Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Abstract

Objective: This study aims to evaluate the predictive value of tumor markers combined with gastrin for tumor recurrence after endoscopic submucosal dissection (ESD) in patients with early gastric cancer.

Methods: The clinicopathological data of 169 patients with early gastric cancer treated with ESD between March 2019 and January 2021 were retrospectively analyzed. The patients were divided into a relapse group (n=45) and a non-recurrence group (n=124). Clinical data such as carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), alpha-fetoprotein (AFP), gastrin 17, pepsinogen I and pepsinogen II, as well as tumor size and degree of infiltration were examined to construct a recurrence prediction model using lasso regression.

Results: The comprehensive model showed superior predictive power (AUC=0.958, C-index=0.966) over biomarker-only models (AUC=0.925), indicating a significant improvement in the prediction of recurrence risk. Decision curve analysis confirmed the clinical utility of the model with a maximum net benefit of 73.37%. Key indicators such as CEA, CA19-9, AFP, gastrin 17 and pepsinogens I and II were statistically significant in predicting recurrence with P values < 0.01.

Conclusion: The comprehensive model combining tumor markers with clinical data provides a more accurate and clinically valuable tool for predicting recurrence in early gastric cancer patients after ESD. This approach facilitates personalized risk assessment and may significantly improve prognostic management, emphasizing the importance of a multifaceted strategy in the management of early gastric cancer.

Keywords: Tumor marker; early gastric cancer; endoscopic submucosal dissection; gastrin; prediction; recurrence.

Figure 1

Research flowchart.

Figure 2

Factor analysis of laboratory indicators for recurrence prediction. A. Lasso paths for feature selection. Each point indicates the model deviation from its baseline performance at different values of λ. The black dashed line marks the value of λ for the optimal model selection. B. Variation of variable coefficients with regularisation parameter λ. Each curve represents the coefficient of a variable as λ increases.

Figure 3

Factor analysis of clinical data combined with laboratory indicators for recurrence prediction. A. Lasso path for feature selection. Each point indicates the model deviation from its baseline performance at different values of λ. The black dashed line marks the value of λ for the optimal model selection. B. Variation of variable coefficients with regularisation parameter λ. Each curve represents the coefficient of a variable as λ increases.

Figure 4

Clinical efficacy assessment of the laboratory biomarker model and the combined model. A. Comparison of per-patient scores based on the biomarker model. B. Comparison of per-patient scores based on the combined model. C. Comparison of ROC curves between biomarker model and combined model.

Figure 5

Nomogram model construction and internal validation. A. Nomogram showing the contribution of the different variables to the final risk prediction, where the score for each variable can be read directly from the graph and accumulated to obtain a total score, which is then converted to a predicted probability. B. Calibration curves, where the agreement between the probabilities predicted by the model and the actual observed probabilities is represented by the blue line, which is closer to the ideal case (red line). C. Net benefit of using the model compared to no strategy under different threshold choices.