Evidence map›Paper›PMID 41123822›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2026

Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis.

Emanuele Dilaghi, Edoardo Cesaroni, Irene Ligato, Matteo Silvestri, Giampaolo Liuzzi, Bruno Annibale, Stefano Lucidi, Gianluca Esposito, Marco Sciandrone

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Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Emanuele Dilaghi *Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189, Rome, Italy.
Edoardo Cesaroni *Department of Computer, Control, and Management Engineering "Antonio Ruberti" - Sapienza University of Rome, Rome, Italy.
Irene LigatoDepartment of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189, Rome, Italy.
Matteo SilvestriDepartment of Computer Science, Sapienza University of Rome, Rome, Italy.
Giampaolo LiuzziDepartment of Computer, Control, and Management Engineering "Antonio Ruberti" - Sapienza University of Rome, Rome, Italy.
Bruno AnnibaleDepartment of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189, Rome, Italy.
Stefano LucidiDepartment of Computer, Control, and Management Engineering "Antonio Ruberti" - Sapienza University of Rome, Rome, Italy.
Gianluca EspositoDepartment of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa 1035, 00189, Rome, Italy. gianluca.esposito@uniroma1.it.ORCID 0000-0002-2242-5048
Marco SciandroneDepartment of Computer, Control, and Management Engineering "Antonio Ruberti" - Sapienza University of Rome, Rome, Italy.

Funding

Next Generation EU, Mission 4 Component 1 CUP B53C23001740006Sapienza Università di Roma RG123188B449C26A
6 · The paper itself

Abstract

backgroundCorpus atrophic gastritis (CAG) requires endoscopic-histological surveillance due to the risk of developing gastric neoplastic lesions (GNL). This study aimed to identify variables associated with GNL development at long-term follow-up using a Fisher score-based feature-ranking-approach coupled with a One-Class Support-Vector-Machine (SVM) model.

methodsA dataset containing 30 clinical, endoscopic, and histological variables from consecutive CAG patients (2001-2023) adhering to a surveillance-program was considered. GNL presence at the longest available follow-up was recorded. Gastric biopsies and histological evaluations followed the updated-Sydney-system. A Fisher score-based feature ranking method and a One-Class SVM were employed to select key variables linked to GNL development, and then validated with synthetically generated data.

resultsOverall, 355 CAG patients were initially considered. Of these, 36 were excluded due to the presence of GNL at baseline gastroscopy, and 216 for missing data. Thus, a total of 103 patients were considered and grouped into: CAG patients with [22 patients (F 68.1%), median-age 68(35-83) years] and without GNL at follow-up [81 patients (F 72.8%) median-age 59(26-84) years]. After a median follow-up of 60(12-192) months, 13 epithelial GNL (gastric adenocarcinoma or high/low-grade dysplasia) and nine type-1 gastric-neuroendocrine-tumors (T1gNET) were recorded. Parietal-cell-antibodies and pepsinogen-I < 30 μg/l were associated with epithelial GNL and T1gNET. Antral inflammation and age > 60 were linked to epithelial GNL, while anti-thyroperoxidase-antibodies, smoking, and dyspeptic-symptoms were linked to T1gNET. Low-dose aspirin and H. pylori eradication therapy showed inverse associations with epithelial GNL and T1gNET, respectively.

conclusionsThis 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.

Indexed as

Artificial IntelligenceGastritis, AtrophicPrecancerous ConditionsStomach NeoplasmsAdultAgedAged, 80 and overFemaleFollow-Up StudiesGastroscopyHumansMaleMiddle AgedRisk AssessmentRisk FactorsArtificial intelligenceCorpus atrophic gastritisGastric cancerGastric neoplastic lesionOne-class support vector machine

Identifiers

PMID41123822
PMCPMC12830466

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.