Comparison of the prognostic value of LODDS and pN stage for esophageal squamous cell carcinoma patients treated with neoadjuvant immunochemotherapy: a multicenter retrospective study

Comparison of the prognostic value of LODDS and pN stage for esophageal squamous cell carcinoma patients treated with neoadjuvant immunochemotherapy: a multicenter retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of the prognostic value of LODDS and pN stage for esophageal squamous cell carcinoma patients treated with neoadjuvant immunochemotherapy: a multicenter retrospective study Hao Chen, Xiangyang Yu, Jianfei Zhu, Ran Yang, Yi Han, Feng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8438230/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background As a novel prognostic factor, log odds of positive lymph nodes (LODDS) has been shown to be associated with the prognosis of many cancers. This study mainly explored whether LODDS is a better lymph node-based prognostic factor compared with traditional pN stage for patients with esophageal squamous cell carcinoma who underwent surgical treatment after neoadjuvant immunochemotherapy. Methods A multicenter retrospective cohort of 305 clinical stage II–III esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant immunochemotherapy followed by curative surgery at four tertiary centers in China (2019–2024) was analyzed. LODDS was calculated as ln[(pLNs+0.5)/(nLNs+0.5)] (pLNs, positive lymph nodes; nLNs, negative lymph nodes). Patients were stratified into three LODDS risk groups using X-tile. Overall survival (OS) and recurrence-free survival (RFS) were assessed by Kaplan–Meier analysis and log-rank tests. For each endpoint, multivariable Cox models including either pN stage or LODDS (with identical covariates) were compared using Harrell’s C-index, AIC/BIC, 3-year Brier score, decision curve analysis, and time-dependent IDI and category-free NRI. Results The 3-year overall survival (OS) and recurrence-free survival (RFS) rates were 71.8% and 63.2%, respectively. Kaplan–Meier analyses showed progressively worse OS and RFS with increasing pN stage and LODDS categories, with clearer separation across LODDS risk groups. After adjustment for clinicopathological covariates, LODDS remained an independent adverse prognostic factor for both endpoints, whereas the prognostic effect of pN stage was weaker and less consistent. Compared with pN stage–based models, LODDS-based models showed slightly higher C-index (OS: 0.743 vs 0.737; RFS: 0.735 vs 0.723), lower AIC/BIC (OS AIC: 749.7 vs 753.4; OS BIC: 765.9 vs 771.9; RFS AIC: 978.1 vs 985.8; RFS BIC: 1001.2 vs 1011.1), and lower 3-year Brier scores (OS: 0.151 vs 0.159; RFS: 0.141 vs 0.155). Decision curve analysis suggested higher net benefit for LODDS-based models across commonly used threshold probabilities. Although IDI and NRI at 18 and 36 months did not reach conventional statistical significance, most estimates favored LODDS and were consistent with other performance metrics. Conclusions For the ESCC patients treated with surgery following neoadjuvant immunochemotherapy, LODDS is a superior prognostic factor to pN stage. Esophageal squamous cell carcinoma Neoadjuvant immunochemotherapy Log odds of positive lymph nodes LODDS Lymph node staging Prognosis Overall survival Recurrence-free survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 BACKGROUND Esophageal cancer (EC) remains one of the major cancer types worldwide[ 1 ], and esophageal squamous cell carcinoma (ESCC) is the most common histological subtype globally[ 2 , 3 ]. At present, surgery (including radical esophagectomy and lymph node dissection) is the main treatment for EC. However, many patients are diagnosed at an advanced stage and miss the opportunity for surgery[ 4 ]. In recent years, given the substantial benefits of immunotherapy in the treatment of advanced cancers, an emerging hotspot—neoadjuvant immunochemotherapy (nICT)[ 5 ]—has significantly influenced treatment strategies for advanced EC and improved overall survival in patients with metastatic disease[ 6 , 7 ]. Accurate staging is crucial for guiding appropriate treatment and predicting prognosis. The post-neoadjuvant pathological TNM (ypTNM) staging system in the 8th edition of the American Joint Committee on Cancer (AJCC) is the most widely used tool for prognostic evaluation of EC after neoadjuvant therapy. N staging based on the number of metastatic lymph nodes remains the most commonly used prognostic indicator for EC[ 8 ]. However, the current staging system relies only on the number of positive lymph nodes and does not consider the impact of the total number of examined lymph nodes. Moreover, it is highly susceptible to differences in surgical practice and specimen examination, making the staging results vulnerable to insufficient lymph node dissection and individual factors of pathologic assessment, thereby resulting in stage migration[ 9 – 12 ]. Log odds of positive lymph nodes (LODDS) is a novel lymph node–related prognostic factor. LODDS is calculated as LODDS = ln[(pLNs + 0.5) / (nLNs + 0.5)]. In multiple studies, LODDS has been confirmed to be a better prognostic factor in bladder cancer, pancreatic cancer, rectal cancer, lung cancer, and EC[ 13 – 18 ]. However, the predictive value of LODDS in ESCC patients undergoing surgery after nICT has not yet been investigated. In this study, by comparing the prognostic value of LODDS and traditional N staging in ESCC patients treated with surgery after nICT, we aim to propose a better lymph node staging indicator for this population, thereby improving the current lymph node staging scheme. METHODS Study Cohort We retrospectively collected data on patients with esophageal squamous cell carcinoma (ESCC) who underwent surgical treatment after neoadjuvant immunochemotherapy (nICT) at four centers in China (Beijing Chest Hospital, Shaanxi Provincial Hospital, Anyang Cancer Hospital, and Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences) between January 2019 and December 2024. Eligible patients had stage II–III disease as defined by the 8th edition of the American Joint Committee on Cancer (AJCC) staging manual. This study aimed to compare the prognostic performance of log odds of positive lymph nodes (LODDS) and pathological N stage (pN stage) in ESCC patients treated with nICT followed by surgery. Exclusion criteria were: age < 18 years at diagnosis; receipt of esophagectomy alone or endoscopic submucosal dissection; neoadjuvant therapy without immune checkpoint inhibitors (ICIs); participation in interventional clinical trials; history of other malignancies before or after esophagectomy; and incomplete clinicopathological or follow-up information. Ultimately, 305 ESCC patients treated with nICT followed by surgery were included. In this study, detailed demographic, imaging, surgical, pathological, and follow-up data were extracted from the hospital information systems and recorded in standardized case report forms. The surgical approach and neoadjuvant regimen were determined and implemented by the local multidisciplinary team (MDT) according to the European Society for Medical Oncology (ESMO) and Chinese Society of Clinical Oncology clinical practice guidelines, the expert consensus on perioperative management of esophageal cancer, or the latest clinical evidence,[ 19 ] and were carried out accordingly. Pre-treatment work-up routinely included contrast-enhanced computed tomography (CT) of the neck, chest, and abdomen; esophagogastroduodenoscopy combined with endoscopic ultrasonography; double-contrast upper gastrointestinal radiography; and cervical ultrasonography for diagnosis, localization, and staging. Brain magnetic resonance imaging (MRI), radionuclide whole-body bone scanning, and positron emission tomography were performed when clinically indicated to exclude occult distant metastases. Contrast-enhanced CT scans of the neck, chest, and abdomen were repeated after every two cycles of neoadjuvant immunochemotherapy and within one week before esophagectomy, and the best radiologic response of the primary tumor was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Post-treatment surveillance routinely included contrast-enhanced chest CT and cervical/abdominal ultrasonography every 3 months for 2 years and every 6 months thereafter until recurrence or death. When necessary, esophagogastroduodenoscopy, brain MRI, and PET-CT were used to differentiate true recurrence from metastasis. MPR was defined as ≤ 10% residual viable tumor cells in the resected primary tumor specimen. In addition to the above, MDT decisions regarding adjuvant therapy were mainly influenced by postoperative complications, pathological response, and patient preferences. Lymphadenectomy was performed using standard or extended (en bloc) techniques. According to the AJCC 8th edition pathological TNM system, pN1 indicates 1–2 metastatic lymph nodes, pN2 indicates 3–6 metastatic lymph nodes, and pN3 indicates ≥ 