A nomogram for predicting non-curative resection in patients with early gastric cancer based on white light imaging

A nomogram for predicting non-curative resection in patients with early gastric cancer based on white light imaging

Abstract Endoscopic submucosal dissection (ESD) is an effective treatment with minimal invasiveness for early gastric cancer (EGC). However, cases undergoing non-curative resection (NCR) may still undergo additional surgical procedures. We aimed to analyze the features of NCR under white light imagi...

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Bibliographic Details
Main Authors: Mengyu Cao, Linlin Ren, Congcong Min, Zeyuan Diao, Hao Chen, Hua Liu, Xiaoyu Li, Jiahui Xu, Zibin Tian, Tao Mao
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Early gastric cancer
Non-curative resection
Endoscopic submucosal dissection
White light imaging
Nomogram
Online Access:https://doi.org/10.1038/s41598-025-16047-9
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Summary:Abstract Endoscopic submucosal dissection (ESD) is an effective treatment with minimal invasiveness for early gastric cancer (EGC). However, cases undergoing non-curative resection (NCR) may still undergo additional surgical procedures. We aimed to analyze the features of NCR under white light imaging (WLI), and develop a prediction model to assess the risk of NCR before ESD. We retrospectively collected and analyzed WLI and clinicopathological features of 568 EGC patients undergoing ESD between March 2016 and March 2024. A nomogram was developed on 455 patients from the training set after subgroup difference analysis. 91 out of 568 (16.0%) cases had NCR. WLI features including remarkable redness, ulceration, fold convergence, marginal elevation, whitish mucosal change, larger lesions, and Helicobacter pylori (Hp) infection were associated with independent risk factors for NCR. The nomogram based on these features showed good predictive value for NCR, with an area under the curve (AUC) of 0.8095 (95% CI: 0.7538–0.8651) in the training set and 0.7567 (95% CI: 0.6427–0.8707) in the validation set. We developed a nomogram incorporating WLI features that exhibits good predictive performance and could potentially assist in selecting optimal treatment strategies for EGC.
ISSN:2045-2322

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