A Nomogram for Predicting Postoperative Anastomotic Leakage in Esophageal Cancer Patients After Esophagectomy: Development and Validation

A Nomogram for Predicting Postoperative Anastomotic Leakage in Esophageal Cancer Patients After Esophagectomy: Development and Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Nomogram for Predicting Postoperative Anastomotic Leakage in Esophageal Cancer Patients After Esophagectomy: Development and Validation Ruonan Tan, Lili Guo, Weiran Huang, Qian Ba, Hang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7660216/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Esophageal cancer is a prevalent malignancy, particularly in East Asia, with high morbidity and mortality rates. Postoperative anastomotic leakage (AL) is a major complication after esophagectomy, impacting recovery and prognosis. Early identification of high-risk patients is critical. Objectives To develop and validate a predictive nomogram for postoperative AL risk using LASSO-logistic regression to identify independent risk factors. Methods A retrospective cohort study was conducted on 850 esophageal cancer patients who underwent esophagectomy. Clinical data were collected, including variables such as hypertension, C-reactive protein (CRP), operation time, lymphocyte-to-monocyte ratio (LMR), and tumor location. LASSO regression was used for variable selection, followed by multivariate logistic regression to identify independent risk factors. A nomogram was developed and validated in a separate cohort. Results Six independent risk factors for AL were identified: hypertension, neoadjuvant therapy, CRP, operation time, LMR, and tumor location. The nomogram showed good performance, with an AUC of 0.820 in the training cohort and 0.786 in the validation cohort, indicating strong discrimination. Calibration curves confirmed good agreement between predicted and observed outcomes. Conclusions The nomogram provides an effective and reliable tool for early risk stratification and individualized management of esophageal cancer patients at high risk for postoperative AL. Anastomotic Leakage Esophageal Cancer Nomogram LASSO Regression Predictive Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Esophageal cancer is one of the most common malignant tumors worldwide, with high morbidity and mortality, particularly in East Asia [ 1 ]. Esophagectomy remains the cornerstone of curative treatment for resectable esophageal cancer [ 2 ]. However, this complex procedure is associated with a high incidence of postoperative complications, among which anastomotic leakage (AL) is one of the most devastating [ 3 – 4 ]. The incidence of AL has been reported to vary significantly, contributing to increased morbidity, prolonged hospitalization, higher costs, and even mortality [ 5 ]. Moreover, AL adversely affects postoperative quality of life and long-term oncologic outcomes. Therefore, early identification of patients at high risk of AL is of great clinical significance [ 6 ]. Numerous factors have been implicated in the development of AL, including patient comorbidities, nutritional and inflammatory status, tumor characteristics, and perioperative variables [ 7 ]. Several predictive models have been proposed, but many have important limitations. Some models rely on single-center data without external validation, while others incorporate parameters that are difficult to obtain in routine clinical practice, limiting their applicability [ 8 ]. Furthermore, traditional statistical approaches may fail to handle collinearity and high-dimensional data effectively. The least absolute shrinkage and selection operator (LASSO) regression method has emerged as a powerful tool for variable selection in the presence of multicollinearity, enabling the construction of parsimonious yet robust predictive models [ 9 ]. Incorporating LASSO with multivariate logistic regression allows for the development of accurate nomograms that can provide individualized risk assessment [ 10 ]. In this study, we retrospectively analyzed clinical data from patients undergoing esophagectomy at our institution to develop and validate a predictive nomogram for postoperative AL. By applying LASSO-logistic regression, we aimed to identify independent risk factors, construct a practical model, and evaluate its discrimination, calibration, and clinical utility. The goal was to establish a simple and reliable tool to assist clinicians in early risk stratification and individualized management of patients with esophageal cancer. 2. Materials and Methods 2.1 Study design and patients This retrospective cohort study included patients with esophageal cancer who underwent esophagectomy at our hospital between January 2015 and May 2025. The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee. Written informed consent was obtained from all patients. A total of 885 patients were initially identified. Exclusion criteria were: (1) history of prior cancer surgery or other malignant tumors (n = 23); (2) incomplete clinical data (n = 6); and (3) emergency operations due to intestinal obstruction or hemorrhage (n = 6). Finally, 850 patients met the inclusion and exclusion criteria (Fig. 1 ). These patients were randomly divided into a training cohort (n = 680) and a validation cohort (n = 170) at a ratio of 8:2. 2.2 Data collection Baseline clinicopathological characteristics and laboratory parameters were collected from medical records, including age, sex, BMI, comorbidities (hypertension, diabetes, lacunar infarction), tumor stage (T, N, M), tumor location, histological type, neoadjuvant therapy, neuroaggression, anastomotic site, operation duration, intraoperative blood loss, albumin, hemoglobin, C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and CALLY index. The primary endpoint was postoperative anastomotic leakage (AL), defined according to the International Study Group of Rectal Cancer criteria, confirmed by clinical manifestations and imaging or endoscopic findings. 2.3 Variable selection using LASSO regression To reduce the risk of multicollinearity and prevent overfitting, the least absolute shrinkage and selection operator (LASSO) logistic regression model was applied to the training cohort. Twenty-three candidate variables were initially included. The optimal penalty parameter λ was determined using ten-fold cross-validation with the minimum criteria. 2.4 Logistic regression analysis Variables with nonzero coefficients in the LASSO model were subsequently entered into multivariate logistic regression to identify independent predictors of AL. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Variables with P < 0.05 were considered statistically significant. 2.5 Construction of the nomogram Based on the independent predictors identified in multivariate logistic regression, a nomogram model was constructed to estimate the probability of postoperative AL. Each variable was assigned a weighted score proportional to its regression coefficient, and the sum of all scores corresponded to the predicted probability. 2.6 Validation and performance assessment The predictive performance of the nomogram was evaluated in both the training and validation cohorts. Model discrimination was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC). Calibration was evaluated by calibration plots, Hosmer–Lemeshow (H-L) goodness-of-fit test, and Brier score. Internal validation was performed using bootstrap resampling (1,000 repetitions). 2.7 Clinical utility Decision curve analysis (DCA) was performed to assess the clinical usefulness of the nomogram by quantifying the net benefit across a range of threshold probabilities. 