Esophageal squamous cell carcinoma (ESCC) is one of the most common types of esophageal cancer in Asia, accounting for approximately 90% of all cases (1). Minimally invasive esophagectomy (MIE) is less invasive and has a better prognosis than open esophagectomy (OE), which makes it the most important surgical method in the treatment of ESCC (2, 3). Presently, no tool can effectively predict the prognosis of patients with ESCC. Therefore, it is necessary to develop a prediction model that allows the measurement of the long-term oncology outcome on patients.

Recent evidence indicates that, in addition to tumor characteristics, immune-inflammatory responses in the tumor microenvironment play a critical role in promoting tumorigenesis and progression (4, 5). Past studies have demonstrated that blood indicators, such as derived neutrophil/lymphocyte ratio (dNLR), lymphocyte/monocyte ratio (LMR), systemic immune-inflammation index (SII), and serum lactate dehydrogenase (LDH), can help measure the degree of immune inflammatory response in the tumor microenvironment, which has significant potential for predicting the disease progression in patients with a variety of cancers (6–9). The immune prognostic index (IPI) established by combining dNLR and LDH is considered to be a better biomarker for reflecting the degree of immune inflammatory response in the body than a single index and has demonstrated excellent predictive performance in the lung, esophageal and gastric cancers (10–12).

Nomogram is a tool that can individually and visually evaluate the clinical prognosis in cancer patients, showing accurate predictive value (13–15). Therefore, the present work attempts to establish a new clinical staging system, namely the survival nomogram scoring staging (SNS) that comprehensively considers tumor characteristics (T and N stage) and tumor immunoinflammatory response (IPI) to predict the long-term oncology outcomes in ESCC after MIE and validates its risk prediction values in an independent cohort.

Our study revealed that IPI influence independently in long-term prognosis in ESCC patients after MIE and established a survival nomogram combining IPI and pathological features. When validated in an independent cohort, the nomogram showed better predictive performance and clinical utility than the traditional AJCC staging. We stratified the entire population using survival nomogram scoring criteria for OS and DFS and proposed SNS staging. Further analysis revealed that the risk of death varied depending on the SNS staging and changed over time.

Recent studies have proved that in addition to its tumor characteristics, the tumor microenvironment also promotes the proliferation and invasion of tumor cells (19, 20). The immune inflammatory reaction is an indispensable part of the tumor microenvironment, which is one of the mechanisms that promote tumor angiogenesis and immune resistance. Preoperative peripheral inflammatory markers can indirectly reflect the degree of immune inflammatory response in the tumor microenvironment and are promising prognostic indicators (4, 5). Mezquita et al. for the first time showed that the immune prognostic index (IPI), a new index of circulating inflammation, which was based on dNLR and LDH, could be an ideal prognostic tool for lung cancer (10). Past studies have confirmed that IPI is an independent prognostic factor in esophageal cancer (11, 21). Similarly, our results revealed that high preoperative IPI scores significantly reduced the OS and DFS in ESCC patients after minimally invasive surgery and were confirmed using an independent validation cohort.

In the present study, we found that age (> 65 years) and intraoperative bleeding (≥200 mL) were independent risk factors for adverse OS and DFS in ESCC patients. Older patients often present more comorbidities and poorer nutritional status than younger ones, and consequently higher mortality, as older patients are more likely to have serious complications and side effects after esophagectomy (22, 23). Although related studies have reported that blood transfusion can achieve short-term benefits in esophageal cancer patients with intraoperative massive bleeding, the immunosuppressive effect of allogeneic blood transfusion may reduce the antitumor activity of natural killer cells or T lymphocyte cells, resulting in poor prognosis in such patients (24, 25). Our study found no significant correlation between intraoperative blood loss and variables including age ( Supplementary Figure S6A ), adjuvant chemotherapy ( Supplementary Figure S6B ), grade T ( Supplementary Figure S6C ), grade N ( Supplementary Figure S6D ), tumor location ( Supplementary Figure S6E ), and surgical approach ( Supplementary Figure S6F )-results consistent with previous studies (24). This may be attributed to the characteristics of the tumor types included in our study (e.g., esophageal squamous cell carcinoma), which primarily exhibit local infiltration and involve relatively fixed surgical scopes. Nevertheless, our multivariate Cox model, which included variables such as T stage and N stage, demonstrated that intraoperative blood loss remained independently associated with tumor characteristics (OS: HR=2.649, P=0.01; DFS: HR=2.450, P=0.01), indicating that its prognostic effect cannot be fully explained by tumor staging.

