Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis
This is the first study in which an AI-model simultaneously considers clinical, endoscopic, and histological features from a dataset of CAG patients, showing the potential to identify variables associated with GNL development.
Authors
- Giampaolo Liuzzi (ORCID: https://orcid.org/0000-0002-4063-8370)
- Bruno Annibale (ORCID: https://orcid.org/0000-0001-9120-5957)
- Gianluca Esposito (ORCID: https://orcid.org/0000-0002-2242-5048)
- Irene Ligato (ORCID: https://orcid.org/0009-0007-1915-8472)
- Elisabetta Cesaroni (ORCID: https://orcid.org/0000-0002-7177-4500)
- Emanuele Dilaghi (ORCID: https://orcid.org/0000-0002-9070-7733)
- Stefano Lucidi (ORCID: https://orcid.org/0000-0003-4356-7958)
- Marco Sciandrone (ORCID: https://orcid.org/0000-0002-5234-3174)
- Michela Silvestri (ORCID: https://orcid.org/0000-0003-4804-2421)
Institutions
- Azienda Ospedaliera Sant'Andrea (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Gastric Cancer
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1007/s10120-025-01679-7
- Citations
- 1
- Primary Topic
- Helicobacter pylori-related gastroenterology studies
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- European Commission
- Sapienza Università di Roma