Evidence map›Paper›PMID 40124264›Full record

ArticleWorld journal of gastroenterology2025

Development and validation of a predictive model for the pathological upgrading of gastric low-grade intraepithelial neoplasia.

Kun-Ming Lyu, Qian-Qian Chen, Yi-Fan Xu, Yao-Qian Yuan, Jia-Feng Wang, Jun Wan, En-Qiang Ling-Hu

Abstract readValidation Study
In one paragraph

Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Kun-Ming LyuDepartment of Gastroenterology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Qian-Qian ChenDepartment of Gastroenterology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Yi-Fan XuDepartment of Gastroenterology, General Hospital of Central Theater Command, Wuhan 430000, Hubei Province, China.
Yao-Qian YuanMedical College, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Jia-Feng WangDepartment of Gastroenterology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Jun WanDepartment of Gastroenterology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
En-Qiang Ling-HuDepartment of Gastroenterology, The First Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100853, China. linghuenqiang@vip.sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia (LGIN) presents challenges in developing diagnostic and treatment protocols.

aimTo develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.

methodsWe retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People's Liberation Army General Hospital between January 2008 and December 2023. A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.

resultsA total of 171 patients were included in this study: 93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN. The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800, respectively, while the least absolute shrinkage and selection operator (LASSO) regression model showed sensitivity and specificity values of 0.842 and 0.840, respectively. The area under the curve (AUC) for the logistic model was 0.896, slightly lower than the AUC of 0.904 for the LASSO model. Internal validation with 30% of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model. The LASSO model provided greater utility in clinical decision-making.

conclusionA risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.

Indexed as

Carcinoma in SituStomach NeoplasmsAdultAgedBiopsyChinaDisease ProgressionEndoscopic Mucosal ResectionFemaleGastric MucosaGastroscopyHumansLogistic ModelsMaleMiddle AgedNeoplasm GradingEarly gastric cancerEndoscopic resectionGastric low-grade intraepithelial neoplasiaPathological upgradePrediction model

Identifiers

PMID40124264
PMCPMC11924016

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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.