Development and validation of a nomogram for predicting postoperative survival in gallbladder cancer

Introduction

Gallbladder cancer (GBC) is a rare but highly aggressive malignancy. Globally, it accounts for approximately 1.2% of cancer incidence and 1.7% of cancer-related deaths (1,2). Early-stage GBC is frequently asymptomatic, leading to delayed detection and a high proportion of patients deemed unsuitable for curative-intent surgery. Among those who do undergo resection, the prognosis is still poor, with 5-year overall survival (OS) rates reported to be only 25%–40% (3). Nevertheless, postoperative survival varies widely among individuals, underscoring the marked biological and clinical heterogeneity of GBC (4,5).

The tumor-nodes-metastasis (TNM) staging system is widely used to guide prognostication and treatment decisions (6). However, it mainly reflects tumor burden and overlooks host-related factors as well as imaging phenotypes that may affect postoperative recovery, eligibility for adjuvant therapy, and long-term survival. As a result, patients within the same pathological stage can have substantially different outcomes (7).

Nomograms have become practical tools for individualized prognostication by integrating multidimensional predictors into an intuitive, quantitative risk estimate. In GBC, numerous postoperative models (8-11)—often built on large population-based datasets such as Surveillance, Epidemiology, and End Results (SEER)—have incorporated demographic factors, tumor size and grade, TNM components, and refined lymph-node metrics (12), demonstrating improved discrimination and clinical utility beyond TNM staging alone.

These related studies have incorporated systemic inflammation/immune markers and nutrition-related indicators to reflect host status (13-15), and some have further explored their ability to predict benefit from adjuvant chemotherapy (11,13). Web-based calculators and dynamic nomograms have also been used to facilitate clinical implementation (16). However, clinically interpretable imaging biomarkers remain limited. Most imaging-based models rely on computed tomography/magnetic resonance imaging (CT/MRI) features or radiomics (17), whereas routinely available ultrasonographic phenotypes are incorporated inconsistently in prognostic modeling.

To address these limitations, our study integrates nodal stage with the American Society of Anesthesiologists (ASA) classification to capture physiologic reserve, serum albumin (Alb) to reflect nutritional and systemic status, and a preoperatively accessible ultrasonographic phenotype characterized by hyperechoic tumor appearance. We thereby develop a clinically aligned nomogram for postoperative survival prediction and risk stratification. We present this article in accordance with the TRIPOD reporting checklist (available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0027/rc).

Methods

Study population

This retrospective study included patients with GBC who underwent surgical treatment at Peking Union Medical College Hospital (PUMCH) between August 2008 and December 2022. Inclusion criteria: (I) pathologically confirmed GBC; (II) underwent curative or palliative surgical treatment; (III) complete clinicopathological data; (IV) complete follow-up data. Exclusion criteria: (I) concurrent other malignancies; (II) received neoadjuvant chemotherapy or radiotherapy before surgery; (III) lost to follow-up or incomplete follow-up data.

Initially, 90 patients were collected. After excluding cases with missing OS data, 61 patients were finally included in the analysis. All patients provided informed consent, and the study was approved by the Institutional Review Board of Peking Union Medical College Hospital (No. I-23PJ532). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Data collection

The following information was collected: (I) demographic characteristics: age, sex; (II) preoperative general condition: ASA physical status classification; (III) preoperative laboratory tests: serum Alb, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (Cr), etc.; (IV) preoperative ultrasonography: maximum tumor diameter, blood flow signals, echogenicity (hypoechoic, isoechoic, hyperechoic); (V) pathological features: tumor TNM staging [according to American Joint Committee on Cancer (AJCC) 8^th edition], surgical margin status (R0/R1/R2); (VI) surgical approach: simple cholecystectomy, extended cholecystectomy, etc.

Follow-up

The follow-up cutoff date was August 2024. Follow-up methods included outpatient reviews, telephone follow-up, and medical record queries. OS time was defined as the period from the date of surgery to death or last follow-up date.

Statistical analysis

Variable selection and preprocessing

Initially, 87 variables were collected and screened as follows: (I) excluding variables with more than 20% missing values; (II) excluding variables with a single value accounting for more than 90% of observations. After screening, 50 variables entered subsequent analysis. We used multiple imputation to handle missing values.

Prognostic factor analysis

Univariate Cox proportional hazards regression analysis was used to evaluate the association between each variable and OS. The three variables with the smallest P values from univariate analysis (ASA grade, serum Alb, N stage) and three clinically significant ultrasonographic features (tumor diameter, blood flow signals, echogenicity) were included in the multivariate Cox regression model to identify independent prognostic factors.