2.8 Statistical analysis All statistical analyses were performed using R software (version 4.3.1). LASSO regression was conducted using the “glmnet” package, and the nomogram was constructed using the “rms” package. ROC curves were generated with the “pROC” package, calibration curves with the “rms” package, and DCA with the “rmda” package. A two-sided P < 0.05 was considered statistically significant. 3. Results 3.1 Case selection and baseline clinical characteristics A total of 885 patients with esophageal cancer who underwent esophagectomy at our hospital between January 2015 and May 2025 were initially screened. After excluding 23 patients with a history of prior malignancy or cancer surgery, 6 patients with incomplete data, and 6 patients who underwent emergency operations, 850 patients were enrolled, including 170 in the AL group and 680 in the N-AL group (Fig. 1 ). Baseline clinical characteristics are summarized in Table 1 . No significant differences were observed in age, sex, BMI, diabetes, tumor stage, neoadjuvant therapy, tumor location, histological type, or anastomotic site between the two groups (all P > 0.05). However, hypertension was more common in the AL group (37.4% vs. 27.6%, P = 0.047). Inflammatory and nutritional indices also differed significantly: the AL group had lower LMR (3.52 ± 1.60 vs. 4.49 ± 1.98, P < 0.001) and CALLY scores (3.73 ± 8.51 vs. 7.63 ± 11.89, P < 0.001), but higher CRP levels (17.83 ± 33.52 vs. 6.16 ± 13.74 mg/L, P < 0.001) and NLR (4.16 ± 5.54 vs. 2.66 ± 3.84, P = 0.007). In addition, operative time was significantly longer in the AL group (349.63 ± 113.75 vs. 312.88 ± 95.90 min, P = 0.002), while hemoglobin levels were lower (127.27 ± 23.46 vs. 132.45 ± 17.11 g/L, P = 0.027). Table 1 Baseline clinical characteristics of the AL and N-AL groups in the training set. Factors Level AL group (n = 115) N-AL group (n = 565) p-value Age, n (%) 0.940 < 60 18(15.7) 93(16.5) ≥ 60 97(84.3) 472(83.5) Sex, n (%) 0.090 Female 18(15.7) 132(23.4) Male 97(84.3) 433(76.6) Body Mass Index(BMI)/༈kg/m²༉ 0.245 BMI<24 69(60.0) 374(66.2) BMI ≥ 24 46(40.0) 191(33.8) Hypertension 0.047 Yes 43(37.4) 156(27.6) No 72(62.6) 409(72.4) Diabetes Yes 9(7.8) 42(7.4) 0.847 No 106(92.2) 523(92.6) T (tumor invasion, %) 0.335 T1-T2 47(40.9) 274(48.5) T3-T4 68(59.1) 318(51.5) N (regional lymph node, %) 0.303 N0-N1 102(88.7) 477(84.4) N2-N3 13(11.3) 88(15.6) M(metastasis,, n %) 0.098 M0 112(97.4) 561(99.3) M1 3(2.6) 4(0.7) Neoadjuvant therapy, n (%) 0.276 Yes 9(7.8) 67(11.9) No 106(92.2) 498(88.1) Lacunar Infarction, n (%) 0.111 Yes 21(18.3) 69(12.2) No 94(81.7) 496(87.8) Neuroaggression, n (%) 0.276 Yes 35(30.4) 205(36.3) No 80(69.6) 360(63.7) Tumor location, n (%) 0.077 Thoracic 79(68.7) 435(77.0) Abdomen 36(31.3) 130(23.0) Histological tumor type, n (%) 0.898 Squamous cell carcinoma 97(84.3) 469(83.0) Adenocarcinoma 14(12.2) 77(13.6) Other types 4(3.5) 19(3.4) Anastomotic site, n (%) 0.521 Neck 41(35.7) 195(34.5) Thoracic 70(60.9) 335(59.3) Abdomen 4(3.4) 35(6.2) Albumin, n(%) / (g/L) 0.086 < 35 15(13.0) 43(7.6) ≥ 35 100(87.0) 522(92.4) CALLY 3.73 ± 8.51 7.63 ± 11.89 < 0.001 Neutrophil-to-Lymphocyte Ratio (NLR) 4.16 ± 5.54 2.66 ± 3.84 0.007 C-reactive protein (CRP), mg/L ± SD 17.83 ± 33.52 6.16 ± 13.74 < 0.001 Operation duration, min ± SD 349.63 ± 113.75 312.88 ± 95.90 0.002 Blood loss, mL ± SD 264.43 ± 211.06 270.02 ± 280.77 0.809 Hemoglobin, mg/L ± SD 127.27 ± 23.46 132.45 ± 17.11 0.027 PLR 202.32 ± 339.46 140.62 ± 68.85 0.056 LMR 3.52 ± 1.60 4.49 ± 1.98 < 0.001 3.2 Identification of independent risk factors LASSO-logistic regression analysis was performed to minimize collinearity among variables (Fig. 2 ). When the penalty parameter λ was chosen via ten-fold cross-validation (λ = 0.0273), six variables with nonzero coefficients were retained: hypertension, neoadjuvant therapy, CRP, operation time, LMR, and tumor location. These variables were subsequently entered into multivariate logistic regression (Table 2 ). The results showed that neoadjuvant therapy (OR = 0.395, 95% CI: 0.170–0.916, P = 0.030) and higher LMR (OR = 0.843, 95% CI: 0.719–0.987, P = 0.034) were protective factors. In contrast, hypertension (OR = 1.751, 95% CI: 1.090–2.811, P = 0.020), elevated CRP (OR = 1.020, 95% CI: 1.009–1.030, P < 0.001), longer operation duration (OR = 1.005, 95% CI: 1.002–1.007, P < 0.001), and tumor location (OR = 1.977, 95% CI: 1.234–3.167, P = 0.005) were independent risk factors for AL. Table 2 Logistic regression analysis Influencing factors B SE Wald P OR 95%CI Neoadjuvant therapy -0.929 0.429 4.686 0.030 0.395 0.170 ~ 0.916 CRP 0.020 0.005 14.310 0.000 1.020 1.009 ~ 1.030 Operation duration 0.005 0.001 15.830 0.000 1.005 1.002 ~ 1.007 Hypertension 0.560 0.242 5.370 0.020 1.751 1.090 ~ 2.811 LMR -0.171 0.081 4.485 0.034 0.843 0.719 ~ 0.987 Tumor location 0.682 0.240 8.045 0.005 1.977 1.234 ~ 3.167 3.3 Construction of the nomogram Based on the six independent predictors, a nomogram was developed to predict the individual probability of postoperative AL (Fig. 3 ). Each predictor was assigned a score according to its contribution, and the total score corresponded to the predicted probability. For example, a patient with hypertension, elevated CRP, low LMR, abdominal tumor location, and longer operative time without neoadjuvant therapy would accumulate approximately 170 points, corresponding to an estimated AL risk of about 55%. 3.4 Model performance in the training cohort In the training set, the calibration curve showed excellent agreement between predicted and observed probabilities (Fig. 4 A). The Hosmer–Lemeshow test yielded χ² = 7.52, P = 0.375, indicating no lack of fit. The Brier score was 0.067. ROC analysis demonstrated strong discrimination, with an AUC of 0.820 (95% CI: 0.775–0.864) (Fig. 4 B). 3.5 Validation of the nomogram In the validation cohort (n = 170), 47 patients (27.6%) developed AL and 123 (72.4%) did not. The group comparison is shown in Table 3 . As in the training set, LMR was significantly lower in the AL group (3.51 ± 1.54 vs. 4.48 ± 2.15, P = 0.001), while other variables showed no significant differences. Validation analysis confirmed the robustness of the model. The AUC was 0.786 (95% CI: 0.710–0.863) (Fig. 5 A). The calibration curve demonstrated good consistency between predicted and observed outcomes (Fig. 5 B). The Hosmer–Lemeshow test yielded χ² = 5.89, P = 0.421, and the Brier score was 0.071. Table 3 Comparison of influencing factors between the two patient groups in the validation set Factors Level AL group (n = 47) N-AL group (n = 123) p-value Hypertension 0.494 Yes 17(36.2) 36(29.3) No 30(63.8) 87(70.7) Neoadjuvant therapy, n (%) 0.420 Yes 6(12.8) 24(19.5) No 41(87.2) 99(80.5) Tumor location, n (%) 0.456 Thoracic 20(42.6) 62(50.4) Abdomen 27(57.4) 61(49.6) C-reactive protein (CRP), mg/L ± SD 6.66 ± 6.79 5.13 ± 7.58 0.212 Operation duration, min ± SD 347.83 ± 124.38 328.25 ± 101.25 0.192 LMR 3.51 ± 1.54 4.48 ± 2.15 0.001 3.6 Clinical utility of the nomogram Decision curve analysis showed that when the threshold probability ranged from 0.1 to 0.8, the nomogram provided greater net benefit than either the “treat-all” or “treat-none” strategies (Fig. 6 ), supporting its favorable clinical applicability in individualized decision-making. 