We observed significant correlations between intraoperative blood loss and ASA score, as well as postoperative complications ( Supplementary Figure S6G ). The ASA (American Society of Anesthesiologists) score is a classic indicator for assessing patients’ preoperative systemic status and directly reflects surgical risk (26). There was a significant positive correlation between intraoperative blood loss and ASA score (P=0.006), meaning patients with higher ASA scores (e.g., those with comorbidities such as cardiovascular or cerebrovascular diseases, or respiratory insufficiency) exhibited a significantly increased probability of higher intraoperative blood loss. A plausible mechanism for this phenomenon is that patients with higher ASA scores often present with coagulation dysfunction or impaired vascular regulation, making it more challenging to control intraoperative oozing during surgery. To reduce surgical risk, such patients may require more conservative surgical strategies (e.g., reduced resection extent), which indirectly increases the difficulty of intraoperative hemostasis and elevates blood loss.

More importantly, ASA score itself is an independent predictor of postoperative complications (e.g., infection, organ failure) (27). These complications directly worsen survival outcomes by prolonging hospitalization, increasing the risk of reoperation, or inducing multi-organ dysfunction. Thus, intraoperative blood loss may exert its prognostic effect through a “preoperative systemic status (ASA score) → intraoperative blood loss → postoperative complications → impaired survival” pathway, rather than merely serving as a surrogate for tumor characteristics. Beyond ASA score, we further identified a significant association between intraoperative blood loss and postoperative complications graded ≥ II using the Clavien-Dindo classification (CDc) (P=0.001) ( Supplementary Figure S6H ). Previous studies have demonstrated that postoperative complications not only pose a direct threat to short-term prognosis but also correlate with long-term survival. For instance, patients with anastomotic fistula exhibit a significantly lower 5-year overall survival rate than those without (HR=1.68, 95% CI: 1.25–2.24), which is closely linked to impaired tissue repair capacity and increased infection risk caused by blood loss (28). Therefore, intraoperative blood loss exacerbates survival disadvantages by directly inducing postoperative complications.

The traditional pathological tumor-node-metastasis (TNM) staging system has been widely used for risk stratification and prediction of survival. However, TNM staging only considers the characteristics of the tumor itself and does not include other prognostic factors, which limits its ability of survival prediction (29). Previous studies suggest that postoperative pathological staging combined with other clinical prognostic factors (age, postoperative adjuvant therapy, tissue differentiation, lymphatic vascular invasion, etc.) can predict the long-term oncology outcomes more accurately (30–32). Feng et al. established a nomogram including IPI score to predict cancer-specific survival in ESCC patients, but the sample size included in this study was small, and most of the study population underwent OE. Moreover, the nomogram developed in the study was not validated in other independent cohorts (11). In the present study, we established nomograms that could visually predict OS and DFS in ESCC patients after MIE by considering both IPI scores as well as clinicopathological features. The nomogram obtained in this way had better predictive power than TNM staging by t-AUC curves. In addition, the DCA showed that using this nomogram, the rate of accuracy to predict OS or DFS was consistently higher than that of TNM staging over a wide range of thresholds.

Previous studies on constructing nomogram models for ESCC have largely relied on static anatomical or pathological variables, which fail to comprehensively reflect the dynamic characteristics of tumor biological behavior. For instance, Li et al. (2022) incorporated preoperative/postoperative inflammatory indicators (PreSII, PostSII) and a nutritional index (PNI) but did not address dynamic changes in the immune microenvironment (33). Zhang et al. (2021) focused on anatomical features such as tumor length and lymphovascular space invasion (LVSI), showing significant deficiencies in assessing systemic immune status (34). Deng et al. (2019) only integrated traditional clinicopathological factors (e.g., TNM staging, age), resulting in a single-dimensional predictive model that could not dissect the core drivers of ESCC heterogeneity (32).