Nomogram construction

A prognostic nomogram was constructed based on independent prognostic factors from the multivariate Cox regression model, providing 1-year, 3-year, and 5-year OS probability predictions.

Model validation

Internal validation was performed using the bootstrap method (500 iterations). Evaluation metrics included: (I) concordance index (C-index) to assess overall discrimination; (II) time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC) to assess discrimination at specific time points; (III) calibration curves to assess concordance between predicted probabilities and actual observations.

Risk stratification

The maximally selected rank statistics [maximal selected rank statistics (MaxStat) method] was used to determine the optimal cutoff value for risk scores, classifying patients into high-risk and low-risk groups. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare differences between groups.

All statistical analyses were performed using R software (version 4.5.2). Two-sided tests were used, with P<0.05 considered statistically significant.

Results

Baseline patient characteristics

Among the 61 patients, the median age was 63.9 years (standard deviation 8.4 years), with 28 males (45.9%) and 33 females (54.1%). During follow-up, 33 patients (54.1%) died and 28 patients (45.9%) survived.

Table 1 summarizes baseline patient characteristics stratified by 3-year survival status. Significant differences were observed between survival and non-survival groups across multiple aspects. Pathologically, non-survivors were more likely to have advanced primary tumors (T3/T4 stage: 54.5% vs. 14.3%, P=0.003), distant metastasis (12.1% vs. 7.1%, P=0.02), and positive surgical margins (R1/R2: 27.3% vs. 0.0%, P=0.009). Regarding preoperative biochemical parameters, the non-survival group had lower serum Alb levels (median: 41.0 vs. 42.5 g/L, P=0.02) and higher ALT levels (median: 17.0 vs. 14.0 U/L, P=0.03). No statistically significant differences were found between groups for other demographic, ultrasonographic, and surgical approach variables (all P>0.05).

Univariate analysis of prognostic factors for OS

The results of univariate Cox regression analysis are shown in Table 2. Eight variables demonstrated statistically significant associations with OS (P<0.05): ASA grade [hazard ratio (HR) =2.486, 95% confidence interval (CI): 1.053–5.866, P=0.002], serum Alb (HR =0.885, 95% CI: 0.820–0.956, P=0.004), lymph node metastasis (HR =3.608, 95% CI: 1.695–7.681, P=0.005), among others.

Multivariate analysis of independent prognostic factors

Considering the limited sample size (n=61), to avoid overfitting, six variables were selected for inclusion in the multivariate Cox regression model: the three variables with the smallest P values from univariate analysis (ASA grade, Alb, N stage) and three clinically significant ultrasonographic features (tumor diameter, blood flow signals, echogenicity).

The results of multivariate analysis are shown in Figure 1. Four factors were confirmed as statistically significant independent prognostic factors:

• ASA grade: compared to ASA grade I, ASA grade II had HR =2.88 (95% CI: 1.09–7.57, P=0.03), and ASA grade III had HR =9.68 (95% CI: 2.46–38.01, P=0.001);
• Serum Alb: HR =0.91 per unit increase (95% CI: 0.84–0.99, P=0.03), indicating that lower Alb levels correlate with higher mortality risk;
• N stage: compared to N0 stage, N1 stage had HR =4.72 (95% CI: 1.90–11.74, P<0.001), and N2 stage had HR =3.52 (95% CI: 1.14–10.84, P=0.03);
• Tumor echogenicity: compared to hypoechoic tumors, hyperechoic tumors had HR =7.69 (95% CI: 1.35–43.70, P=0.02).

Figure 1 Forest plot of multivariate Cox regression for independent prognostic factors. Alb, albumin; ASA, American Society of Anesthesiologists; N, node.

Ultrasonographic tumor diameter and blood flow signals did not show statistical significance in the multivariate model (P>0.05). We tested the proportional hazards assumption of the Cox regression model, and the results indicated that the assumption holds.

Construction of prognostic nomogram

Based on the above independent prognostic factors (ASA grade, serum Alb, N stage, ultrasonographic features), a prognostic nomogram was constructed (Figure 2). The nomogram includes point scales for each predictor, a total point scale, and survival probability scales for 1-year, 2-year, and 3-year OS.

Figure 2 Nomogram for predicting postoperative OS in GBC. *, P<0.01; **, P<0.001; ***, P<0.0001. Alb, albumin; ASA, American Society of Anesthesiologists; GBC, gallbladder cancer; N, node; OS, overall survival; Pr, probability.

Instructions for using the nomogram:

• Locate the patient’s value on each variable axis and project upward to the “points” axis to obtain the score for that factor;
• Sum the scores from all factors to obtain the “total points”;
• Locate the total points on the “total points” axis and project downward to the corresponding survival probability axes to read the patient’s predicted 1-year, 2-year, and 3-year survival probabilities.