4. Discussion In this study, we developed and validated a novel nomogram to predict the risk of postoperative anastomotic leakage (AL) in patients with esophageal cancer after esophagectomy. Based on six independent predictors—hypertension, neoadjuvant therapy, lymphocyte-to-monocyte ratio (LMR), C-reactive protein (CRP), tumor location, and operation time—the model demonstrated favorable discrimination, calibration, and clinical applicability in both the training and validation cohorts. Hypertension was identified as an independent risk factor for AL [ 11 ]. This finding is consistent with previous studies suggesting that microvascular dysfunction and impaired tissue perfusion in hypertensive patients may compromise anastomotic healing. Neoadjuvant therapy, on the other hand, was found to be a protective factor [ 12 ]. Although some earlier studies reported conflicting results regarding its impact, our findings suggest that neoadjuvant treatment may facilitate tumor shrinkage and reduce surgical complexity, thereby lowering AL risk [ 13 ]. Systemic inflammatory and nutritional indices also played an important role. Higher CRP levels were associated with an increased risk of AL, underscoring the detrimental effect of systemic inflammation on wound healing [ 14 ]. In contrast, higher LMR was protective, reflecting the importance of immune and nutritional status in anastomotic integrity [ 15 – 16 ]. These findings align with accumulating evidence that inflammatory and nutritional markers are reliable predictors of postoperative complications. Tumor location was another significant factor: lower esophageal tumors were more strongly associated with AL, likely due to the technical difficulty of low anastomoses, limited blood supply, and greater anastomotic tension [ 17 – 18 ]. In addition, prolonged operation time independently increased the risk of AL, which is plausible given that longer procedures generally involve more surgical trauma, increased blood loss, and prolonged ischemia [ 19 – 20 ]. Several predictive models for AL after esophagectomy have been proposed in previous studies. However, many lacked external validation or included variables that were not easily available preoperatively [ 21 ]. Our model incorporates routinely collected clinical and laboratory parameters, and its predictive performance (AUC 0.820 in the training set and 0.786 in the validation set) is comparable to or better than those reported previously. Moreover, decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities [ 22 – 23 ]. The proposed nomogram provides a practical tool for individualized risk assessment [ 24 ]. By integrating common preoperative data with operative factors, it enables clinicians to identify high-risk patients and adopt preventive strategies, such as careful perioperative monitoring, optimization of nutritional and inflammatory status, and consideration of protective stoma creation [ 25 – 26 ]. This study has several limitations. It was a single-center retrospective study, and the relatively small validation cohort may limit generalizability. Some potentially relevant factors, such as surgeon experience and intraoperative perfusion assessment, were not included. Future multicenter prospective studies with larger sample sizes are needed to further validate and refine the model. In conclusion, we established and validated a nomogram based on six independent predictors to estimate the risk of postoperative AL in patients with esophageal cancer. The model demonstrated good predictive accuracy and clinical usefulness, and may assist clinicians in perioperative decision-making and individualized patient management. Abbreviations AL: Anastomotic Leakage; N-AL: Non-Anastomotic Leakage; LASSO: Least Absolute Shrinkage and Selection Operator; CRP: C-reactive Protein; LMR: Lymphocyte-to-Monocyte Ratio; AUC: Area Under the Curve; ROC: Receiver Operating Characteristic; DCA: Decision Curve Analysis; CI: Confidence Interval; OR: Odds Ratio; NLR: Neutrophil-to-Lymphocyte Ratio; PLR: Platelet-to-Lymphocyte Ratio; BMI: Body Mass Index; T stage: Tumor Stage N stage: Nodal Stage; M stage: Metastatic Stage; PDF: Probability Density Function H-L: Hosmer-Lemeshow. Declarations Funding This work was sponsored by“the Fundamental Research Funds for the Central Universities”(No. YG2024QNA30 to Hang Zhang) Acknowledgements We sincerely thank all the study participants for their involvement in this research. Ethics approval and consent to participate This study was approved by the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine (Approval Number: 2023SQ355). Informed written consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Availability of data and materials The data are available from the corresponding author on reasonable request. Authors' contributions TRN and GLL Performed preliminary analysis, contributed to the development of the predictive model, and co-drafted sections of the initial manuscript. HWR undertook data collection. ZH provided patient data from the Shanghai General Hospital and played a key role in the study's conceptual design. BQ oversaw the overall research planning and project management, conducted statistical analyses of key outcomes, and provided critical revisions to the manuscript. 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15:46:20","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96626,"visible":true,"origin":"","legend":"","description":"","filename":"40e29e680a664313a502a582926933821structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/f0dc0e47ce1956d9554f1642.xml"},{"id":94474437,"identity":"ffe07374-90e5-4813-bf61-b3f72234d5a6","added_by":"auto","created_at":"2025-10-27 15:48:57","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105325,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/f1dc0b25ea1cf37bcee04406.html"},{"id":94474012,"identity":"3acc52f1-1f0d-44d9-8178-61129287ab73","added_by":"auto","created_at":"2025-10-27 15:46:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCase selection procedure\u003c/strong\u003e\u003cbr\u003e\nA total of 885 patients with esophageal cancer who underwent esophagectomy at our hospital between January 2015 and May 2025 were initially screened. Among them, 23 patients with a history of prior cancer surgery or other malignant tumors were excluded, leaving 862 cases. Subsequently, 6 patients with incomplete clinical data and 6 patients who underwent emergency operations due to intestinal obstruction or hemorrhage were further excluded. Finally, 850 patients met the inclusion and exclusion criteria and were enrolled in this study, comprising 170 patients in the anastomotic leakage (AL) group and 680 patients in the non-anastomotic leakage (N-AL) group.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/4b5c05b3ca52bb1ddc37ae35.png"},{"id":94474142,"identity":"a314b8ce-2852-466d-abde-86ab98c1c66b","added_by":"auto","created_at":"2025-10-27 15:47:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO-logistic regression analysis of risk factors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) LASSO coefficient profiles of the 23 variables, with the tuning parameter (λ) plotted against log(λ). Each colored line represents the trajectory of a coefficient as λ varies. (B) Ten-fold cross-validation for LASSO regression was applied to select the optimal λ value. The left dashed line represents the minimum criterion, and the right dashed line indicates the λ value within 1 standard error of the minimum. At the optimal λ, five variables were retained in the model.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/bd4db26813f78b7f7adcecd1.png"},{"id":94474047,"identity":"80727b12-c0ff-4dd8-ac97-ac282291ed1f","added_by":"auto","created_at":"2025-10-27 15:46:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the risk of postoperative anastomotic leakage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nomogram was constructed based on the independent predictors identified in the LASSO-logistic regression model, including hypertension, neoadjuvant therapy, lymphocyte-to-monocyte ratio (LMR), C-reactive protein (CRP), tumor location, and operation time. For each patient, a score is assigned to each predictor according to its value on the corresponding axis. The total points, calculated as the sum of all individual predictor scores, are then projected onto the bottom scales to obtain the predicted probability of anastomotic leakage.