Development and Validation of a Pathological Upgrading Prediction Model for Low-grade Gastric Mucosal Dysplasia.

ABSTRACT

Objective: The objective of this study is to develop a prediction model for the pathological upgrading of low-grade dysplasia (LGD) in gastric mucosa. The study aims to compare the performance of a traditional model based on clinical and endoscopic factors with an enhanced model that incorporates AMACR staining of biopsy tissues. Methods: The study utilized a training dataset of 405 LGD cases to establish and compare the traditional and enhanced prediction models. Factors associated with upgrading were identified, and the traditional model was based on these factors. The enhanced model incorporated AMACR staining. The models' performances were evaluated using the area under the curve (AUC), bootstrap resampling, and decision curve analysis. External validation was performed using 171 LGD cases. Statistical techniques such as logistic regression and resampling methods were employed to assess the models' predictive abilities and robustness. Results: In the training dataset, the traditional model achieved an AUC of 0.824 (95% confidence interval [CI] 0.783-0.865) for predicting pathological upgrading. However, the enhanced model, which incorporated AMACR staining, exhibited a significantly improved performance with an AUC of 0.878 (95% CI 0.843-0.913). This increase in AUC by 0.054 (95% CI 0.015-0.093) demonstrates a statistically significant enhancement provided by the inclusion of AMACR staining in the prediction model for pathological upgrading of LGD lesions in gastric mucosa. Conclusion: The findings of this study highlight the practical implications of the enhanced prediction model incorporating AMACR staining for low-grade gastric mucosal dysplasia (LGD). The significantly improved performance of the enhanced model in predicting pathological upgrading emphasizes its potential to revolutionize the management and treatment strategies for patients with LGD. By providing a more accurate prediction of upgrading, the enhanced model enables early intervention and timely decision-making, leading to improved outcomes and prognosis for patients. The incorporation of AMACR staining in the prediction model holds promise for enhancing diagnostic strategies and reducing the incidence of postoperative pathological upgrading. This research underscores the importance of leveraging advanced techniques to improve the early detection rate of gastric cancer and ultimately benefit patient care.