Innovatively, our study incorporated the immune prognostic index (IPI, calculated as dNLR + LDH) into the predictive framework. By quantifying the synergistic effects of systemic inflammatory burden (dNLR) and tumor metabolic activity (LDH), we dynamically evaluated the perioperative immune microenvironment status. Additionally, we included intraoperative blood loss (≥200 mL)—a key indicator reflecting surgical stress—thereby filling the knowledge gap in traditional models regarding immune-metabolic crosstalk and perioperative impacts.

In terms of predictive performance, the survival nomogram scoring system (SNS) developed in this study demonstrated significant advantages. The time-dependent area under the curve (AUC) for 5-year overall survival (OS) reached 0.780 in the training cohort and increased to 0.827 in the validation cohort. This performance not only substantially outperformed the AJCC 8th edition staging system (training/validation AUC: 0.687/0.750) but also surpassed the traditional pathological model reported by Deng et al. (training/validation AUC: 0.685/0.744) (32). Notably, although the AUC values of models by Li et al. (training/validation: 0.850/0.797) and Zhang et al. (training/validation: 0.802/0.829) were comparable to ours, our study was based on a large-sample ESCC cohort (613 patients; training set: n=425, validation set: n=188), with a significantly larger sample size than those of Li et al. (n=268) and Zhang et al. (n=399) (33, 34). This provided a more robust statistical foundation and stronger generalizability for our model.

To make nomograms practical in clinical application, we established threshold values for risk assessment. In the training cohort, we developed accurate prediction scores for OS (low risk: ≤170; moderate risk:170 - 219; high risk: >219) and DFS (low risk: ≤167; moderate risk: 167 - 227; high risk: >227). The survival prediction ability of SNS staging was also confirmed in the validation cohort. Further exploration of the dynamic mortality risk in different SNS stagings revealed that the risk of death in patients with high probability showed a double peak, at 12 and 48 months after surgery, respectively. Patients with moderate risk had the highest probability of death at 12 months after surgery, while patients with low risk had the greatest probability of death at 24 months after surgery, and then the risk of death decreased. This finding provides precise temporal anchors for clinical decision-making, such as enhancing imaging surveillance and implementing intervention measures at 12 and 48 months for high-risk groups.

The present study, for the first time, combined immunoinflammatory indicators and clinicopathological features to establish a new comprehensive staging system, SNS staging, which can effectively predict the prognosis in ESCC patients after MIE. However, there are some limitations to our study. First, the selection and analysis biases are inevitable because of single-center retrospective analysis, and more external data is required for validation. Although referencing the dNLR and LDH cutoff values from the gastric cancer cohort allowed us to preliminarily evaluate the prognostic value of the IPI in ESCC (12), the extrapolation validity of these cutoff values requires cautious interpretation. In the future, it will be necessary to re-optimize the thresholds in larger, multicenter ESCC cohorts using internal statistical methods (e.g., ROC curve analysis to determine optimal cutoff values and the X-tile method to dynamically stratify risk groups) and further enhance the generalizability and clinical utility of the IPI score by integrating clinical validation (e.g., consistency of survival curves and relevance to clinical decision-making).Second, our nomogram and comprehensive SNS staging were based on the data of ESCC patients undergoing only surgery, and their clinical significance needs to be further confirmed for patients undergoing neoadjuvant plus surgery. Finally, the IPI score in this study was measured at a single time point before surgery. The impact of postoperative IPI on the prognosis in ESCC patients and the dynamic changes in IPI should be assessed further.

Overall, the survival nomogram combining IPI and clinicopathological features gives a comprehensive prediction model by considering tumor characteristics and immune-inflammatory responses, which can accurately evaluate the long-term outcomes in ESCC patients after minimally invasive surgery. The predictive performance and clinical benefit rate of the survival nomogram were higher than those in conventional AJCC staging. More importantly, based on the survival nomogram score, we developed SNS staging for OS and DFS respectively, which can provide reference values in clinical follow-up.