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/c2907e7cb5887310e6eb5232.png"},{"id":94473991,"identity":"af10118a-a282-4f36-b31e-1366e72d4862","added_by":"auto","created_at":"2025-10-27 15:46:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration and discrimination of the nomogram model.\u003cbr\u003e\n \u003c/strong\u003e(A) Calibration curve of the nomogram for predicting postoperative anastomotic leakage. The x-axis represents the predicted probability and the y-axis represents the actual probability. The blue line indicates the apparent performance of the model, the red line shows the bias-corrected performance after bootstrapping, and the gray dashed line represents the ideal reference line. The close alignment between the predicted and actual probabilities demonstrates good calibration. (B) Receiver operating characteristic (ROC) curve of the nomogram model. The area under the curve (AUC) was 0.820 (95% CI: 0.775–0.864), indicating good discrimination ability.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/3936bbd0f4a296052462ca5d.png"},{"id":94474048,"identity":"425503aa-00ca-4f33-9b9a-f0fb318003a0","added_by":"auto","created_at":"2025-10-27 15:46:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the nomogram model in the validation cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Receiver operating characteristic (ROC) curve of the nomogram in the validation set. The area under the curve (AUC) was 0.786 (95% CI: 0.710–0.863), indicating acceptable discrimination. (B) Calibration curve of the nomogram in the validation set. The blue line indicates the apparent performance, the red line shows the bias-corrected performance after bootstrapping, and the gray dashed line represents the ideal reference line. The predicted probabilities were in good agreement with the actual observations, suggesting satisfactory calibration of the model.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/ac2dc510ece669e10a290682.png"},{"id":94474306,"identity":"81598390-9229-4dee-87c5-e26590aaad93","added_by":"auto","created_at":"2025-10-27 15:48:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis (DCA) of the nomogram model.\u003c/strong\u003e\u003cbr\u003e\n The DCA curve demonstrates the clinical utility of the nomogram for predicting postoperative anastomotic leakage. The x-axis represents the threshold probability, and the y-axis represents the standardized net benefit. The red line corresponds to the nomogram model, the gray line represents the assumption that all patients experience the event, and the black line represents the assumption that none of the patients experience the event. The results indicate that when the threshold probability is within an appropriate range, the nomogram provides a higher net benefit compared with the “treat all” or “treat none” strategies, suggesting good clinical applicability.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/ca5264d28af0e3ba9feca75e.png"},{"id":97660083,"identity":"a961adef-a2ff-4bb7-89e4-1c702ae7c08a","added_by":"auto","created_at":"2025-12-08 07:39:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2159943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7660216/v1/c485afcd-4ed1-4013-bdf7-56605651c797.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram for Predicting Postoperative Anastomotic Leakage in Esophageal Cancer Patients After Esophagectomy: Development and Validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEsophageal cancer is one of the most common malignant tumors worldwide, with high morbidity and mortality, particularly in East Asia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Esophagectomy remains the cornerstone of curative treatment for resectable esophageal cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, this complex procedure is associated with a high incidence of postoperative complications, among which anastomotic leakage (AL) is one of the most devastating [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The incidence of AL has been reported to vary significantly, contributing to increased morbidity, prolonged hospitalization, higher costs, and even mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, AL adversely affects postoperative quality of life and long-term oncologic outcomes. Therefore, early identification of patients at high risk of AL is of great clinical significance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous factors have been implicated in the development of AL, including patient comorbidities, nutritional and inflammatory status, tumor characteristics, and perioperative variables [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several predictive models have been proposed, but many have important limitations. Some models rely on single-center data without external validation, while others incorporate parameters that are difficult to obtain in routine clinical practice, limiting their applicability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, traditional statistical approaches may fail to handle collinearity and high-dimensional data effectively.\u003c/p\u003e\u003cp\u003eThe least absolute shrinkage and selection operator (LASSO) regression method has emerged as a powerful tool for variable selection in the presence of multicollinearity, enabling the construction of parsimonious yet robust predictive models [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Incorporating LASSO with multivariate logistic regression allows for the development of accurate nomograms that can provide individualized risk assessment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we retrospectively analyzed clinical data from patients undergoing esophagectomy at our institution to develop and validate a predictive nomogram for postoperative AL. By applying LASSO-logistic regression, we aimed to identify independent risk factors, construct a practical model, and evaluate its discrimination, calibration, and clinical utility. The goal was to establish a simple and reliable tool to assist clinicians in early risk stratification and individualized management of patients with esophageal cancer.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and patients\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study included patients with esophageal cancer who underwent esophagectomy at our hospital between January 2015 and May 2025. The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee. Written informed consent was obtained from all patients. A total of 885 patients were initially identified. Exclusion criteria were: (1) history of prior cancer surgery or other malignant tumors (n\u0026thinsp;=\u0026thinsp;23); (2) incomplete clinical data (n\u0026thinsp;=\u0026thinsp;6); and (3) emergency operations due to intestinal obstruction or hemorrhage (n\u0026thinsp;=\u0026thinsp;6). Finally, 850 patients met the inclusion and exclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These patients were randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;680) and a validation cohort (n\u0026thinsp;=\u0026thinsp;170) at a ratio of 8:2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\u003cp\u003eBaseline clinicopathological characteristics and laboratory parameters were collected from medical records, including age, sex, BMI, comorbidities (hypertension, diabetes, lacunar infarction), tumor stage (T, N, M), tumor location, histological type, neoadjuvant therapy, neuroaggression, anastomotic site, operation duration, intraoperative blood loss, albumin, hemoglobin, C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and CALLY index. The primary endpoint was postoperative anastomotic leakage (AL), defined according to the International Study Group of Rectal Cancer criteria, confirmed by clinical manifestations and imaging or endoscopic findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Variable selection using LASSO regression\u003c/h2\u003e\u003cp\u003eTo reduce the risk of multicollinearity and prevent overfitting, the least absolute shrinkage and selection operator (LASSO) logistic regression model was applied to the training cohort. Twenty-three candidate variables were initially included. The optimal penalty parameter λ was determined using ten-fold cross-validation with the minimum criteria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Logistic regression analysis\u003c/h2\u003e\u003cp\u003eVariables with nonzero coefficients in the LASSO model were subsequently entered into multivariate logistic regression to identify independent predictors of AL. