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Development of a nomogram for the prediction of postoperative survival in hepatoid adenocarcinoma of the stomach
Journal article   Peer reviewed

Development of a nomogram for the prediction of postoperative survival in hepatoid adenocarcinoma of the stomach

Kang Wang, Yusong Chen, Xiaoli Li, Xinshuo Li, Yuqian Zhai, Yudan Yang, Haochen Li, Yan Wang and Ming Gao
Carcinogenesis (New York), Vol.46(4), bgaf081
21/11/2025
PMID: 41230881

Abstract

Adenocarcinoma - mortality Adenocarcinoma - pathology Adenocarcinoma - surgery Adult Aged Female Humans Male Middle Aged Neoplasm Staging Nomograms Prognosis Stomach Neoplasms - mortality Stomach Neoplasms - pathology Stomach Neoplasms - surgery Survival Rate
This study developed a prognostic nomogram for hepatoid adenocarcinoma of the stomach (HAS) using clinicopathological data from 61 surgically treated patients (First Affiliated Hospital of Zhengzhou University, 2013-2025) and externally validated it with 20 cases from Henan Cancer Hospital. Multivariate Cox regression identified TNM stage, CA125, Ki67, and primary tumor location as independent predictors of overall survival (OS). These factors were integrated into a nomogram and internally validated via bootstrap resampling. Compared to the TNM staging system, the nomogram demonstrated superior predictive performance, as indicated by lower Akaike (160.947 versus 168.746) and Bayesian Information Criterion values (169.391 versus 170.857). The bootstrap-corrected concordance index (C-index) for the nomogram was 0.800 (95% CI: 0.729-0.880), significantly outperforming the TNM system (0.653, 95% CI: 0.559-0.746; P = .001); the external validation C-index was 0.754 (95% CI: 0.638-0.870). Time-dependent C-index and calibration curves confirmed superior discriminative ability and accuracy, while decision curve analysis indicated clinical utility. Patients stratified as high-risk by the nomogram had significantly worse OS versus low-risk groups in both training (P < .0001) and validation cohorts (P = .02). This tool enables risk stratification to guide personalized HAS management.

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