This nomogram integrates preoperative information (ASA grade, Alb, ultrasonographic echogenicity) and postoperative pathological information (N stage), providing clinicians with individualized prognostic assessment at different time points.

Validation of the nomogram

Discrimination

Internal validation was performed using the bootstrap method (500 iterations). The nomogram’s C-index was 0.733, indicating moderate overall discrimination.

Time-dependent ROC analysis further evaluated the model’s predictive performance at specific time points (Figure 3). The nomogram achieved AUC values of 0.882, 0.845, and 0.840 for predicting 1-year, 2-year, and 3-year OS, respectively, indicating excellent discrimination at both short-term and medium-term follow-up points.

Figure 3 Validation of the nomogram. Time-dependent ROC curves. AUC, area under the curve; ROC, receiver operating characteristic.

Calibration

Calibration curves assessed the concordance between predicted survival probabilities and actual observations. Calibration curves for 1-year and 3-year survival predictions are shown in Figure 4A,4B, respectively. The calibration solid lines (representing bias-corrected performance of the nomogram after bootstrap correction) closely follow the 45-degree ideal diagonal line, indicating good calibration across different ranges of predicted probabilities with accurate predictions and no systematic overestimation or underestimation.

Figure 4 Calibration curves. (A) 1-year survival calibration curve. (B) 3-year survival calibration curve.

Risk stratification based on nomogram

Using the maximally selected rank statistics (MaxStat method), the optimal cutoff value for risk scores was determined, classifying patients into low-risk (n=26) and high-risk (n=35) groups. Kaplan-Meier survival curves showed highly significant differences in OS between the two groups (log-rank test, P<0.001) (Figure 5).

Figure 5 Kaplan-Meier survival curves based on nomogram risk stratification.

Low-risk group patients demonstrated significantly better survival outcomes, while the high-risk group had a median survival time of 14.5 months.

This significant survival difference confirms the clinical utility of this nomogram in effectively identifying patients with poor postoperative prognosis, providing a basis for clinical decision-making.

Discussion

The present study successfully developed and validated a novel nomogram for predicting postoperative survival in patients with GBC, integrating readily available preoperative clinicopathological and ultrasonographic variables. The model, based on four independent prognostic factors (ASA grade, serum Alb, N stage, and tumor echogenicity on ultrasound), demonstrated robust predictive performance and clinical utility, and it effectively stratified patients into distinct risk groups with markedly different survival.

This study confirms that prognosis in GBC is multifactorial, determined not only by tumor-related factors but also by host-related characteristics. Our nomogram effectively synthesizes these dimensions, offering a visualized tool for individualized survival prediction superior to the conventional TNM staging system alone.

Consistent with emerging evidence in surgical oncology (18,19), our study identifies ASA grade as a powerful prognostic indicator, with ASA III patients facing nearly a tenfold increased mortality risk. This underscores that ASA grading transcends its original purpose of assessing anesthesia risk, serving as a composite surrogate for host physiological reserve (20). A higher ASA grade signifies a greater burden of systemic comorbidities, which may impair a patient’s capacity to withstand major surgery, recover postoperatively, and tolerate adjuvant chemotherapy (21,22). Furthermore, the chronic inflammatory or immunosenescent state often associated with comorbidities may foster a permissive microenvironment for micrometastases (23,24). Thus, ASA grade provides a crucial window into the host’s overall resilience against cancer progression.

Our finding that hypoalbuminemia is an independent predictor of poor survival aligns with extensive literature (25-27). It is critical to conceptualize low serum Alb not merely as a marker of malnutrition but, more importantly, as a reflection of a sustained systemic inflammatory response driven by the tumor. Pro-inflammatory cytokines, particularly interleukin-6 (IL-6) released by tumor and stromal cells, suppress hepatic Alb synthesis while promoting the production of acute-phase proteins (28,29). Consequently, low Alb levels signal a tumor-promoting inflammatory milieu, which is linked to cancer cachexia, impaired immune function, and higher rates of complications (30). While composite inflammation-nutrition indices [e.g., fibrinogen-to-albumin ratio (FAR), albumin-to-alkaline phosphatase ratio (AAPR)] have been proposed, the standalone prognostic value and clinical accessibility of serum Alb affirm its practical utility in risk stratification.

The strong prognostic weight of nodal status (N stage) in our model reaffirms its established role as a cornerstone of the TNM system. The significantly elevated hazard associated with N1/N2 disease underscores the dire implications of lymphatic spread. This finding reinforces the imperative for meticulous and adequate lymphadenectomy during surgery to achieve accurate staging, which is pivotal for guiding adjuvant therapy decisions. Even in earlier T-stage tumors, the presence of nodal metastasis dramatically alters the prognosis, necessitating a more aggressive therapeutic approach.