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Construction of the nomogram\u003c/h2\u003e\u003cp\u003eBased on the independent predictors identified in multivariate logistic regression, a nomogram model was constructed to estimate the probability of postoperative AL. Each variable was assigned a weighted score proportional to its regression coefficient, and the sum of all scores corresponded to the predicted probability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Validation and performance assessment\u003c/h2\u003e\u003cp\u003eThe predictive performance of the nomogram was evaluated in both the training and validation cohorts. Model discrimination was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC). Calibration was evaluated by calibration plots, Hosmer\u0026ndash;Lemeshow (H-L) goodness-of-fit test, and Brier score. Internal validation was performed using bootstrap resampling (1,000 repetitions).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Clinical utility\u003c/h2\u003e\u003cp\u003eDecision curve analysis (DCA) was performed to assess the clinical usefulness of the nomogram by quantifying the net benefit across a range of threshold probabilities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.3.1). LASSO regression was conducted using the \u0026ldquo;glmnet\u0026rdquo; package, and the nomogram was constructed using the \u0026ldquo;rms\u0026rdquo; package. ROC curves were generated with the \u0026ldquo;pROC\u0026rdquo; package, calibration curves with the \u0026ldquo;rms\u0026rdquo; package, and DCA with the \u0026ldquo;rmda\u0026rdquo; package. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Case selection and baseline clinical characteristics\u003c/h2\u003e\u003cp\u003eA total of 885 patients with esophageal cancer who underwent esophagectomy at our hospital between January 2015 and May 2025 were initially screened. After excluding 23 patients with a history of prior malignancy or cancer surgery, 6 patients with incomplete data, and 6 patients who underwent emergency operations, 850 patients were enrolled, including 170 in the AL group and 680 in the N-AL group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Baseline clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed in age, sex, BMI, diabetes, tumor stage, neoadjuvant therapy, tumor location, histological type, or anastomotic site between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, hypertension was more common in the AL group (37.4% vs. 27.6%, P\u0026thinsp;=\u0026thinsp;0.047). Inflammatory and nutritional indices also differed significantly: the AL group had lower LMR (3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 vs. 4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CALLY scores (3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51 vs. 7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but higher CRP levels (17.83\u0026thinsp;\u0026plusmn;\u0026thinsp;33.52 vs. 6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.74 mg/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NLR (4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54 vs. 2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84, P\u0026thinsp;=\u0026thinsp;0.007). In addition, operative time was significantly longer in the AL group (349.63\u0026thinsp;\u0026plusmn;\u0026thinsp;113.75 vs. 312.88\u0026thinsp;\u0026plusmn;\u0026thinsp;95.90 min, P\u0026thinsp;=\u0026thinsp;0.002), while hemoglobin levels were lower (127.27\u0026thinsp;\u0026plusmn;\u0026thinsp;23.46 vs. 132.45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.11 g/L, P\u0026thinsp;=\u0026thinsp;0.027).\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 clinical characteristics of the AL and N-AL groups in the training set.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAL group (n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN-AL group (n\u0026thinsp;=\u0026thinsp;565)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18(15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93(16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e472(83.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18(15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132(23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e433(76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index(BMI)/༈kg/m\u0026sup2;༉\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u0026lt;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e374(66.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e191(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43(37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156(27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72(62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e409(72.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9(7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106(92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e523(92.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT (tumor invasion, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1-T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47(40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e274(48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3-T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68(59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e318(51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN (regional lymph node, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0-N1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102(88.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e477(84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN2-N3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13(11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88(15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM(metastasis,, n %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112(97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e561(99.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3(2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9(7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67(11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106(92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e498(88.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLacunar Infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69(12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94(81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e496(87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuroaggression, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35(30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205(36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80(69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e360(63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79(68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e435(77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbdomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130(23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological tumor type, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(84.