Development and validation of a machine learning model to predict postoperative complications following radical gastrectomy for gastric cancer

Abstract

Objective:

Postoperative complications significantly adversely affect recovery and prognosis following radical gastrectomy for gastric cancer. We developed and validated machine learning (ML) models to predict these complications and constructed a clinically applicable dynamic nomogram.

Methods:

Using a prospectively maintained database, we conducted a retrospective analysis of 1,486 patients from Fujian Cancer Hospital (training cohort) and 498 from the First Hospital of Putian City (validation cohort). Feature selection integrated Lasso regression, the Boruta algorithm, and Recursive Feature Elimination (RFE). Six ML models were developed and evaluated: TreeBagger (TB), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gaussian Naïve Bayes (GNB), and Artificial Neural Network (ANN). The significant predictors identified were incorporated into a logistic regression model to determine independent risk factors, which then formed the basis of a dynamic nomogram deployed as an interactive web application for clinical use.

Results:

RF demonstrated numerically superior performance among the evaluated models in both cohorts. Independent risk factors included age, BMI, diabetes mellitus, ASA grade, operative time, and surgical approach. The dynamic nomogram achieved AUCs of 0.805 (training) and 0.856 (validation), with calibration curves and decision curve analysis confirming its reliability. DeLong’s test revealed no significant difference in AUC between the RF model and nomogram in either cohort (training: Z = -0.385, p = 0.701; validation: Z = -1.756, p = 0.058).

Conclusion:

While the RF model provided optimal predictive accuracy among ML algorithms, the interpretable nomogram offers comparable discrimination and clinical accessibility. Both tools facilitate the early identification of high-risk patients, enabling personalized interventions to optimize postoperative recovery.

Introduction

Gastric cancer ranks as the fifth most common cancer globally and is the fourth leading cause of cancer-related death (1). Surgery remains the primary curative treatment; however, it poses significant challenges due to the complex anatomy of the stomach, its rich vascular supply, and the technical difficulty of lymph node dissection. Furthermore, reconstruction of the digestive tract alters normal anatomy, increasing both the likelihood and complexity of postoperative complications (2, 3).

Although perioperative mortality rates for gastric cancer have declined in recent decades, the incidence of postoperative complications remains substantial, ranging from 11.0% to 40.1% (4–8). These complications can significantly delay recovery, prolong hospitalization, increase healthcare costs, diminish quality of life, and adversely impact long-term survival (9, 10). Consequently, accurate preoperative risk assessment and early intervention represent a critical strategy for mitigating postoperative complications.

Machine learning (ML), a subset of artificial intelligence, leverages algorithms to uncover complex relationships within large datasets. Within healthcare, ML is increasingly employed to predict disease outcomes, personalize treatments, and enhance clinical decision-making, ultimately aiming to improve patient outcomes and optimize healthcare delivery (11, 12). However, existing predictive tools face significant limitations. Traditional scoring systems often oversimplify non-linear relationships, while many ML-based models exhibit methodological shortcomings, including reliance on small single-center cohorts, lack of external validation, and suboptimal handling of high-dimensional data during feature selection (13–17). Recent efforts to enhance clinical utility focus on ensemble methods (e.g., Random Forest, XGBoost) and interpretable nomograms. Nevertheless, comprehensive comparisons of multiple algorithms integrated with robust hybrid feature selection strategies remain lacking specifically for predicting complications following gastric cancer surgery. To address these critical gaps, we developed and validated six distinct ML models using a large multicenter cohort. Our approach integrates hybrid feature selection, rigorous external validation, and the development of a clinically accessible dynamic nomogram for practical implementation.

Methods

Patients

We performed a retrospective analysis using data from a prospectively maintained database. The analysis included 1,486 gastric cancer patients who underwent radical gastrectomy at Fujian Cancer Hospital between January 2020 and March 2024, constituting the training cohort. Inclusion criteria were: (1) age ≥ 18 years; (2) histologically confirmed gastric adenocarcinoma; and (3) radical gastrectomy. Exclusion criteria comprised: (1) incomplete clinical or pathological data; (2) emergency surgery; (3) intraoperative peritoneal dissemination; and (4) receipt of neoadjuvant chemotherapy with immunotherapy (NCI; excluded due to limited case numbers precluding meaningful subgroup analysis). An independent cohort of 498 patients from the First Hospital of Putian City, meeting identical inclusion criteria, served as the validation cohort. Figure 1 illustrates the patient selection flowchart.