7 metastatic lymph nodes. LODDS was calculated as ln[(pLNs + 0.5)/(nLNs + 0.5)], where pLNs represent the number of positive lymph nodes and nLNs represent the number of negative lymph nodes (total retrieved lymph nodes minus pLNs). Three independent thoracic surgeons assessed the quality of all collected data and cross-checked the dataset before statistical analyses. Ethical approval was obtained from the Ethics Committee of Beijing Chest Hospital, Capital Medical University (approval No. LW-2025-025; approval date: 14 August 2025) and the Ethics Committee of Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (ethics acceptance No. YW2024-3-1; approval date: 29 February 2024). Written informed consent was waived due to the retrospective design and the use of de-identified clinical data. The study was conducted in accordance with the Declaration of Helsinki (2013 revision) and Good Clinical Practice guidelines. Outcomes Overall survival (OS) was defined as the time from surgery to death from any cause; patients alive at last follow-up were censored. Recurrence-free survival (RFS) was defined as the time from surgery to the first documented recurrence (local/regional or distant) or death from any cause, whichever occurred first; patients alive without recurrence were censored at last follow-up. Follow-Up Follow-up was performed every 3 months during the first 2 years, every 6 months from year 3 to year 5, and annually thereafter. Patient status was assessed using blood tests, tumor markers, chest CT, and abdominal ultrasonography. Brain MRI was performed annually. All patients were followed until December 31, 2024, or death, whichever occurred first. Statistical Analysis The optimal cutoff values for LODDS were determined using X-tile software (version 3.6.1; Yale University, USA), and patients were categorized into low-, intermediate-, and high-risk LODDS groups. Kaplan–Meier methods were used to estimate overall survival (OS) and recurrence-free survival (RFS), and differences between groups were compared using the log-rank test. A two-sided P value < 0.05 was considered statistically significant. Univariate Cox proportional hazards regression was first performed for all candidate clinical and pathological variables. Variables with P < 0.10 in univariate analyses were entered into multivariable Cox regression models. To compare the prognostic performance of different lymph node evaluation approaches, two sets of multivariable models were constructed with either traditional pN stage or LODDS as the key variable while keeping other covariates identical. Each model reported hazard ratios (HRs), 95% confidence intervals (CIs), and Harrell’s concordance index (C-index), and P values were calculated based on the likelihood ratio test to assess covariate significance and overall model discrimination. The proportional hazards assumption was assessed using Schoenfeld residuals. Missing data, if any, were handled using complete-case analysis. Model discrimination was evaluated using the C-index and time-dependent receiver operating characteristic (ROC) curves with the area under the curve (AUC) at 18 and 36 months. Model fit was compared using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), with lower AIC/BIC values indicating better fit. Overall prediction error at 36 months was assessed using the Brier score, and multivariable models based on pN stage and LODDS were compared against a null model at 36 months; lower Brier scores indicate smaller prediction error and better predictive performance. Clinical utility was assessed using decision curve analysis (DCA). Based on the 36-month event risk predicted by each Cox model, net benefit across a range of threshold probabilities was calculated and compared with “treat-all” and “treat-none” strategies to evaluate the clinical usefulness of pN- and LODDS-based models. To further quantify the incremental predictive value of LODDS over pN stage, time-dependent integrated discrimination improvement (IDI) and category-free net reclassification improvement (NRI) at 18 and 36 months were calculated using the survIDINRI package. All statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). The main R packages included survival, survminer, timeROC, rms, riskRegression, rmda, and survIDINRI. RESULTS A total of 305 patients with esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant immunochemotherapy (nICT) followed by surgery were included. Based on the optimal cut points determined by X-tile, LODDS was categorized into three levels: low (≤ −3.14), intermediate (> −3.14 to ≤ −2.10), and high (> −2.10), accounting for 60.7%, 22.0%, and 17.4% of patients, respectively. During follow-up, 75 overall survival (OS) events and 96 recurrence-free survival (RFS) events were recorded. Baseline characteristics and univariate Cox regression results for OS and RFS are presented in Table 1A and Table 1B, respectively. Kaplan–Meier survival curves showed that for both OS and RFS, survival probabilities decreased significantly with increasing pN stage or LODDS category (Fig. 1). Compared with pN stage, the survival curves across LODDS categories exhibited clearer separation, suggesting stronger discriminative ability for survival differences. Details of univariate Cox regression are shown in Table 1A (OS) and Table 1B (RFS). Variables with P < 0.10 in univariate analyses were entered into multivariable Cox regression models. In the multivariable model for OS, the pN-based model indicated that, after adjustment for covariates, cycles of neoadjuvant therapy ≥3 were independently associated with worse OS, whereas major pathological response (MPR) was an independent protective factor; with respect to nodal staging, both pN1 and pN2 were independent adverse prognostic factors. After replacing pN with LODDS, the independent effects of cycles of neoadjuvant therapy ≥3 and MPR remained stable; LODDS-High remained a significant adverse prognostic factor, while LODDS-Intermediate did not reach statistical significance but showed a trend toward increased risk. Notably, the risk magnitude associated with LODDS-High was comparable to that of pN2, supporting its strong prognostic stratification capability (Figs. 2 and 3). In the multivariable analysis for RFS, the pN-based model suggested that cycles of neoadjuvant therapy ≥3, age ≥63 years, and male sex were independently associated with worse RFS, whereas MPR was protective; additionally, moderate differentiation was associated with better RFS. Among pN categories, the adverse prognostic effect of pN2 was the most evident. In the corresponding LODDS-based model, cycles of neoadjuvant therapy ≥3, age ≥63 years, male sex, and LODDS-High remained independent adverse prognostic factors, while MPR retained its protective effect. Moreover, LODDS categories demonstrated an increasing risk gradient (a trend for LODDS-Intermediate and a significant effect for LODDS-High), further supporting the stability of LODDS for recurrence risk assessment (Figs. 4 and 5). In model performance comparisons, LODDS-based models showed lower AIC and BIC than the corresponding pN-based models for both OS and RFS, indicating better model fit. For OS prediction, the AIC values for the LODDS and pN models were 749.7 and 753.4, with corresponding BIC values of 765.9 and 771.9; for RFS prediction, the AIC values were 978.1 and 985.8, with corresponding BIC values of 1001.2 and 1011.1. Harrell’s C-index further favored the LODDS model, with C-index values of 0.743 (OS) and 0.735 (RFS), slightly higher than those of the pN model (0.737 and 0.723, respectively). Time-dependent ROC analysis showed that LODDS had a slightly higher or comparable AUC at 18 and 36 months, suggesting better long-term discrimination. For OS prediction, the AUC at 18 months was 0.762 for the pN-based model and 0.750 for the LODDS-based model, whereas at 36 months, the LODDS-based model showed a higher AUC (0.814 vs 0.797). For RFS prediction, the AUC at 18 months was 0.757 for the pN model and 0.767 for the LODDS model, and at 36 months, the LODDS model showed a higher AUC (0.873 vs 0.844) (Fig. 6). At the 3-year time point, Brier scores were 0.202 (null model), 0.159 (pN model), and 0.151 (LODDS model) for OS, and 0.233 (null model), 0.155 (pN model), and 0.141 (LODDS model) for RFS. The LODDS model yielded the lowest Brier scores, indicating the smallest overall prediction error and superior predictive performance. Decision curve analysis (DCA) further supported the clinical utility of LODDS-based models (Fig. 7). In 3-year OS and RFS DCA, the LODDS model curve (red) was generally above the pN model curve (blue) across commonly used clinical threshold probabilities (approximately 0.10–0.40), with more pronounced net benefit advantages in the 0.20–0.35 range; both models outperformed the “treat-all” and “treat-none” strategies. Incremental value analyses showed that although IDI and NRI at 18 and 36 months did not reach conventional statistical