Risk factors and nomogram construction for predicting long-term survival in hepatoid adenocarcinoma of the stomach
10.3760/cma.j.cn441530-20240910-00312
- VernacularTitle:基于临床多变量构建胃肝样腺癌预后的诺曼图预测模型
- Author:
Yuyuan LU
1
;
Hao CUI
;
Bo CAO
;
Qixuan XU
;
Jingwang GAO
;
Ruiyang ZHAO
;
Huiguang REN
;
Zhen YUAN
;
Jiajun DU
;
Jiahong SUN
;
Jianxin CUI
;
Bo WEI
Author Information
1. 解放军总医院第一医学中心普通外科学部，北京 100853
- Publication Type:Journal Article
- Keywords:
Stomach neoplasms, hepatoid adenocarcinoma;
Nomogram clinical prediction model;
Long-term survival;
Risk factor
- From:
Chinese Journal of Gastrointestinal Surgery
2025;28(2):157-168
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.
