p\u003e","manuscriptTitle":"Comparative Prognostic Value of L1-SMI and T4-PMI in Esophageal Cancer Radical Resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:44:54","doi":"10.21203/rs.3.rs-9139366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T15:54:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210418614460127469411510494797003359005","date":"2026-04-24T01:01:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T06:58:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T11:54:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T08:48:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T08:47:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-16T14:36:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65aacd3b-3559-43ec-84bb-a67cdbf257bc","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T15:54:27+00:00","index":67,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:44:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:44:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9139366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9139366","identity":"rs-9139366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}