The most intriguing and novel finding of our study is the identification of preoperative ultrasound hyperechogenicity as a powerful independent prognostic factor, associated with a 7.69-fold increased mortality risk compared to hypoechoic tumors. This appears paradoxical, as hypoechogenicity is typically considered a classic malignant feature (31,32). This finding likely points to underlying high-grade pathological subtypes or aggressive biological behaviors. We propose several mechanistic hypotheses:

• Hypothesis A: association with mucinous adenocarcinoma. Mucinous adenocarcinoma, a rare but highly aggressive variant of GBC, often appears hyperechoic or mixed-echoic on ultrasound due to abundant intratumoral mucin pools and fibrous septa that create strong acoustic interfaces (33). This subtype is notorious for its advanced stage at presentation and poor chemosensitivity (34), which could explain the observed survival disparity.
• Hypothesis B: link to porcelain gallbladder or calcification. Diffuse wall calcification (porcelain gallbladder), a known precursor lesion, presents as a bright hyperechoic band with posterior shadowing. Cancers arising in this setting are often diagnosed late due to imaging concealment (35). Alternatively, intratumoral necrosis and dystrophic calcification in rapidly growing tumors can also manifest as hyperechoic foci.
• Hypothesis C: indicator of specific stromal reaction. A dense, scirrhous stromal reaction or particular collagen deposition pattern might alter acoustic properties (36). Therefore, preoperative ultrasound hyperechogenicity may serve as a non-invasive imaging biomarker hinting at a distinct, virulent tumor biology, warranting heightened clinical suspicion and potentially more aggressive management.

However, all these assumptions are based on the reliability of our conclusions. And our conclusions would benefit from validation in a larger sample group.

Compared to the traditional TNM staging, our nomogram offers multidimensional integration of host status, systemic inflammation, preoperative imaging, and pathological staging, enabling more nuanced and individualized prognosis estimation. Its visual format facilitates quick assessment and doctor-patient communication. The model’s ability to stratify patients into distinct risk groups (high vs. low) holds significant practical value. High-risk patients, identifiable preoperatively (via ASA, Alb, ultrasound) and postoperatively (adding N stage), may be prioritized for neoadjuvant/adjuvant therapy trials, intensified surveillance, or comprehensive supportive care.

Previously, there have been prognostic prediction models for GBC established using nomograms (37,38), but compared to articles published previously, the innovation of the nomogram in this article lies in: (I) incorporating variables that are less common in other GBC prognostic models, such as the ASA classification and ultrasound echo characteristics; (II) the discovery that “high echo” is an independent poor prognostic factor holds clinical novelty.

This study has several limitations. The number of samples we included is relatively small. Its retrospective, single-center design with a relatively modest sample size may introduce selection bias and limit the generalizability of the findings. The nomogram requires external validation in larger, prospective, and multicenter cohorts. Furthermore, we did not incorporate emerging molecular or genetic biomarkers, which might further refine prognostic prediction. The interpretation of ultrasound echogenicity, though standardized, can have inter-observer variability.

In conclusion, we have developed a practical prognostic nomogram for GBC that integrates ASA grade, serum Alb, N stage, and tumor echogenicity on ultrasound. This tool underscores the prognostic importance of host physiology and preoperative imaging features alongside established pathological factors. The striking association of hyperechoic ultrasound pattern with poor outcome merits further pathological-radiological correlation studies. This model aids in personalized risk assessment and clinical decision-making, potentially improving patient stratification for tailored management strategies.

Conclusions

This study successfully developed and validated a nomogram integrating clinicopathological and ultrasonographic features for predicting postoperative survival in GBC. Based on four independent prognostic factors (ASA grade, serum Alb, N stage, tumor echogenicity), the model demonstrates good discrimination (C-index =0.733, AUC >0.84) and calibration. Risk stratification based on the nomogram can effectively identify high-risk patients. This nomogram provides clinicians with a simple and accurate tool for individualized prognostic assessment, helping to optimize treatment decisions, formulate follow-up strategies, and ultimately improve the prognosis of GBC patients. Future larger-scale, multicenter prospective studies are needed to further validate and refine this model.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0027/rc

Data Sharing Statement: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0027/dss

Peer Review File: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0027/prf

Funding: This work was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (No. 2023-I2M-2-002).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tgh.amegroups.com/article/view/10.21037/tgh-2026-0027/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Review Board of Peking Union Medical College Hospital (No. I-23PJ532) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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