ORIGINAL RESEARCH article
Front. Med.
Sec. Gastroenterology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1577569
This article is part of the Research TopicClinical prediction models in cancer through bioinformaticsView all 8 articles
Prediction of Tumor Deposits in Stage I-III Gastric Cancer: A Clinically Applicable Nomogram Integrating Clinicopathology Outcomes
Provisionally accepted
- 1Affiliated Hospital of Hebei University, Baoding, Hebei Province, China
- 2Sun Yat-sen University, Guangzhou, China
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Objective: This study seeks to identify clinicopathological risk factors associated with tumor deposits (TD) development in stage I-III gastric cancer patients and to construct a visualized predictive model for clinical application.A retrospective cohort of 1,284 gastric cancer patients treated at the Affiliated Hospital of Hebei University (September 2010-September 2022) was analyzed. Patients were stratified into training (n=963) and validation (n=321) cohorts via simple randomization at a 3:1 ratio. Lasso regression analysis was employed to screen variables, followed by multivariate logistic regression to establish an individualized nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).Results: TD-positive patients (n=224) exhibited significantly reduced overall survival and disease-free survival compared to TD-negative counterparts (n=1,060, p<0.05). Multivariate logistic regression analysis confirmed tumor size (OR=1.26; 95% CI 1.01-2.21), elevated CEA (OR=2.04; 95% CI 1.02-3.16), elevated CA199 (OR=1.007, 95% CI:1.003-1.011), and pN stage (OR=3.22; 95% CI 2.12-4.34) as independent predictors of TD occurrence (all p<0.05). The nomogram demonstrated robust discriminative capacity, with AUC values of 0.803 (95% CI 0.751-0.894) and 0.864 (95% CI 0.725-0.917) in the training and validation cohorts, respectively.Calibration plots revealed excellent agreement between predicted and observed probabilities. DCA further validated the model's clinical utility, showing superior net benefits across threshold probabilities of 1-99%.This TD-specific nomogram, incorporating tumor size, serum biomarkers (CEA/CA199), and pathological staging (pN), provides a clinically applicable tool for preoperative risk stratification and personalized therapeutic decision-making in stage I-III gastric cancer.
Keywords: Gastric tumor, tumor deposits, Risk factors, predictive model, nomogram
Received: 16 Feb 2025; Accepted: 09 May 2025.
Copyright: © 2025 Wang, Yu, Huo, Su, Xiao and An. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weiguang Yu, Sun Yat-sen University, Guangzhou, China
Lin An, Affiliated Hospital of Hebei University, Baoding, Hebei Province, China
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