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Comparative Analysis of Differences Between Preoperative Endoscopic Biopsy and Postoperative Pathological Examination for Diagnosis of Gastric Intraepithelial Neoplasia

Overview
Journal J Int Med Res
Publisher Sage Publications
Specialty General Medicine
Date 2021 Mar 19
PMID 33736516
Citations 2
Authors
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Abstract

Objective: This study was performed to compare the differences between preoperative endoscopic biopsy (PEB) and postoperative pathological examination (PPE) for diagnosis of gastric intraepithelial neoplasia (GIN).

Methods: From September 2016 to July 2019, 188 consecutive patients with GIN at Yuyao People's Hospital were retrospectively analyzed. The 188 patients had 218 GIN lesions. All patients underwent PEB and either endoscopic submucosal dissection or surgical treatment. PPE was performed on pathological tissues that had been surgically removed.

Results: Among 138 cases of low-grade dysplasia (LGD) diagnosed by PEB, 46 were upgraded to high-grade dysplasia (HGD), 20 were upgraded to early gastric cancer (EGC), and 2 were downgraded to inflammation after PPE. Among 42 cases of HGD, 23 were upgraded to EGC, 2 were downgraded to LGD, and 2 were downgraded to inflammation after PPE. Among 38 cases of EGC, 1 was downgraded to HGD and 2 were downgraded to LGD after PPE. The original diagnosis was maintained after the operation in 120 cases of GIN.

Conclusion: Biopsy did not fully reflect the lesions of GIN. Biopsy review should be actively performed, and the lesions should be clarified by endoscopic submucosal dissection or surgery.

Citing Articles

Establishment and Validation of a Pathologic Upgrade Prediction Nomogram Model for Gastric Low-Grade Intraepithelial Neoplasia Patients After the Eradication of .

Ruan Y, Lu G, Zhu Y, Ma X, Shi Y, Zhang X Cancer Control. 2022; 29:10732748221143390.

PMID: 36475870 PMC: 9742585. DOI: 10.1177/10732748221143390.


Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors.

Jiang S, Gao H, He J, Shi J, Tong Y, Wu J Front Artif Intell. 2022; 5:956385.

PMID: 36052291 PMC: 9424643. DOI: 10.3389/frai.2022.956385.

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