significance, most estimates were positive and favored the LODDS model (Table 3). This trend was consistent with results from Cox regression, ROC metrics, Brier scores, and DCA, suggesting that replacing pN with LODDS may improve overall prognostic performance to some extent. Overall, LODDS demonstrated superior prognostic performance compared with traditional pN staging in this study. LODDS provided a clear risk gradient and showed advantages in model fit, long-term discrimination, prediction consistency, and clinical net benefit, offering a more informative lymph node–based assessment for postoperative prognostic evaluation. DISCUSSION Using a multicenter cohort of 305 ESCC patients treated with nICT followed by surgery from four centers (Beijing Chest Hospital, Shaanxi Provincial Hospital, Anyang Cancer Hospital, and Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences), this study systematically compared the prognostic value of log odds of positive lymph nodes (LODDS) and the current AJCC 8th edition pathological N staging (pN) in the era of neoadjuvant immunotherapy. The main findings are summarized as follows. First, LODDS stratified patients into three groups with clearly separated survival curves for both OS and RFS; after adjustment for key clinicopathological variables, the high-LODDS group remained an independent adverse prognostic factor. Second, compared with pN-based models, LODDS-based multivariable models showed better model fit (lower AIC/BIC), slightly higher C-index, and lower 3-year Brier scores for both OS and RFS. Third, DCA demonstrated higher clinical net benefit of the LODDS model across clinically relevant threshold probabilities, and IDI/NRI analyses showed trends favoring LODDS. Collectively, these results suggest that in ESCC patients treated with nICT followed by surgery, LODDS may be a more powerful and robust lymph node–based prognostic indicator than traditional pN staging. With immune checkpoint inhibitors incorporated into neoadjuvant treatment, the therapeutic landscape for resectable ESCC is undergoing substantial changes.[ 20 – 23 ] However, the traditional pN staging system was developed before the advent of immunotherapy and may not fully capture lymph node biology after nICT. Immunotherapy can induce immune activation, remodeling of the lymph node immune microenvironment, and fibrosis, potentially affecting pathological evaluation of lymph nodes,[ 24 ] which may partly explain the attenuated prognostic performance of pN for OS and RFS observed in this study. Traditional pN staging, established in the pre-immunotherapy era, is defined solely by the number of metastatic lymph nodes.[ 8 ] Although pN has long been considered one of the most important determinants of postoperative prognosis in esophageal cancer,[ 25 ] it does not account for the total number of examined lymph nodes and is therefore highly sensitive to variations in surgical and pathological practices.[ 9 – 12 ] Inadequate lymphadenectomy or incomplete pathological assessment may result in missed nodal metastases and lead to “stage migration,” whereby patients with similar tumor biology are classified into different nodal categories. This limitation is particularly prominent in real-world practice, as the number of retrieved lymph nodes varies substantially across institutions and surgeons.[ 9 , 10 , 12 ] LODDS was proposed as an alternative metric integrating information from both positive and negative lymph nodes into a continuous variable. By reflecting both metastatic burden and the extent of lymph node examination, LODDS is less influenced by variability in the total number of examined lymph nodes and can also differentiate patients with zero positive nodes but differing numbers of examined nodes. Previous studies in esophageal cancer and other gastrointestinal malignancies have suggested that LODDS often outperforms traditional indicators such as pN and lymph node ratio (LNR) [ 11 , 13 – 18 , 24 , 25 ]. Cao et al. reported that LODDS predicts prognosis after esophagectomy more accurately than the number of positive nodes or LNR.[ 13 ] Wu et al. further demonstrated that LODDS-based stratification was superior to pN staging in ESCC patients undergoing primary esophagectomy.[ 26 ] The present study extends this evidence to the nICT setting, indicating that even with systemic immunotherapy incorporated into treatment, LODDS retains robust prognostic value. Several mechanisms may explain the superior performance of LODDS in this cohort. First, by incorporating both positive and negative lymph nodes, LODDS reduces the impact of insufficient lymph node retrieval and may lower the risk of stage migration. Second, compared with the discrete categories of pN staging, LODDS provides a finer-grained, continuous characterization of nodal metastatic burden, facilitating more precise risk discrimination. Third, under nICT, changes in the tumor immune microenvironment and patterns of residual nodal disease may occur,[ 24 ] and LODDS may better capture the combined effects of treatment response and residual nodal involvement than simply counting positive lymph nodes. In addition to these model comparison metrics, time-dependent ROC analyses further affirmed the prognostic power of LODDS. At 18 and 36 months, the LODDS-based model consistently showed comparable or slightly higher AUC values than the pN-based model. For OS prediction, LODDS achieved an AUC of 0.814 at 36 months, compared to 0.797 for pN, and for RFS, LODDS had an AUC of 0.873 at 36 months, compared to 0.844 for pN. These findings suggest that LODDS provides better long-term discrimination between patients at risk for recurrence or death, highlighting its potential utility in predicting outcomes over time. The improvement in model performance with LODDS over pN is particularly important in the clinical setting, where accurate long-term predictions are crucial for guiding patient management. The higher AUC values and lower prediction error for LODDS demonstrate its robustness in capturing the complexity of lymph node involvement, which is not fully addressed by the pN staging system. This superior performance further supports the clinical applicability of LODDS as a more reliable lymph node-based prognostic factor for ESCC patients after neoadjuvant immunochemotherapy. Clinically, these findings have several implications. The consistent advantages of the LODDS-based model across multiple performance metrics suggest that LODDS may help optimize postoperative risk stratification after nICT. Patients with high LODDS may face higher risks of recurrence and death and could benefit from intensified surveillance and consideration of adjuvant therapy, whereas those with low LODDS may be candidates for de-escalated follow-up intensity. Importantly, LODDS can be readily calculated from routine pathological data without additional cost or equipment, supporting its feasibility for clinical implementation. This study has several limitations. First, the retrospective design inevitably introduces selection bias and residual confounding, despite multivariable adjustment. Second, although multicenter, the overall sample size remains limited, which may reduce statistical power for subgroup analyses and reclassification metrics; this may partly explain why IDI and NRI showed favorable trends but did not reach conventional statistical significance. Third, heterogeneity in nICT regimens, number of treatment cycles, and surgical techniques across centers may have influenced outcomes, although this heterogeneity also reflects real-world practice. Fourth, no universally accepted LODDS cutoffs exist for ESCC, particularly in the nICT context; thresholds vary across studies and further research is needed to establish more robust, treatment-adapted stratification standards. Finally, lymph node retrieval and pathological assessment were performed by different surgeons and pathologists across centers; although standardized procedures were followed, subtle inter-institutional differences cannot be fully excluded. Despite these limitations, the study provides clinically meaningful evidence that in ESCC patients undergoing surgery after nICT, LODDS offers a more informative lymph node–based prognostic assessment than traditional pN staging. As immunotherapy continues to expand, incorporating LODDS may further refine postoperative risk stratification and help guide adjuvant treatment decisions and follow-up strategies. Prospective studies with larger sample sizes and external validation are warranted to confirm these findings and to explore the feasibility of integrating LODDS into future staging systems and prognostic nomograms. CONCLUSION In ESCC patients undergoing esophagectomy after neoadjuvant immunochemotherapy, log odds of positive lymph nodes (LODDS) demonstrated superior prognostic performance compared with traditional pathological N staging (pN). LODDS provided a clearer risk gradient, improved model fit and discrimination, reduced