Prediction of tumor deposits in stage I-III gastric cancer: a clinically applicable nomogram integrating clinicopathology outcomes.

IF 3.1 3区医学 Q1 MEDICINE, GENERAL & INTERNAL

Frontiers in Medicine Pub Date : 2025-05-30 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1577569

Kunjie Wang, Yue Huo, Yuanfang Zhang, Song Guo, Weiguang Yu, Na Xiao, Shenyong Su, Lin An

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引用次数: 0

Abstract

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.

Methods: 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%.

Conclusion: 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.

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预测I-III期胃癌肿瘤沉积：一种结合临床病理结果的临床应用nomogram。

目的：探讨I-III期胃癌患者肿瘤沉积（tumor deposits， TD）发展的相关临床病理危险因素，并建立可视化预测模型供临床应用。方法：回顾性分析河北大学附属医院2010年9月至2022年9月收治的1284例胃癌患者。通过简单随机分组，按3:1的比例将患者分为训练组（n = 963）和验证组（n = 321）。采用套索回归分析筛选变量，再采用多元逻辑回归建立个体化nomogram。采用受试者工作特征曲线（AUC）下面积、校准图和决策曲线分析（DCA）来评估模型性能。结果：与td阴性患者相比，td阳性患者（n = 224）的总生存期和无病生存期显著降低（n = 1,060, p < 0.05）。多因素logistic回归分析证实肿瘤大小(OR = 1.26；95% CI 1.01-2.21)， CEA升高(OR = 2.04；95% CI 1.02-3.16)、CA199升高（OR = 1.007, 95% CI:1.003-1.011）和pN分期(OR = 3.22；95% CI 2.12-4.34)作为TD发生的独立预测因子（均p < 0.05）。nomogram显示了稳健的判别能力，在训练组和验证组的AUC值分别为0.803 （95% CI 0.751-0.894）和0.864 （95% CI 0.725-0.917）。校正图显示预测概率与观测概率非常吻合。DCA进一步验证了该模型的临床效用，显示出超过1-99%阈值概率的优越净效益。结论：这种结合肿瘤大小、血清生物标志物（CEA/CA199）和病理分期（pN）的td特异性nomogram方法，为I-III期胃癌术前风险分层和个性化治疗决策提供了一种临床适用的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Medicine Medicine-General Medicine

CiteScore

5.10

自引率

5.10%

发文量

3710

审稿时长

12 weeks

期刊介绍： Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world