Cao MY, Chen YF, Liao YX, Yang Q, Lin SY, Li Y, Weng J, Huang XX, Gao XY, Wang GB, Shan HB, Li JJ. Endoscopic ultrasonography lesion thickness predicts deep invasion in gastric cancer: Development and temporal validation of a preoperative model. World J Gastrointest Oncol 2026; 18(7): 118867 [DOI: 10.4251/wjgo.v18.i7.118867]
Corresponding Author of This Article
Hong-Bo Shan, MD, PhD, Chief Physician, Senior Research Fellow, Department of Endoscopy, Sun Yat-sen University Cancer Center, No. 651 Dongfengdong Street, Yuexiu District, Guangzhou 510060, Guangdong Province, China. shanhb@sysucc.org.cn
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Medicine, General & Internal
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research-article
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Cao MY, Chen YF, Liao YX, Yang Q, Lin SY, Li Y, Weng J, Huang XX, Gao XY, Wang GB, Shan HB, Li JJ. Endoscopic ultrasonography lesion thickness predicts deep invasion in gastric cancer: Development and temporal validation of a preoperative model. World J Gastrointest Oncol 2026; 18(7): 118867 [DOI: 10.4251/wjgo.v18.i7.118867]
Meng-Yu Cao, Yu-Fan Chen, Yun-Xiu Liao, Qing Yang, Shi-Yong Lin, Yin Li, Jun Weng, Xin-Xin Huang, Xiao-Yan Gao, Guo-Bao Wang, Hong-Bo Shan, Jian-Jun Li, Department of Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong Province, China
Co-first authors: Meng-Yu Cao and Yu-Fan Chen.
Co-corresponding authors: Hong-Bo Shan and Jian-Jun Li.
Author contributions: Cao MY and Chen YF contributed to the conception and design of the study, performed the statistical analysis and interpreted the data as co-first authors; Cao MY, Chen YF, Liao YX, and Yang Q participated in data acquisition and data curation; Cao MY drafted the manuscript; Liao YX and Yang Q accessed and verified the underlying study data; Shan HB supervised the study and provided methodological oversight; Shan HB and Li JJ contributed equally as co-corresponding authors. All authors critically reviewed the manuscript for important intellectual content, approved the final version, and accepted responsibility for the decision to submit the manuscript for publication.
Supported by Natural Science Foundation of Tibet Autonomous Region for Group Medical Assistance Project, No. XZ2019ZR-ZY60(Z); and Guangzhou Clinical Specialty Technology Construction Project, No. 2026P-TS016.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Sun Yat-sen University Cancer Center, No. SL-B2025-207-01.
Informed consent statement: The requirement for informed consent was waived by the Institutional Review Board because of the retrospective study design and the use of anonymised clinical data.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The de-identified data and statistical code that support the findings of this study are available from the corresponding author upon reasonable request, subject to institutional and ethical requirements.
Corresponding author: Hong-Bo Shan, MD, PhD, Chief Physician, Senior Research Fellow, Department of Endoscopy, Sun Yat-sen University Cancer Center, No. 651 Dongfengdong Street, Yuexiu District, Guangzhou 510060, Guangdong Province, China. shanhb@sysucc.org.cn
Received: January 14, 2026 Revised: February 26, 2026 Accepted: March 31, 2026 Published online: July 15, 2026 Processing time: 173 Days and 21.8 Hours
Abstract
BACKGROUND
Accurate preoperative identification of advanced (≥ pT3) gastric cancer is essential for selecting candidates for neoadjuvant therapy and planning operative strategy. Endoscopic ultrasonography (EUS) provides routine categorical staging (uT) and quantitative lesion thickness, yet thickness is often simplified using categorical cut-offs, potentially obscuring non-linear risk patterns. We hypothesized that modelling EUS-measured thickness as a continuous predictor could improve individualised estimation of ≥ pT3 risk and provide a probabilistic complement to uT staging.
AIM
To develop and validate a preoperative model using EUS lesion thickness to predict pathological deep invasion in gastric cancer.
METHODS
We retrospectively studied 518 gastrectomy patients (2017-2020) without neoadjuvant therapy. The outcome was pathological deep invasion (≥ pT3). A multivariable logistic model was developed using lesion thickness as restricted cubic splines plus age, sex, tumour location, Lauren type, carcinoembryonic antigen, and carbohydrate antigen 19-9, with bootstrap internal validation and uniform shrinkage. The model was benchmarked against a uT-only model and a uT ≥ 3 rule, and then temporally tested in a later same-institution cohort (2021-2025; n = 246).
RESULTS
Deep invasion occurred in 354/518 patients. Discrimination, measured by the area under the receiver operating characteristic curve (AUC), was 0.851 [95% confidence interval (CI): 0.817-0.885]. The model outperformed a uT-only model (AUC 0.810, 95%CI: 0.768-0.851) and a uT ≥ 3 rule (AUC 0.761, 95%CI: 0.721-0.802); the uT ≥ 3 rule had poorer accuracy (Brier score 0.214). In temporal validation, AUC was 0.845 (95%CI: 0.798-0.893) with calibration drift (intercept -0.466; slope 0.623).
CONCLUSION
An EUS thickness-based model estimates deep invasion risk and may complement routine uT staging. Temporal testing showed preserved discrimination but calibration drift, supporting future recalibration and external validation.
Core Tip: Preoperative identification of deep gastric wall invasion remains challenging. Endoscopic ultrasonography provides both a categorical T stage and a measurable lesion thickness. We developed and internally validated a logistic model that treats lesion thickness as a continuous predictor, together with routinely available clinical variables, to estimate individualised risk of pathological ≥ pT3 disease. The model showed good discrimination and decision-curve utility, and outperformed uT-based benchmarks. Temporal testing in a later same-institution cohort preserved discrimination but showed calibration drift, supporting future recalibration, external validation, and context-specific threshold selection.