prediction error, and yielded higher clinical net benefit. These findings support LODDS as a valuable lymph node assessment approach in the nICT era and suggest its potential utility for postoperative risk stratification and individualized clinical management in ESCC patient Abbreviations AJCC American Joint Committee on Cancer AIC Akaike information criterion AUC Area under the curve BIC Bayesian information criterion CI Confidence interval C-index Concordance index CT Computed tomography DCA Decision curve analysis EC Esophageal cancer ESCC Esophageal squamous cell carcinoma ESMO European Society for Medical Oncology HR Hazard ratio ICIs Immune checkpoint inhibitors IDI Integrated discrimination improvement KM Kaplan–Meier LODDS log odds of positive lymph nodes LNR lymph node ratio MDT multidisciplinary team MPR major pathological response MRI magnetic resonance imaging nICT neoadjuvant immunochemotherapy NRI net reclassification improvement OS overall survival PET-CT positron emission tomography–computed tomography pCR pathological complete response pLNs positive lymph nodes pN stage pathological N stage RFS recurrence-free survival ROC receiver operating characteristic RECIST Response Evaluation Criteria in Solid Tumors TNM tumor–node–metastasis Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki (2013 revision) and the principles of Good Clinical Practice. The study protocol was reviewed and approved by the Ethics Committee of Beijing Chest Hospital, Capital Medical University (Beijing, China; approval No. LW-2025-025; approval date: 14 August 2025) and the Ethics Committee of Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (Shenzhen, China; ethics acceptance No. YW2024-3-1; approval date: 29 February 2024). All clinical data were de-identified prior to analysis. Given the retrospective nature of the study and the use of anonymized data, the requirement for written informed consent was waived by the ethics committees. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding authors on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors’ contributions Hao Chen and Xiangyang Yu contributed equally to this work and share first authorship. Hao Chen and Xiangyang Yu contributed to data collection, data curation, statistical analysis, visualization, and drafting of the manuscript. Jianfei Zhu and Ran Yang contributed to patient enrollment and data acquisition at their respective centers, and critically revised the manuscript for important intellectual content. Yi Han and Feng Wang contributed to study conception and design, supervision, interpretation of results, and critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Authors’ information Not applicable. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Prognosis of patients with esophageal squamous cell carcinoma after esophagectomy using the log odds of positive lymph nodes. Oncotarget. 2015;6:36911–22. https://doi.org/10.18632/oncotarget.5366. Tables Tables 1 and 3 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Jan, 2026 Editor invited by journal 29 Dec, 2025 Editor assigned by journal 28 Dec, 2025 Submission checks completed at journal 28 Dec, 2025 First submitted to journal 23 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8438230","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578220806,"identity":"1559d2e2-339c-44d3-a52f-6d7d934a6838","order_by":0,"name":"Hao Chen","email":"","orcid":"","institution":"Beijing Chest Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Chen","suffix":""},{"id":578220807,"identity":"b7c2a920-d900-47fa-b297-30a922b8319b","order_by":1,"name":"Xiangyang Yu","email":"","orcid":"","institution":"Shenzhen Hospital, National Cancer Center/Cancer 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1","display":"","copyAsset":false,"role":"figure","size":2287682,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival by pN stage and LODDS groups.\u003c/p\u003e\n\u003cp\u003eKaplan–Meier curves for overall survival (OS) and recurrence-free survival (RFS) in 305 esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant immunochemotherapy followed by curative surgery. (A) OS by pathological N (pN) stage (AJCC 8th edition). (B) RFS by pN stage. (C) OS by log odds of positive lymph nodes (LODDS) risk groups. (D) RFS by LODDS risk groups. Shaded areas indicate 95% confidence intervals; numbers at risk are shown. P values were calculated using the log-rank test.\u003c/p\u003e","description":"","filename":"Figure1.KM2x2risktablefixed.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/89e36222f71af5b4bfbcbe20.png"},{"id":100883873,"identity":"7d1f014f-ac6d-4290-8aad-500abc2d038a","added_by":"auto","created_at":"2026-01-22 11:41:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142465,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Cox model for OS with pN stage.\u003c/p\u003e\n\u003cp\u003eForest plot of adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) from the multivariable Cox proportional hazards model for OS, including pN stage as the lymph node variable. Squares denote HRs and horizontal lines denote 95% CIs; reference categories are indicated. Model covariates are shown in the plot.\u003c/p\u003e","description":"","filename":"Figure2ForestOSpN.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/c050179c6e52eae79bca2584.png"},{"id":100883889,"identity":"c58a5d89-61a5-4619-a2f1-5c04b54b6554","added_by":"auto","created_at":"2026-01-22 11:41:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132270,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Cox model for OS with LODDS groups.\u003c/p\u003e\n\u003cp\u003eForest plot of adjusted HRs and 95% CIs from the multivariable Cox proportional hazards model for OS, including LODDS risk groups as the lymph node variable. Squares denote HRs and horizontal lines denote 95% CIs; reference categories are indicated. Model covariates are shown in the plot.\u003c/p\u003e","description":"","filename":"Figure3ForestOSLODDS.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/8a81c5052301e565e632918c.png"},{"id":100883909,"identity":"2e97f76e-7cbc-4640-a0c9-e8f3c9831108","added_by":"auto","created_at":"2026-01-22 11:41:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157257,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Cox model for RFS with pN stage.\u003c/p\u003e\n\u003cp\u003eForest plot of adjusted HRs and 95% CIs from the multivariable Cox proportional hazards model for RFS, including pN stage as the lymph node variable. Squares denote HRs and horizontal lines denote 95% CIs; reference categories are indicated. Model covariates are shown in the plot.\u003c/p\u003e","description":"","filename":"Figure4ForestRFSpN.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/70f7d724d6ce3ace9a497e54.png"},{"id":100883636,"identity":"942d283a-a9be-4567-8795-74b67f934c6a","added_by":"auto","created_at":"2026-01-22 11:40:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148394,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable Cox model for RFS with LODDS groups.\u003c/p\u003e\n\u003cp\u003eForest plot of adjusted HRs and 95% CIs from the multivariable Cox proportional hazards model for RFS, including LODDS risk groups as the lymph node variable. Squares denote HRs and horizontal lines denote 95% CIs; reference categories are indicated. Model covariates are shown in the plot.\u003c/p\u003e","description":"","filename":"Figure5ForestRFSLODDS.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/038657cf2e388aae99475f4e.png"},{"id":100883663,"identity":"bd0ee1dc-e47a-47d1-a762-6f8f0801f376","added_by":"auto","created_at":"2026-01-22 11:40:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1487543,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves for pN- and LODDS-based models.\u003c/p\u003e\n\u003cp\u003eTime-dependent receiver operating characteristic (ROC) curves comparing the multivariable pN-based model and the multivariable LODDS-based model for predicting OS and RFS at 18 and 36 months. The area under the curve (AUC) for each model and time point is reported within each panel; the dashed diagonal indicates no-discrimination.\u003c/p\u003e","description":"","filename":"Figure6.TimeROC2x2.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/492b683a980ae02f21e4945f.png"},{"id":100883794,"identity":"590f0e60-3ce7-458f-9cd1-809b81bc3f91","added_by":"auto","created_at":"2026-01-22 11:41:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":260432,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis at 36 months.\u003c/p\u003e\n\u003cp\u003eDecision curve analysis (DCA) comparing the clinical net benefit of the multivariable pN-based model and the multivariable LODDS-based model for predicting (left) OS and (right) RFS at 36 months. Net benefit is plotted against threshold probability. The “treat-all” and “treat-none” strategies are shown for reference.