Figure 1

Definitions and outcome measures

The severity of postoperative complications was assessed using the Clavien-Dindo classification system (18, 19), the standard grading system for surgical complications.

Age ≥ 65 was used as the threshold to define elderly patients, in line with previous clinical conventions (20, 21). Anemia was defined as hemoglobin <110 g/L in females and <120 g/L in males (22). Preoperative hypoalbuminemia was defined as serum albumin <35 g/L, a validated threshold associated with increased complication risk (23–25). and this threshold is commonly regarded as indicative of hypoalbuminemia. Body mass index (BMI) was categorized per WHO criteria: BMI < 18.5 kg/m² is underweight, BMI between 18.5 and 24.9 kg/m² is normal weight, and BMI ≥ 25 kg/m² is overweight (26–28). Tumor location is categorized into three regions—upper, middle, and lower third—based on the Japanese Gastric Cancer Treatment Guidelines (29).

Implementation of machine learning models

All six models were implemented in Python 3.10 using scikit-learn (v1.3.0) and XGBoost (v1.7.5) with the following specifications:

TreeBagger (TB): 500 decision trees, bootstrap sampling, Gini impurity for splitting, and default scikit-learn parameters for other settings.

Random Forest (RF): 500 trees, Gini impurity criterion, max depth=10, min samples split=5.

Support Vector Machine (SVM): Radial basis function kernel (C = 1.0, gamma=‘scale’).

XGBoost: 300 estimators, learning rate=0.05, max depth=4, subsample=0.8.

Gaussian Naïve Bayes (GNB): Default scikit-learn parameters (priors adjusted to class distribution).

Artificial Neural Network (ANN): Single hidden layer (32 neurons), ReLU activation, Adam optimizer (learning rate=0.001).

All models underwent 5-fold stratified cross-validation on the training cohort.

Statistical analysis

Categorical variables are presented as frequencies (percentages), with between-group comparisons using chi-square tests. Continuous variables are expressed as mean ± standard deviation (SD) or median [interquartile range, IQR] based on distribution normality, compared using independent t-tests or Mann-Whitney U tests as appropriate. Feature selection identified predictive variables through intersection analysis of three methods: Lasso regression, Boruta algorithm, and Recursive Feature Elimination (RFE). Model performance evaluation included: Receiver operating characteristic (ROC) curves with area under curve (AUC); Calibration curves; Decision curve analysis (DCA). Quantitative metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Statistical significance was defined as P < 0.05.

Results

Patient characteristics and feature selection

In the training cohort, the overall incidence of postoperative complications was 20.5% (304/1,486), with 9.3% (138/1,486) classified as Clavien–Dindo grade ≥ IIIa; in the validation cohort, these rates were 23.9% (119/498) and 11.6% (58/498), respectively (Supplementary Table 1, 2). The specific types and frequencies of postoperative complications are detailed in Table 1. Baseline characteristics were comparable between cohorts except for sex, drinking history, dyslipidemia, and CA19-9 levels (Table 2).

Through intersection analysis of three feature selection methods (Lasso regression, Boruta algorithm, and RFE), we identified eight factors associated with postoperative complications in the training cohort: age, BMI, diabetes mellitus, ASA grade, neoadjuvant chemotherapy, multivisceral resection, operative time, and surgical approach (Figure 2).

Figure 2

Establishment and comparison of machine learning models

Six distinct ML models (TB, RF, SVM, XGBoost, GNB, ANN) were developed using the training cohort to predict postoperative complications. Model performance was rigorously assessed via five-fold cross-validation. Among the evaluated models, RF demonstrated superior predictive performance in the training cohort across key metrics including AUC, sensitivity, and NPV (Table 3; Supplementary Figure 1; Figures 3A–C). This performance advantage was maintained in the independent validation cohort (Supplementary Table 3; Supplementary Figure 2; Figures 4A–C), establishing RF as the optimal model among the tested algorithms.

