Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer

Abstract

BACKGROUND

Neoadjuvant therapy prior to surgery plays a critical role in improving the prognosis of patients with unresectable or locally advanced gastric cancer (GC). Postoperative complications, particularly those classified as Clavien-Dindo grade ≥ II, remain a major concern for surgeons. In recent years machine learning (ML) has emerged as a prominent approach for disease diagnosis and prediction. However, studies on both postoperative complications and ML in patients with GC receiving neoadjuvant therapy remain limited.

AIM

To develop an ML model to predict Clavien-Dindo grade ≥ II complications in patients with GC after neoadjuvant therapy and laparoscopic gastrectomy.

METHODS

Clinical data were collected from 455 patients with GC who underwent neoadjuvant therapy followed by laparoscopic gastrectomy at Peking Union Medical College Hospital (2014-2024). Potential predictors were identified through univariate analysis and least absolute shrinkage and selection operator regression. Six ML algorithms including XGBoost, random forest, neural network ensemble (NNE), logistic regression, GLMnet, and decision tree were trained and optimized using nested cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve, decision curve analysis, and calibration curves.

RESULTS

A total of 455 patients were included of whom 69 (15.16%) developed Clavien-Dindo grade ≥ II complications. The predictive model was constructed using seven variables, including smoking status, Nutritional Risk Screening-2002 score, American Society of Anesthesiologists classification, neoadjuvant therapy, surgical approach, operating time, and intraoperative blood loss. Among the six models the NNE model outperformed the others, achieving the highest area under the receiver operating characteristic curve (0.789, 0.739-0.840) and demonstrating superior discrimination, clinical utility, and calibration.

CONCLUSION

The NNE-based prediction model effectively identified patients with GC at high risk of Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy.

Keywords: Gastric cancer; Machine learning; Postoperative complications; Risk prediction; Neoadjuvant therapy

Core Tip: Addressing data scarcity in gastric cancer neoadjuvant therapy, this study used a large patient cohort (n = 455) to pioneer a machine learning model for predicting Clavien-Dindo grade ≥ II complications post-neoadjuvant therapy and laparoscopic gastrectomy. Key predictors included smoking status, Nutritional Risk Screening-2002 score, American Society of Anesthesiologists classification, neoadjuvant therapy, surgical approach, operating time, and intraoperative blood loss. The neural network ensemble model demonstrated superior performance with optimal discrimination, calibration, and clinical utility, potentially offering a tool for perioperative risk stratification and management optimization.