\u003c/p\u003e","description":"","filename":"Figure7DCAOSRFS2pane.png","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/c4638b5b8434e62234f73eb5.png"},{"id":100953330,"identity":"48d5eff3-71bd-41fb-a241-9b95f354ad66","added_by":"auto","created_at":"2026-01-23 07:21:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5020275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/f8b1e215-d1e8-4a02-9aea-c87e48acd821.pdf"},{"id":100883959,"identity":"ed030dbd-2dcb-4b0a-af0c-aa0d41949140","added_by":"auto","created_at":"2026-01-22 11:41:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31236,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8438230/v1/10a32685392989b01a83ccff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the prognostic value of LODDS and pN stage for esophageal squamous cell carcinoma patients treated with neoadjuvant immunochemotherapy: a multicenter retrospective study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eEsophageal cancer (EC) remains one of the major cancer types worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and esophageal squamous cell carcinoma (ESCC) is the most common histological subtype globally[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At present, surgery (including radical esophagectomy and lymph node dissection) is the main treatment for EC. However, many patients are diagnosed at an advanced stage and miss the opportunity for surgery[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, given the substantial benefits of immunotherapy in the treatment of advanced cancers, an emerging hotspot\u0026mdash;neoadjuvant immunochemotherapy (nICT)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026mdash;has significantly influenced treatment strategies for advanced EC and improved overall survival in patients with metastatic disease[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate staging is crucial for guiding appropriate treatment and predicting prognosis. The post-neoadjuvant pathological TNM (ypTNM) staging system in the 8th edition of the American Joint Committee on Cancer (AJCC) is the most widely used tool for prognostic evaluation of EC after neoadjuvant therapy. N staging based on the number of metastatic lymph nodes remains the most commonly used prognostic indicator for EC[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the current staging system relies only on the number of positive lymph nodes and does not consider the impact of the total number of examined lymph nodes. Moreover, it is highly susceptible to differences in surgical practice and specimen examination, making the staging results vulnerable to insufficient lymph node dissection and individual factors of pathologic assessment, thereby resulting in stage migration[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLog odds of positive lymph nodes (LODDS) is a novel lymph node\u0026ndash;related prognostic factor. LODDS is calculated as LODDS\u0026thinsp;=\u0026thinsp;ln[(pLNs\u0026thinsp;+\u0026thinsp;0.5) / (nLNs\u0026thinsp;+\u0026thinsp;0.5)]. In multiple studies, LODDS has been confirmed to be a better prognostic factor in bladder cancer, pancreatic cancer, rectal cancer, lung cancer, and EC[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the predictive value of LODDS in ESCC patients undergoing surgery after nICT has not yet been investigated.\u003c/p\u003e \u003cp\u003eIn this study, by comparing the prognostic value of LODDS and traditional N staging in ESCC patients treated with surgery after nICT, we aim to propose a better lymph node staging indicator for this population, thereby improving the current lymph node staging scheme.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy Cohort\u003c/p\u003e\n\u003cp\u003eWe retrospectively collected data on patients with esophageal squamous cell carcinoma (ESCC) who underwent surgical treatment after neoadjuvant immunochemotherapy (nICT) at four centers in China (Beijing Chest Hospital, Shaanxi Provincial Hospital, Anyang Cancer Hospital, and Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences) between January 2019 and December 2024. Eligible patients had stage II\u0026ndash;III disease as defined by the 8th edition of the American Joint Committee on Cancer (AJCC) staging manual. This study aimed to compare the prognostic performance of log odds of positive lymph nodes (LODDS) and pathological N stage (pN stage) in ESCC patients treated with nICT followed by surgery.\u003c/p\u003e\n\u003cp\u003eExclusion criteria were: age\u0026thinsp;\u0026lt;\u0026thinsp;18 years at diagnosis; receipt of esophagectomy alone or endoscopic submucosal dissection; neoadjuvant therapy without immune checkpoint inhibitors (ICIs); participation in interventional clinical trials; history of other malignancies before or after esophagectomy; and incomplete clinicopathological or follow-up information. Ultimately, 305 ESCC patients treated with nICT followed by surgery were included.\u003c/p\u003e\n\u003cp\u003eIn this study, detailed demographic, imaging, surgical, pathological, and follow-up data were extracted from the hospital information systems and recorded in standardized case report forms. The surgical approach and neoadjuvant regimen were determined and implemented by the local multidisciplinary team (MDT) according to the European Society for Medical Oncology (ESMO) and Chinese Society of Clinical Oncology clinical practice guidelines, the expert consensus on perioperative management of esophageal cancer, or the latest clinical evidence,[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] and were carried out accordingly.\u003c/p\u003e\n\u003cp\u003ePre-treatment work-up routinely included contrast-enhanced computed tomography (CT) of the neck, chest, and abdomen; esophagogastroduodenoscopy combined with endoscopic ultrasonography; double-contrast upper gastrointestinal radiography; and cervical ultrasonography for diagnosis, localization, and staging. Brain magnetic resonance imaging (MRI), radionuclide whole-body bone scanning, and positron emission tomography were performed when clinically indicated to exclude occult distant metastases.\u003c/p\u003e\n\u003cp\u003eContrast-enhanced CT scans of the neck, chest, and abdomen were repeated after every two cycles of neoadjuvant immunochemotherapy and within one week before esophagectomy, and the best radiologic response of the primary tumor was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1.\u003c/p\u003e\n\u003cp\u003ePost-treatment surveillance routinely included contrast-enhanced chest CT and cervical/abdominal ultrasonography every 3 months for 2 years and every 6 months thereafter until recurrence or death. When necessary, esophagogastroduodenoscopy, brain MRI, and PET-CT were used to differentiate true recurrence from metastasis. MPR was defined as \u0026le;\u0026thinsp;10% residual viable tumor cells in the resected primary tumor specimen.\u003c/p\u003e\n\u003cp\u003eIn addition to the above, MDT decisions regarding adjuvant therapy were mainly influenced by postoperative complications, pathological response, and patient preferences. Lymphadenectomy was performed using standard or extended (en bloc) techniques. According to the AJCC 8th edition pathological TNM system, pN1 indicates 1\u0026ndash;2 metastatic lymph nodes, pN2 indicates 3\u0026ndash;6 metastatic lymph nodes, and pN3 indicates\u0026thinsp;\u0026ge;\u0026thinsp;7 metastatic lymph nodes. LODDS was calculated as ln[(pLNs\u0026thinsp;+\u0026thinsp;0.5)/(nLNs\u0026thinsp;+\u0026thinsp;0.5)], where pLNs represent the number of positive lymph nodes and nLNs represent the number of negative lymph nodes (total retrieved lymph nodes minus pLNs). Three independent thoracic surgeons assessed the quality of all collected data and cross-checked the dataset before statistical analyses.\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Committee of Beijing Chest Hospital, Capital Medical University (approval No. LW-2025-025; approval date: 14 August 2025) and the Ethics Committee of Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (ethics acceptance No. YW2024-3-1; approval date: 29 February 2024). Written informed consent was waived due to the retrospective design and the use of de-identified clinical data. The study was conducted in accordance with the Declaration of Helsinki (2013 revision) and Good Clinical Practice guidelines.\u003c/p\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003cp\u003eOverall survival (OS) was defined as the time from surgery to death from any cause; patients alive at last follow-up were censored. Recurrence-free survival (RFS) was defined as the time from surgery to the first documented recurrence (local/regional or distant) or death from any cause, whichever occurred first; patients alive without recurrence were censored at last follow-up.\u003c/p\u003e\n\u003cp\u003eFollow-Up\u003c/p\u003e\n\u003cp\u003eFollow-up was performed every 3 months during the first 2 years, every 6 months from year 3 to year 5, and annually thereafter. Patient status was assessed using blood tests, tumor markers, chest CT, and abdominal ultrasonography. Brain MRI was performed annually. All patients were followed until December 31, 2024, or death, whichever occurred first.