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e469(83.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14(12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77(13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther types\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4(3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomotic site, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeck\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e195(34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70(60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e335(59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbdomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4(3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35(6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, n(%) / (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15(13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43(7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100(87.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e522(92.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCALLY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil-to-Lymphocyte Ratio (NLR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP), mg/L\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.83\u0026thinsp;\u0026plusmn;\u0026thinsp;33.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation duration, min\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e349.63\u0026thinsp;\u0026plusmn;\u0026thinsp;113.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e312.88\u0026thinsp;\u0026plusmn;\u0026thinsp;95.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood loss, mL\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264.43\u0026thinsp;\u0026plusmn;\u0026thinsp;211.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e270.02\u0026thinsp;\u0026plusmn;\u0026thinsp;280.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, mg/L\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e127.27\u0026thinsp;\u0026plusmn;\u0026thinsp;23.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132.45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202.32\u0026thinsp;\u0026plusmn;\u0026thinsp;339.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140.62\u0026thinsp;\u0026plusmn;\u0026thinsp;68.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Identification of independent risk factors\u003c/h2\u003e\u003cp\u003eLASSO-logistic regression analysis was performed to minimize collinearity among variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the penalty parameter λ was chosen via ten-fold cross-validation (λ\u0026thinsp;=\u0026thinsp;0.0273), six variables with nonzero coefficients were retained: hypertension, neoadjuvant therapy, CRP, operation time, LMR, and tumor location. These variables were subsequently entered into multivariate logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that neoadjuvant therapy (OR\u0026thinsp;=\u0026thinsp;0.395, 95% CI: 0.170\u0026ndash;0.916, P\u0026thinsp;=\u0026thinsp;0.030) and higher LMR (OR\u0026thinsp;=\u0026thinsp;0.843, 95% CI: 0.719\u0026ndash;0.987, P\u0026thinsp;=\u0026thinsp;0.034) were protective factors. In contrast, hypertension (OR\u0026thinsp;=\u0026thinsp;1.751, 95% CI: 1.090\u0026ndash;2.811, P\u0026thinsp;=\u0026thinsp;0.020), elevated CRP (OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.009\u0026ndash;1.030, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), longer operation duration (OR\u0026thinsp;=\u0026thinsp;1.005, 95% CI: 1.002\u0026ndash;1.007, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and tumor location (OR\u0026thinsp;=\u0026thinsp;1.977, 95% CI: 1.234\u0026ndash;3.167, P\u0026thinsp;=\u0026thinsp;0.005) were independent risk factors for AL.\u003c/p\u003e\u003cp\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\u003eLogistic regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluencing factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.170\u0026thinsp;~\u0026thinsp;0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.009\u0026thinsp;~\u0026thinsp;1.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.002\u0026thinsp;~\u0026thinsp;1.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.090\u0026thinsp;~\u0026thinsp;2.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.719\u0026thinsp;~\u0026thinsp;0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.234\u0026thinsp;~\u0026thinsp;3.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Construction of the nomogram\u003c/h2\u003e\u003cp\u003eBased on the six independent predictors, a nomogram was developed to predict the individual probability of postoperative AL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Each predictor was assigned a score according to its contribution, and the total score corresponded to the predicted probability. For example, a patient with hypertension, elevated CRP, low LMR, abdominal tumor location, and longer operative time without neoadjuvant therapy would accumulate approximately 170 points, corresponding to an estimated AL risk of about 55%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Model performance in the training cohort\u003c/h2\u003e\u003cp\u003eIn the training set, the calibration curve showed excellent agreement between predicted and observed probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The Hosmer\u0026ndash;Lemeshow test yielded χ\u0026sup2; = 7.52, P\u0026thinsp;=\u0026thinsp;0.375, indicating no lack of fit. The Brier score was 0.067. ROC analysis demonstrated strong discrimination, with an AUC of 0.820 (95% CI: 0.775\u0026ndash;0.864) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Validation of the nomogram\u003c/h2\u003e\u003cp\u003eIn the validation cohort (n\u0026thinsp;=\u0026thinsp;170), 47 patients (27.6%) developed AL and 123 (72.4%) did not. The group comparison is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As in the training set, LMR was significantly lower in the AL group (3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 vs. 4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15, P\u0026thinsp;=\u0026thinsp;0.001), while other variables showed no significant differences. Validation analysis confirmed the robustness of the model. The AUC was 0.786 (95% CI: 0.710\u0026ndash;0.863) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The calibration curve demonstrated good consistency between predicted and observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The Hosmer\u0026ndash;Lemeshow test yielded χ\u0026sup2; = 5.89, P\u0026thinsp;=\u0026thinsp;0.421, and the Brier score was 0.071.\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\u003eComparison of influencing factors between the two patient groups in the validation set\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAL group (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN-AL group (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36(29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30(63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87(70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6(12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24(19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41(87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99(80.