Figure 3

Figure 4

Nomogram construction and validation

The eight factors identified via intersection analysis of three feature selection methods (Lasso regression, Boruta algorithm, and RFE) were incorporated into a multivariable logistic regression model to screen for independent risk factors for postoperative complications, which revealed that age, BMI, diabetes mellitus, ASA grade, operative time, and surgical approach were independently associated with postoperative complications (Figure 5). A nomogram and its dynamic version were developed to facilitate clinical application, with the dynamic tool accessible via a web application (https://lzmdoc123456789.shinyapps.io/pcingc) (Figures 6). Internal validation of the nomogram demonstrated excellent calibration, as evidenced by the calibration curve showing close alignment between predicted and observed outcomes.

Figure 5

Figure 6

Additionally, DCA confirmed its clinical utility, with favorable net benefits across threshold probabilities ranging from 0.06 to 0.95 (Figure 7).

Figure 7

External validation was performed using the validation cohort. ROC curve analysis yielded an AUC of 0.856 (95% CI: 0.817-0.895), indicating excellent discriminatory ability. The calibration curve further confirmed the model’s accuracy, with predicted outcomes closely aligning with observed results. Moreover, DCA demonstrated favorable net benefits across a broad range of threshold probabilities (0.04 to 0.97), supporting the nomogram’s clinical value (Figure 8).

Figure 8

Comparison of predictive performance between RF model and nomogram

To compare the optimal ML model (RF) with the nomogram, DeLong’s test was used to analyze their ROC curves. The RF model showed marginally higher AUCs than the nomogram in both cohorts (training: 0.827 vs. 0.805; validation: 0.871 vs. 0.856), but these differences were not statistically significant (training: Z = -0.385, p = 0.701; validation: Z = -1.756, p = 0.058).

Discussion

Postoperative complications following gastric cancer surgery pose substantial threats to patient recovery and long-term survival. These adverse events significantly elevate mortality risk, prolong hospitalization duration, escalate healthcare expenditures, and impair quality of life - particularly concerning for cancer patients requiring adjuvant therapy. Such complications may also compromise functional recovery and nutritional status, creating barriers to timely oncological treatment (30, 31). In this study, the incidence of postoperative complications was 20.5% in the training cohort and 23.9% in the validation cohort, which aligns with findings from previous studies on gastric cancer surgery. Kanda et al. (32) reported a postoperative complication rate of 22.5%. Similarly, a study analyzing 663 gastric cancer patients found that 20.8% experienced postoperative complications (7). Data from the National Clinical Database, which includes over 33,917 Japanese patients, also supports this finding, showing a complication rate of 18.3% (33). However, other studies report higher complication rates. A European observational, retrospective trial indicated that 33% of patients experienced at least one postoperative complication (34), and a randomized controlled trial (JCOG1001) found a complication rate of 34.3% (4). The variation in postoperative complication rates may be attributed to differences in the definition of complications, patient population characteristics (such as age and comorbidities), surgical techniques, the experience of the surgical team, and postoperative care protocols.

ML has proven valuable for improving predictive accuracy in clinical settings (35–37). In the present study, among the six evaluated machine learning algorithms, the RF model showed numerically superior performance, achieving the highest AUC in both cohorts. However, DeLong’s test revealed no statistically significant difference in discriminative ability between the optimal RF model and the logistic regression-based nomogram. This comparable predictive performance, combined with the nomogram’s visual interpretability and user-friendly design, underscores its value as a pragmatic clinical tool. The dynamic nomogram facilitates rapid point-of-care risk stratification without requiring computational expertise, enabling clinicians to intuitively assess risk factors and implement personalized preventive strategies for high-risk patients. Our findings support a dual-model approach: the RF model serves as a high-performance reference standard in settings with adequate computational resources, while the dynamic nomogram offers an immediately deployable alternative with preserved discriminative power. This strategy balances algorithmic performance with real-world applicability across diverse healthcare contexts.