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe optimal cutoff values for LODDS were determined using X-tile software (version 3.6.1; Yale University, USA), and patients were categorized into low-, intermediate-, and high-risk LODDS groups. Kaplan\u0026ndash;Meier methods were used to estimate overall survival (OS) and recurrence-free survival (RFS), and differences between groups were compared using the log-rank test. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003cp\u003eUnivariate Cox proportional hazards regression was first performed for all candidate clinical and pathological variables. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analyses were entered into multivariable Cox regression models. To compare the prognostic performance of different lymph node evaluation approaches, two sets of multivariable models were constructed with either traditional pN stage or LODDS as the key variable while keeping other covariates identical. Each model reported hazard ratios (HRs), 95% confidence intervals (CIs), and Harrell\u0026rsquo;s concordance index (C-index), and P values were calculated based on the likelihood ratio test to assess covariate significance and overall model discrimination. The proportional hazards assumption was assessed using Schoenfeld residuals. Missing data, if any, were handled using complete-case analysis.\u003c/p\u003e\n \u003cp\u003eModel discrimination was evaluated using the C-index and time-dependent receiver operating characteristic (ROC) curves with the area under the curve (AUC) at 18 and 36 months. Model fit was compared using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), with lower AIC/BIC values indicating better fit. Overall prediction error at 36 months was assessed using the Brier score, and multivariable models based on pN stage and LODDS were compared against a null model at 36 months; lower Brier scores indicate smaller prediction error and better predictive performance.\u003c/p\u003e\n \u003cp\u003eClinical utility was assessed using decision curve analysis (DCA). Based on the 36-month event risk predicted by each Cox model, net benefit across a range of threshold probabilities was calculated and compared with \u0026ldquo;treat-all\u0026rdquo; and \u0026ldquo;treat-none\u0026rdquo; strategies to evaluate the clinical usefulness of pN- and LODDS-based models.\u003c/p\u003e\n \u003cp\u003eTo further quantify the incremental predictive value of LODDS over pN stage, time-dependent integrated discrimination improvement (IDI) and category-free net reclassification improvement (NRI) at 18 and 36 months were calculated using the survIDINRI package.\u003c/p\u003e\n \u003cp\u003eAll statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). The main R packages included survival, survminer, timeROC, rms, riskRegression, rmda, and survIDINRI.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 305 patients with esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant immunochemotherapy (nICT) followed by surgery were included. Based on the optimal cut points determined by X-tile, LODDS was categorized into three levels: low (≤ −3.14), intermediate (\u0026gt; −3.14 to ≤ −2.10), and high (\u0026gt; −2.10), accounting for 60.7%, 22.0%, and 17.4% of patients, respectively. During follow-up, 75 overall survival (OS) events and 96 recurrence-free survival (RFS) events were recorded. Baseline characteristics and univariate Cox regression results for OS and RFS are presented in Table 1A and Table 1B, respectively.\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival curves showed that for both OS and RFS, survival probabilities decreased significantly with increasing pN stage or LODDS category (Fig. 1). Compared with pN stage, the survival curves across LODDS categories exhibited clearer separation, suggesting stronger discriminative ability for survival differences.\u003c/p\u003e\n\u003cp\u003eDetails of univariate Cox regression are shown in Table 1A (OS) and Table 1B (RFS). Variables with P \u0026lt; 0.10 in univariate analyses were entered into multivariable Cox regression models. In the multivariable model for OS, the pN-based model indicated that, after adjustment for covariates, cycles of neoadjuvant therapy ≥3 were independently associated with worse OS, whereas major pathological response (MPR) was an independent protective factor; with respect to nodal staging, both pN1 and pN2 were independent adverse prognostic factors. After replacing pN with LODDS, the independent effects of cycles of neoadjuvant therapy ≥3 and MPR remained stable; LODDS-High remained a significant adverse prognostic factor, while LODDS-Intermediate did not reach statistical significance but showed a trend toward increased risk. Notably, the risk magnitude associated with LODDS-High was comparable to that of pN2, supporting its strong prognostic stratification capability (Figs. 2 and 3).\u003c/p\u003e\n\u003cp\u003eIn the multivariable analysis for RFS, the pN-based model suggested that cycles of neoadjuvant therapy ≥3, age ≥63 years, and male sex were independently associated with worse RFS, whereas MPR was protective; additionally, moderate differentiation was associated with better RFS. Among pN categories, the adverse prognostic effect of pN2 was the most evident. In the corresponding LODDS-based model, cycles of neoadjuvant therapy ≥3, age ≥63 years, male sex, and LODDS-High remained independent adverse prognostic factors, while MPR retained its protective effect. Moreover, LODDS categories demonstrated an increasing risk gradient (a trend for LODDS-Intermediate and a significant effect for LODDS-High), further supporting the stability of LODDS for recurrence risk assessment (Figs. 4 and 5).\u003c/p\u003e\n\u003cp\u003eIn model performance comparisons, LODDS-based models showed lower AIC and BIC than the corresponding pN-based models for both OS and RFS, indicating better model fit. For OS prediction, the AIC values for the LODDS and pN models were 749.7 and 753.4, with corresponding BIC values of 765.9 and 771.9; for RFS prediction, the AIC values were 978.1 and 985.8, with corresponding BIC values of 1001.2 and 1011.1. Harrell’s C-index further favored the LODDS model, with C-index values of 0.743 (OS) and 0.735 (RFS), slightly higher than those of the pN model (0.737 and 0.723, respectively).\u003c/p\u003e\n\u003cp\u003eTime-dependent ROC analysis showed that LODDS had a slightly higher or comparable AUC at 18 and 36 months, suggesting better long-term discrimination. For OS prediction, the AUC at 18 months was 0.762 for the pN-based model and 0.750 for the LODDS-based model, whereas at 36 months, the LODDS-based model showed a higher AUC (0.814 vs 0.797). For RFS prediction, the AUC at 18 months was 0.757 for the pN model and 0.767 for the LODDS model, and at 36 months, the LODDS model showed a higher AUC (0.873 vs 0.844) (Fig. 6).\u003c/p\u003e\n\u003cp\u003eAt the 3-year time point, Brier scores were 0.202 (null model), 0.159 (pN model), and 0.151 (LODDS model) for OS, and 0.233 (null model), 0.155 (pN model), and 0.141 (LODDS model) for RFS. The LODDS model yielded the lowest Brier scores, indicating the smallest overall prediction error and superior predictive performance.\u003c/p\u003e\n\u003cp\u003eDecision curve analysis (DCA) further supported the clinical utility of LODDS-based models (Fig. 7). In 3-year OS and RFS DCA, the LODDS model curve (red) was generally above the pN model curve (blue) across commonly used clinical threshold probabilities (approximately 0.10–0.40), with more pronounced net benefit advantages in the 0.20–0.35 range; both models outperformed the “treat-all” and “treat-none” strategies. Incremental value analyses showed that although IDI and NRI at 18 and 36 months did not reach conventional statistical significance, most estimates were positive and favored the LODDS model (Table 3). This trend was consistent with results from Cox regression, ROC metrics, Brier scores, and DCA, suggesting that replacing pN with LODDS may improve overall prognostic performance to some extent.\u003c/p\u003e\n\u003cp\u003eOverall, LODDS demonstrated superior prognostic performance compared with traditional pN staging in this study. LODDS provided a clear risk gradient and showed advantages in model fit, long-term discrimination, prediction consistency, and clinical net benefit, offering a more informative lymph node–based assessment for postoperative prognostic evaluation.