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThoracic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20(42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62(50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbdomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27(57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61(49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP), mg/L\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation duration, min\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e347.83\u0026thinsp;\u0026plusmn;\u0026thinsp;124.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e328.25\u0026thinsp;\u0026plusmn;\u0026thinsp;101.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\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\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Clinical utility of the nomogram\u003c/h2\u003e\u003cp\u003eDecision curve analysis showed that when the threshold probability ranged from 0.1 to 0.8, the nomogram provided greater net benefit than either the \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), supporting its favorable clinical applicability in individualized decision-making.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we developed and validated a novel nomogram to predict the risk of postoperative anastomotic leakage (AL) in patients with esophageal cancer after esophagectomy. Based on six independent predictors\u0026mdash;hypertension, neoadjuvant therapy, lymphocyte-to-monocyte ratio (LMR), C-reactive protein (CRP), tumor location, and operation time\u0026mdash;the model demonstrated favorable discrimination, calibration, and clinical applicability in both the training and validation cohorts.\u003c/p\u003e\u003cp\u003eHypertension was identified as an independent risk factor for AL [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This finding is consistent with previous studies suggesting that microvascular dysfunction and impaired tissue perfusion in hypertensive patients may compromise anastomotic healing. Neoadjuvant therapy, on the other hand, was found to be a protective factor [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although some earlier studies reported conflicting results regarding its impact, our findings suggest that neoadjuvant treatment may facilitate tumor shrinkage and reduce surgical complexity, thereby lowering AL risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSystemic inflammatory and nutritional indices also played an important role. Higher CRP levels were associated with an increased risk of AL, underscoring the detrimental effect of systemic inflammation on wound healing [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In contrast, higher LMR was protective, reflecting the importance of immune and nutritional status in anastomotic integrity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings align with accumulating evidence that inflammatory and nutritional markers are reliable predictors of postoperative complications. Tumor location was another significant factor: lower esophageal tumors were more strongly associated with AL, likely due to the technical difficulty of low anastomoses, limited blood supply, and greater anastomotic tension [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, prolonged operation time independently increased the risk of AL, which is plausible given that longer procedures generally involve more surgical trauma, increased blood loss, and prolonged ischemia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral predictive models for AL after esophagectomy have been proposed in previous studies. However, many lacked external validation or included variables that were not easily available preoperatively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our model incorporates routinely collected clinical and laboratory parameters, and its predictive performance (AUC 0.820 in the training set and 0.786 in the validation set) is comparable to or better than those reported previously. Moreover, decision curve analysis confirmed its clinical utility across a wide range of threshold probabilities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe proposed nomogram provides a practical tool for individualized risk assessment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By integrating common preoperative data with operative factors, it enables clinicians to identify high-risk patients and adopt preventive strategies, such as careful perioperative monitoring, optimization of nutritional and inflammatory status, and consideration of protective stoma creation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has several limitations. It was a single-center retrospective study, and the relatively small validation cohort may limit generalizability. Some potentially relevant factors, such as surgeon experience and intraoperative perfusion assessment, were not included. Future multicenter prospective studies with larger sample sizes are needed to further validate and refine the model.\u003c/p\u003e\u003cp\u003eIn conclusion, we established and validated a nomogram based on six independent predictors to estimate the risk of postoperative AL in patients with esophageal cancer. The model demonstrated good predictive accuracy and clinical usefulness, and may assist clinicians in perioperative decision-making and individualized patient management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAL: Anastomotic Leakage; N-AL: Non-Anastomotic Leakage; LASSO: Least Absolute Shrinkage and Selection Operator; CRP: C-reactive Protein; LMR: Lymphocyte-to-Monocyte Ratio; AUC: Area Under the Curve; ROC: Receiver Operating Characteristic; DCA: Decision Curve Analysis; CI: Confidence Interval; OR: Odds Ratio; NLR: Neutrophil-to-Lymphocyte Ratio; PLR: Platelet-to-Lymphocyte Ratio; BMI: Body Mass Index; T stage: Tumor Stage N stage: Nodal Stage; M stage: Metastatic Stage; PDF: Probability Density Function H-L: Hosmer-Lemeshow.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was sponsored by\u0026ldquo;the Fundamental Research Funds for the Central Universities\u0026rdquo;(No. YG2024QNA30 to Hang Zhang)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the study participants for their involvement in this research.\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine (Approval Number: 2023SQ355).\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTRN and GLL Performed preliminary analysis, contributed to the development of the predictive model, and co-drafted sections of the initial manuscript. HWR undertook data collection. ZH provided patient data from the Shanghai General Hospital and played a key role in the study\u0026apos;s conceptual design. BQ oversaw the overall research planning and project management, conducted statistical analyses of key outcomes, and provided critical revisions to the manuscript. All authors actively participated in scientific discussions, reviewed the findings, and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., \u003cem\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e CA Cancer J Clin, 2024. \u003cstrong\u003e74\u003c/strong\u003e(3): p. 229-263.\u003c/li\u003e\n\u003cli\u003eDi, J., et al., \u003cem\u003eHospital volume-mortality association after esophagectomy for cancer: a systematic review and meta-analysis.\u003c/em\u003e Int J Surg, 2024. \u003cstrong\u003e110\u003c/strong\u003e(5): p. 3021-3029.\u003c/li\u003e\n\u003cli\u003eFabbi, M., et al., \u003cem\u003eAnastomotic leakage after esophagectomy for esophageal cancer: definitions, diagnostics, and treatment.\u003c/em\u003e Dis Esophagus, 2021. \u003cstrong\u003e34\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eBachmann, J., et al., \u003cem\u003eAnastomotic leakage following resection of the esophagus-introduction of an endoscopic grading system.\u003c/em\u003e World J Surg Oncol, 2022. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 104.