In this study, elderly patients were found to be more prone to postoperative complications. As individuals age, various physiological changes occur, including a decline in organ function, slower wound healing, and a weakened immune response, all of which complicate recovery. Moreover, elderly patients often have multiple comorbidities, which further delay the recovery process. Additionally, aging is frequently associated with sarcopenia, frailty, and a reduction in muscle mass and strength, all of which diminish the body’s ability to tolerate surgical and anesthetic stressors (38–40).

Consistent with previous studies (41–43), our study confirmed overweight as an independent risk factor for postoperative complications. Excess body weight contributes to increased intra-abdominal pressure, leading to impaired tissue oxygenation and impaired wound healing (44). Furthermore, individuals with higher body weight are more likely to develop comorbid conditions (e.g., diabetes mellitus), thereby exacerbating the risk of complications. Additionally, adiposity is associated with a chronic inflammatory state, which may compromise immune function and predispose patients to infections and prolonged inflammation (45). Lastly, overweight patients often undergo longer operative times and more complex surgical procedures—both well-established factors that increase the likelihood of postoperative complications (46).

Our study corroborates previous findings that diabetes mellitus significantly increases the risk of postoperative adverse events (13, 47). Hyperglycemia in diabetic patients impairs immune function by disrupting neutrophil chemotaxis and phagocytosis (48). Elevated blood glucose levels also impair collagen synthesis and extracellular matrix remodeling, both crucial to tissue repair, thereby contributing to poor wound healing (49). Moreover, diabetes is often associated with microvascular changes (e.g., impaired circulation), which can further compromise tissue oxygenation and delay healing (50).

The ASA physical status classification system is a widely utilized tool for evaluating preoperative patient health status (51). In our study, patients classified as ASA III had a higher likelihood of developing adverse postoperative outcomes relative to those with lower ASA grades. Severe comorbidities commonly observed in ASA III patients—such as cardiovascular disease, diabetes, and respiratory disorders—reduce their physiological reserves, thereby impairing tolerance to surgical stress. Furthermore, such patients with severe systemic diseases are at increased risk of infection due to impaired immune function, as these conditions often induce a chronic inflammatory state and impair immune responses (18, 52).

Our study confirmed operative time as an independent risk factor for postoperative complications. Prolonged operative time is frequently linked to more complex surgical procedures, increased tissue trauma, and extended anesthesia exposure—all of which increase the incidence of complications, including infections, bleeding, and delayed recovery (53, 54).

We identified total gastrectomy as a significant risk factor for postoperative complications. Total gastrectomy entails more extensive lymph node dissection and removal of a larger volume of tissue, rendering it a more invasive procedure that often results in greater surgical trauma and a higher risk of postoperative complications. Esophagojejunal anastomosis, a key step in gastrointestinal reconstruction after total gastrectomy, involves deep anastomosis in a confined surgical space, making it both complex and challenging. Specifically, in patients with excessive visceral fat, tension or stretching of the anastomosis may induce serosal tears and bleeding (19, 55).

This study has several limitations. First, despite using a prospectively maintained database, it is a retrospective analysis, and certain parameters (e.g., preoperative nutritional status) that may influence postoperative complications (56–58), were not available. Second, the role of NCI in gastric cancer is under active investigation and has shown promising results (59–61); however, due to the limited number of cases at our center, NCI was excluded from this study. Third, external validation was performed at only one hospital, which could restrict the generalizability of our findings.

Conclusions

This study developed and validated ML models to predict postoperative complications in gastric cancer patients undergoing radical gastrectomy. Among the six evaluated models, RF model demonstrated numerically superior performance, while a logistic regression-based nomogram-incorporating key predictors including age, BMI, diabetes mellitus, ASA grade, operative time, and surgical approach-exhibited comparable discriminative ability and clinical practicability. Both tools facilitate the identification of high-risk patients and can guide clinical decision-making to optimize postoperative outcomes.

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