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e Using a multicenter cohort of 305 ESCC patients treated with nICT followed by surgery from four centers (Beijing Chest Hospital, Shaanxi Provincial Hospital, Anyang Cancer Hospital, and Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences), this study systematically compared the prognostic value of log odds of positive lymph nodes (LODDS) and the current AJCC 8th edition pathological N staging (pN) in the era of neoadjuvant immunotherapy. The main findings are summarized as follows. First, LODDS stratified patients into three groups with clearly separated survival curves for both OS and RFS; after adjustment for key clinicopathological variables, the high-LODDS group remained an independent adverse prognostic factor. Second, compared with pN-based models, LODDS-based multivariable models showed better model fit (lower AIC/BIC), slightly higher C-index, and lower 3-year Brier scores for both OS and RFS. Third, DCA demonstrated higher clinical net benefit of the LODDS model across clinically relevant threshold probabilities, and IDI/NRI analyses showed trends favoring LODDS. Collectively, these results suggest that in ESCC patients treated with nICT followed by surgery, LODDS may be a more powerful and robust lymph node\u0026ndash;based prognostic indicator than traditional pN staging.\u003c/p\u003e \u003cp\u003eWith immune checkpoint inhibitors incorporated into neoadjuvant treatment, the therapeutic landscape for resectable ESCC is undergoing substantial changes.[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] However, the traditional pN staging system was developed before the advent of immunotherapy and may not fully capture lymph node biology after nICT. Immunotherapy can induce immune activation, remodeling of the lymph node immune microenvironment, and fibrosis, potentially affecting pathological evaluation of lymph nodes,[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] which may partly explain the attenuated prognostic performance of pN for OS and RFS observed in this study.\u003c/p\u003e \u003cp\u003eTraditional pN staging, established in the pre-immunotherapy era, is defined solely by the number of metastatic lymph nodes.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Although pN has long been considered one of the most important determinants of postoperative prognosis in esophageal cancer,[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] it does not account for the total number of examined lymph nodes and is therefore highly sensitive to variations in surgical and pathological practices.[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Inadequate lymphadenectomy or incomplete pathological assessment may result in missed nodal metastases and lead to \u0026ldquo;stage migration,\u0026rdquo; whereby patients with similar tumor biology are classified into different nodal categories. This limitation is particularly prominent in real-world practice, as the number of retrieved lymph nodes varies substantially across institutions and surgeons.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eLODDS was proposed as an alternative metric integrating information from both positive and negative lymph nodes into a continuous variable. By reflecting both metastatic burden and the extent of lymph node examination, LODDS is less influenced by variability in the total number of examined lymph nodes and can also differentiate patients with zero positive nodes but differing numbers of examined nodes. Previous studies in esophageal cancer and other gastrointestinal malignancies have suggested that LODDS often outperforms traditional indicators such as pN and lymph node ratio (LNR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cao et al. reported that LODDS predicts prognosis after esophagectomy more accurately than the number of positive nodes or LNR.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Wu et al. further demonstrated that LODDS-based stratification was superior to pN staging in ESCC patients undergoing primary esophagectomy.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] The present study extends this evidence to the nICT setting, indicating that even with systemic immunotherapy incorporated into treatment, LODDS retains robust prognostic value.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain the superior performance of LODDS in this cohort. First, by incorporating both positive and negative lymph nodes, LODDS reduces the impact of insufficient lymph node retrieval and may lower the risk of stage migration. Second, compared with the discrete categories of pN staging, LODDS provides a finer-grained, continuous characterization of nodal metastatic burden, facilitating more precise risk discrimination. Third, under nICT, changes in the tumor immune microenvironment and patterns of residual nodal disease may occur,[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and LODDS may better capture the combined effects of treatment response and residual nodal involvement than simply counting positive lymph nodes.\u003c/p\u003e \u003cp\u003eIn addition to these model comparison metrics, time-dependent ROC analyses further affirmed the prognostic power of LODDS. At 18 and 36 months, the LODDS-based model consistently showed comparable or slightly higher AUC values than the pN-based model. For OS prediction, LODDS achieved an AUC of 0.814 at 36 months, compared to 0.797 for pN, and for RFS, LODDS had an AUC of 0.873 at 36 months, compared to 0.844 for pN. These findings suggest that LODDS provides better long-term discrimination between patients at risk for recurrence or death, highlighting its potential utility in predicting outcomes over time.\u003c/p\u003e \u003cp\u003eThe improvement in model performance with LODDS over pN is particularly important in the clinical setting, where accurate long-term predictions are crucial for guiding patient management. The higher AUC values and lower prediction error for LODDS demonstrate its robustness in capturing the complexity of lymph node involvement, which is not fully addressed by the pN staging system. This superior performance further supports the clinical applicability of LODDS as a more reliable lymph node-based prognostic factor for ESCC patients after neoadjuvant immunochemotherapy.\u003c/p\u003e \u003cp\u003eClinically, these findings have several implications. The consistent advantages of the LODDS-based model across multiple performance metrics suggest that LODDS may help optimize postoperative risk stratification after nICT. Patients with high LODDS may face higher risks of recurrence and death and could benefit from intensified surveillance and consideration of adjuvant therapy, whereas those with low LODDS may be candidates for de-escalated follow-up intensity. Importantly, LODDS can be readily calculated from routine pathological data without additional cost or equipment, supporting its feasibility for clinical implementation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective design inevitably introduces selection bias and residual confounding, despite multivariable adjustment. Second, although multicenter, the overall sample size remains limited, which may reduce statistical power for subgroup analyses and reclassification metrics; this may partly explain why IDI and NRI showed favorable trends but did not reach conventional statistical significance. Third, heterogeneity in nICT regimens, number of treatment cycles, and surgical techniques across centers may have influenced outcomes, although this heterogeneity also reflects real-world practice. Fourth, no universally accepted LODDS cutoffs exist for ESCC, particularly in the nICT context; thresholds vary across studies and further research is needed to establish more robust, treatment-adapted stratification standards. Finally, lymph node retrieval and pathological assessment were performed by different surgeons and pathologists across centers; although standardized procedures were followed, subtle inter-institutional differences cannot be fully excluded.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study provides clinically meaningful evidence that in ESCC patients undergoing surgery after nICT, LODDS offers a more informative lymph node\u0026ndash;based prognostic assessment than traditional pN staging. As immunotherapy continues to expand, incorporating LODDS may further refine postoperative risk stratification and help guide adjuvant treatment decisions and follow-up strategies. Prospective studies with larger sample sizes and external validation are warranted to confirm these findings and to explore the feasibility of integrating LODDS into future staging systems and prognostic nomograms.