\u003c/li\u003e\n\u003cli\u003eBootsma, B.T., et al., \u003cem\u003eTowards optimal intraoperative conditions in esophageal surgery: A review of literature for the prevention of esophageal anastomotic leakage.\u003c/em\u003e Int J Surg, 2018. \u003cstrong\u003e54\u003c/strong\u003e(Pt A): p. 113-123.\u003c/li\u003e\n\u003cli\u003eRutegard, M., et al., \u003cem\u003eIntrathoracic anastomotic leakage and mortality after esophageal cancer resection: a population-based study.\u003c/em\u003e Ann Surg Oncol, 2012. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 99-103.\u003c/li\u003e\n\u003cli\u003eVetter, D. and C.A. Gutschow, \u003cem\u003eStrategies to prevent anastomotic leakage after esophagectomy and gastric conduit reconstruction.\u003c/em\u003e Langenbecks Arch Surg, 2020. \u003cstrong\u003e405\u003c/strong\u003e(8): p. 1069-1077.\u003c/li\u003e\n\u003cli\u003eZhou, Z.R., et al., \u003cem\u003eIn-depth mining of clinical data: the construction of clinical prediction model with R.\u003c/em\u003e Ann Transl Med, 2019. \u003cstrong\u003e7\u003c/strong\u003e(23): p. 796.\u003c/li\u003e\n\u003cli\u003eBarrett, S.E. and D.A. Mitchell, \u003cem\u003eAdvances in lasso peptide discovery, biosynthesis, and function.\u003c/em\u003e Trends Genet, 2024. \u003cstrong\u003e40\u003c/strong\u003e(11): p. 950-968.\u003c/li\u003e\n\u003cli\u003eCheng, C. and Z.C. Hua, \u003cem\u003eLasso Peptides: Heterologous Production and Potential Medical Application.\u003c/em\u003e Front Bioeng Biotechnol, 2020. \u003cstrong\u003e8\u003c/strong\u003e: p. 571165.\u003c/li\u003e\n\u003cli\u003evan Kooten, R.T., et al., \u003cem\u003ePatient-Related Prognostic Factors for Anastomotic Leakage, Major Complications, and Short-Term Mortality Following Esophagectomy for Cancer: A Systematic Review and Meta-Analyses.\u003c/em\u003e Ann Surg Oncol, 2022. \u003cstrong\u003e29\u003c/strong\u003e(2): p. 1358-1373.\u003c/li\u003e\n\u003cli\u003eHong, Z., et al., \u003cem\u003eAdditional neoadjuvant immunotherapy does not increase the risk of anastomotic leakage after esophagectomy for esophageal squamous cell carcinoma: a multicenter retrospective cohort study.\u003c/em\u003e Int J Surg, 2023. \u003cstrong\u003e109\u003c/strong\u003e(8): p. 2168-2178.\u003c/li\u003e\n\u003cli\u003eHong, Z., et al., Additional Neoadjuvant Immunotherapy Does Not Increase the Risk of Anastomotic Leakage After Esophagectomy for Esophageal Squamous Cell Carcinoma: A Multicenter Retrospective Cohort Study. Int J Surg, 2023. 109: p. 2168-2178\u003c/li\u003e\n\u003cli\u003eCharbonneau, J., et al., \u003cem\u003ePredictive Value of C-Reactive Protein for Infectious Complications After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Single-Center Prospective Study.\u003c/em\u003e Ann Surg Oncol, 2024. \u003cstrong\u003e31\u003c/strong\u003e(13): p. 8538-8548.\u003c/li\u003e\n\u003cli\u003eAoyama, T., et al., Lymphocyte to Monocyte Ratio Is an Independent Prognostic Factor in Patients With Esophageal Cancer Who Receive Curative Treatment. Anticancer Res, 2024. 44(1): p. 339-346.\u003c/li\u003e\n\u003cli\u003eWang, X., et al., Neoadjuvant Immunochemotherapy for Resectable Esophageal Cancer: A Study on Efficacy and Safety. Biomolecules Biomedicine, 2025. 25(9): p. 2127-2138.\u003c/li\u003e\n\u003cli\u003evan Workum, F., et al., \u003cem\u003eIntrathoracic vs Cervical Anastomosis After Totally or Hybrid Minimally Invasive Esophagectomy for Esophageal Cancer: A Randomized Clinical Trial.\u003c/em\u003e JAMA Surg, 2021. \u003cstrong\u003e156\u003c/strong\u003e(7): p. 601-610.\u003c/li\u003e\n\u003cli\u003eMcKenna, N.P., et al., The Intersection of Tumor Location and Combined Bowel Preparation: Utilization Differs but Anastomotic Leak Risk Reduction Does Not. 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Sci Rep, 2024. 14(1): p. 29197.\u003c/li\u003e\n\u003cli\u003eGreijdanus, N.G., et al., \u003cem\u003eStoma-free Survival After Rectal Cancer Resection With Anastomotic Leakage: Development and Validation of a Prediction Model in a Large International Cohort.\u003c/em\u003e Ann Surg, 2023. \u003cstrong\u003e278\u003c/strong\u003e(5): p. 772-780.\u003c/li\u003e\n\u003cli\u003eKang, B.Y., et al., \u003cem\u003eSerum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study.\u003c/em\u003e World J Gastroenterol, 2025. \u003cstrong\u003e31\u003c/strong\u003e(19): p. 105283.\u003c/li\u003e\n\u003cli\u003eKang, B.Y., et al., Serum Calcium-based Interpretable Machine Learning Model for Predicting Anastomotic Leakage After Rectal Cancer Resection: A Multi-center Study. World J Gastroenterol, 2025. 31(19): p. 105283.\u003c/li\u003e\n\u003cli\u003eWu, J., et al., A Nomogram for Predicting Overall Survival in Patients with Low-Grade Endometrial Stromal Sarcoma: A Population-Based Analysis. Cancer Commun, 2020. 40(7): p. 301-312.\u003c/li\u003e\n\u003cli\u003eHuang, Y., et al., Nomogram for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using MRI-based Intratumoral Heterogeneity Quantification. Radiology, 2025. 315(1): p. e241805.\u003c/li\u003e\n\u003cli\u003eZhao, J., et al., Predicting Anastomotic Leak in Patients with Esophageal Squamous Cell Cancer Treated with Neoadjuvant Chemoradiotherapy Using a Nomogram Based on CT Radiomic and Clinicopathologic Factors. BMC Cancer, 2025. 25(1): p. 484.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anastomotic Leakage, Esophageal Cancer, Nomogram, LASSO Regression, Predictive Model","lastPublishedDoi":"10.21203/rs.3.rs-7660216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7660216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEsophageal cancer is a prevalent malignancy, particularly in East Asia, with high morbidity and mortality rates. Postoperative anastomotic leakage (AL) is a major complication after esophagectomy, impacting recovery and prognosis. Early identification of high-risk patients is critical.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo develop and validate a predictive nomogram for postoperative AL risk using LASSO-logistic regression to identify independent risk factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted on 850 esophageal cancer patients who underwent esophagectomy. Clinical data were collected, including variables such as hypertension, C-reactive protein (CRP), operation time, lymphocyte-to-monocyte ratio (LMR), and tumor location. LASSO regression was used for variable selection, followed by multivariate logistic regression to identify independent risk factors. A nomogram was developed and validated in a separate cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSix independent risk factors for AL were identified: hypertension, neoadjuvant therapy, CRP, operation time, LMR, and tumor location. The nomogram showed good performance, with an AUC of 0.820 in the training cohort and 0.786 in the validation cohort, indicating strong discrimination. Calibration curves confirmed good agreement between predicted and observed outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe nomogram provides an effective and reliable tool for early risk stratification and individualized management of esophageal cancer patients at high risk for postoperative AL.\u003c/p\u003e","manuscriptTitle":"A Nomogram for Predicting Postoperative Anastomotic Leakage in Esophageal Cancer Patients After Esophagectomy: Development and Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 14:32:58","doi":"10.21203/rs.3.rs-7660216/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"965f243b-70fc-41aa-a17a-6c191c82726f","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T07:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 14:32:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7660216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7660216","identity":"rs-7660216","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}