Gastric preneoplastic conditions, including chronic atrophic gastritis and gastric intestinal metaplasia, represent the histopathological substrate from which gastric dysplasia and adenocarcinoma may develop. Among these, corpus atrophic gastritis (CAG) is a condition of increasing clinical relevance due to its pathophysiological complexity and neoplastic potential. This doctoral thesis aimed to provide an integrated characterization of CAG through four main research areas. First, an endoscopic evaluation of gastric preneoplastic conditions was conducted using electronic chromoendoscopy, identifying specific mucosal patterns associated with active or past Helicobacter pylori (H. pylori) infection and validating the EGGIM system for the staging of gastric intestinal metaplasia. Artificial intelligence models based on Support Vector Machine and Convolutional Neural Networks were developed to stratify neoplastic risk and automatically recognize gastric intestinal metaplasia in endoscopic images. Second, longitudinal histological changes of the gastric mucosa were analyzed, assessing differences according to H. pylori infection status and the presence of autoimmune features. Third, the therapeutic management of chronic and atrophic gastritis was investigated, with particular attention to H. pylori eradication regimens in patients with CAG. Finally, the incidence and predictors of gastric neoplastic lesions were evaluated, as well as the risk of gastric cancer in first-degree relatives of patients affected by gastric adenocarcinoma. Overall, this doctoral project contributes to defining an integrated framework for the management of gastric preneoplastic conditions, in which endoscopic innovation, histopathological evaluation, therapeutic optimization, and computational modeling converge to improve diagnostic accuracy, clinical management, and surveillance of the risk of gastric adenocarcinoma development.

Gastric preneoplastic and neoplastic conditions: endoscopic evaluation, histopathological characterization, and assessment of neoplastic risk

DILAGHI, EMANUELE
2026

Abstract

Gastric preneoplastic conditions, including chronic atrophic gastritis and gastric intestinal metaplasia, represent the histopathological substrate from which gastric dysplasia and adenocarcinoma may develop. Among these, corpus atrophic gastritis (CAG) is a condition of increasing clinical relevance due to its pathophysiological complexity and neoplastic potential. This doctoral thesis aimed to provide an integrated characterization of CAG through four main research areas. First, an endoscopic evaluation of gastric preneoplastic conditions was conducted using electronic chromoendoscopy, identifying specific mucosal patterns associated with active or past Helicobacter pylori (H. pylori) infection and validating the EGGIM system for the staging of gastric intestinal metaplasia. Artificial intelligence models based on Support Vector Machine and Convolutional Neural Networks were developed to stratify neoplastic risk and automatically recognize gastric intestinal metaplasia in endoscopic images. Second, longitudinal histological changes of the gastric mucosa were analyzed, assessing differences according to H. pylori infection status and the presence of autoimmune features. Third, the therapeutic management of chronic and atrophic gastritis was investigated, with particular attention to H. pylori eradication regimens in patients with CAG. Finally, the incidence and predictors of gastric neoplastic lesions were evaluated, as well as the risk of gastric cancer in first-degree relatives of patients affected by gastric adenocarcinoma. Overall, this doctoral project contributes to defining an integrated framework for the management of gastric preneoplastic conditions, in which endoscopic innovation, histopathological evaluation, therapeutic optimization, and computational modeling converge to improve diagnostic accuracy, clinical management, and surveillance of the risk of gastric adenocarcinoma development.
26-gen-2026
Inglese
ANNIBALE, Bruno
NIGRI, Giuseppe
Università degli Studi di Roma "La Sapienza"
71
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/357495
Il codice NBN di questa tesi è URN:NBN:IT:UNIROMA1-357495