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn ESCC patients undergoing esophagectomy after neoadjuvant immunochemotherapy, log odds of positive lymph nodes (LODDS) demonstrated superior prognostic performance compared with traditional pathological N staging (pN). LODDS provided a clearer risk gradient, improved model fit and discrimination, reduced prediction error, and yielded higher clinical net benefit. These findings support LODDS as a valuable lymph node assessment approach in the nICT era and suggest its potential utility for postoperative risk stratification and individualized clinical management in ESCC patient\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAJCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConcordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEsophageal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEsophageal squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Society for Medical Oncology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated discrimination improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLODDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elog odds of positive lymph nodes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLNR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymph node ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultidisciplinary team\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor pathological response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003enICT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneoadjuvant immunochemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enet reclassification improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET-CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositron emission tomography\u0026ndash;computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epathological complete response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epLNs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive lymph nodes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epN stage\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epathological N stage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erecurrence-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRECIST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResponse Evaluation Criteria in Solid Tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor\u0026ndash;node\u0026ndash;metastasis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki (2013 revision) and the principles of Good Clinical Practice. The study protocol was reviewed and approved by the Ethics Committee of Beijing Chest Hospital, Capital Medical University (Beijing, China; approval No. LW-2025-025; approval date: 14 August 2025) and the Ethics Committee of Shenzhen Hospital of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (Shenzhen, China; ethics acceptance No. YW2024-3-1; approval date: 29 February 2024). All clinical data were de-identified prior to analysis. Given the retrospective nature of the study and the use of anonymized data, the requirement for written informed consent was waived by the ethics committees.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are not publicly available due to privacy and ethical restrictions but are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Hao Chen and Xiangyang Yu contributed equally to this work and share first authorship. Hao Chen and Xiangyang Yu contributed to data collection, data curation, statistical analysis, visualization, and drafting of the manuscript. Jianfei Zhu and Ran Yang contributed to patient enrollment and data acquisition at their respective centers, and critically revised the manuscript for important intellectual content. Yi Han and Feng Wang contributed to study conception and design, supervision, interpretation of results, and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors’ information\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Sintilimab versus placebo in combination with chemotherapy as first line treatment for locally advanced or metastatic oesophageal squamous cell carcinoma (ORIENT-15): multicentre, randomised, double blind, phase 3 trial. BMJ. 2022;377:e068714. https://doi.org/10.1136/bmj-2021-068714.\u003c/li\u003e\n\u003cli\u003eLuo H, Lu J, Bai Y, Mao T, Wang J, Fan Q, et al. Effect of Camrelizumab vs Placebo Added to Chemotherapy on Survival and Progression-Free Survival in Patients With Advanced or Metastatic Esophageal Squamous Cell Carcinoma: The ESCORT-1st Randomized Clinical Trial. JAMA. 2021;326:916. https://doi.org/10.1001/jama.2021.12836.\u003c/li\u003e\n\u003cli\u003eLiu L, Liu Y, Xu L, Ding Y, Han J, Wang Q, et al. Tumor immune microenvironment remodeling and prognosis of patients with esophageal squamous cell carcinoma after neoadjuvant chemotherapy with and without immunotherapy: a retrospective cohort study. J Thorac Dis. 2024;16:3909\u0026ndash;22. https://doi.org/10.21037/jtd-24-828.\u003c/li\u003e\n\u003cli\u003eTakeno S, Yamashita S-I, Yamamoto S, Takahashi Y, Moroga T, Kawahara K, et al. Number of metastasis-positive lymph node stations is a simple and reliable prognostic factor following surgery in patients with esophageal cancer. Exp Ther Med. 2012;4:1087\u0026ndash;91. https://doi.org/10.3892/etm.2012.705.\u003c/li\u003e\n\u003cli\u003eWu S-G, Sun J-Y, Yang L-C, Zhou J, Li F-Y, Li Q, et al. Prognosis of patients with esophageal squamous cell carcinoma after esophagectomy using the log odds of positive lymph nodes. Oncotarget. 2015;6:36911\u0026ndash;22. https://doi.org/10.18632/oncotarget.5366.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 3 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Esophageal squamous cell carcinoma, Neoadjuvant immunochemotherapy, Log odds of positive lymph nodes, LODDS, Lymph node staging, Prognosis, Overall survival, Recurrence-free survival","lastPublishedDoi":"10.21203/rs.3.rs-8438230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8438230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eAs a novel prognostic factor, log odds of positive lymph nodes (LODDS) has been shown to be associated with the prognosis of many cancers. This study mainly explored whether LODDS is a better lymph node-based prognostic factor compared with traditional pN stage for patients with esophageal squamous cell carcinoma who underwent surgical treatment after neoadjuvant immunochemotherapy.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eA multicenter retrospective cohort of 305 clinical stage II–III esophageal squamous cell carcinoma (ESCC) patients treated with neoadjuvant immunochemotherapy followed by curative surgery at four tertiary centers in China (2019–2024) was analyzed. LODDS was calculated as ln[(pLNs+0.5)/(nLNs+0.5)] (pLNs, positive lymph nodes; nLNs, negative lymph nodes). Patients were stratified into three LODDS risk groups using X-tile. Overall survival (OS) and recurrence-free survival (RFS) were assessed by Kaplan–Meier analysis and log-rank tests. For each endpoint, multivariable Cox models including either pN stage or LODDS (with identical covariates) were compared using Harrell’s C-index, AIC/BIC, 3-year Brier score, decision curve analysis, and time-dependent IDI and category-free NRI.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe 3-year overall survival (OS) and recurrence-free survival (RFS) rates were 71.8% and 63.2%, respectively. Kaplan–Meier analyses showed progressively worse OS and RFS with increasing pN stage and LODDS categories, with clearer separation across LODDS risk groups. After adjustment for clinicopathological covariates, LODDS remained an independent adverse prognostic factor for both endpoints, whereas the prognostic effect of pN stage was weaker and less consistent. Compared with pN stage–based models, LODDS-based models showed slightly higher C-index (OS: 0.743 vs 0.737; RFS: 0.735 vs 0.723), lower AIC/BIC (OS AIC: 749.7 vs 753.4; OS BIC: 765.9 vs 771.9; RFS AIC: 978.1 vs 985.8; RFS BIC: 1001.2 vs 1011.1), and lower 3-year Brier scores (OS: 0.151 vs 0.159; RFS: 0.141 vs 0.155). Decision curve analysis suggested higher net benefit for LODDS-based models across commonly used threshold probabilities. Although IDI and NRI at 18 and 36 months did not reach conventional statistical significance, most estimates favored LODDS and were consistent with other performance metrics.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eFor the ESCC patients treated with surgery following neoadjuvant immunochemotherapy, LODDS is a superior prognostic factor to pN stage.\u003c/p\u003e","manuscriptTitle":"Comparison of the prognostic value of LODDS and pN stage for esophageal squamous cell carcinoma patients treated with neoadjuvant immunochemotherapy: a multicenter retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 11:38:43","doi":"10.21203/rs.3.rs-8438230/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-20T11:16:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-29T11:54:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T01:39:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T01:38:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-24T02:46:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63af06a4-0dfb-44ee-8ff7-71654e1e1028","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T11:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 11:38:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8438230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8438230","identity":"rs-8438230","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}