The Association of Neutrophil-To-Lymphocyte and Platelet-To-Lymphocyte Ratios With Distant Metastasis in Vietnamese Gastric Cancer Patients.

IF 2.1 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2026-07-31 eCollection Date: 2026-01-01 DOI:10.1177/11769351261466304
N Tran, D N L Tran, T H D Phan, H Q Nguyen
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引用次数: 0

Abstract

Background: Gastric cancer (GC) remains a leading cause of mortality in Vietnam, where distant metastasis is frequently present at diagnosis. Since accessible biomarkers are scarce, we evaluated the association of Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR) with distant metastasis in a Vietnamese cohort to develop a model for patient stratification.

Methods: This retrospective study analyzed 114 patients, categorizing into metastatic (Stage IV) and non-metastatic groups per AJCC 8th criteria. The optimal cutoffs were determined using receiver operating characteristic (ROC) curve analysis. The diagnostic efficacy between models was compared by DeLong's test. LASSO regression was employed to identify stable indicators. A multivariable logistic regression model was constructed, and its performance was evaluated using 1,000 bootstrap resamples. Clinical utility was assessed via Decision Curve Analysis (DCA).

Results: Distant metastasis was 44.7% of cases. Optimal cutoffs of 2.0 for NLR and 181.5 for PLR were identified. Both markers were significantly higher in metastatic group and positively correlated with disease progression. LASSO identified PLR, NLR, and tumor location as the most robust determinants. In multivariable analysis, only multiple tumor location and high PLR remained independent prognostic factors. The combined model integrating clinical factors with NLR and PLR significantly outperformed clinical features alone (AUC: 0.766 vs. 0.619, p=0.0036), with an optimism-corrected C-index of 0.708. At a 0.586 threshold, the model achieved 82.5% specificity and 62.7% sensitivity, supporting a conservative "rule-out" strategy. Calibration slope was 0.659. DCA demonstrated superior net benefit across a 15%-90% risk range; specifically, at a 40% threshold, model spared 19 per 100 patients from unnecessary intervention.

Conclusion: This study establishes the first baseline for NLR and PLR in the Vietnamese cohort, showing an association of these biomarkers with GC distant metastasis. A model integrating PLR, NLR and tumor location optimizes patient stratification and resource allocation in resource-constrained healthcare environments.

越南胃癌患者中性粒细胞与淋巴细胞和血小板与淋巴细胞比例与远处转移的关系
背景:胃癌(GC)仍然是越南死亡的主要原因,在越南诊断时经常出现远处转移。由于可获得的生物标志物很少,我们在越南队列中评估了中性粒细胞与淋巴细胞比率(NLR)和血小板与淋巴细胞比率(PLR)与远处转移的关系,以建立患者分层模型。方法:本回顾性研究分析了114例患者,根据AJCC第8条标准分为转移(IV期)和非转移组。采用受试者工作特征(ROC)曲线分析确定最佳截止点。采用DeLong’s检验比较模型间的诊断效果。采用LASSO回归识别稳定指标。构建了一个多变量逻辑回归模型,并使用1000个bootstrap样本对其性能进行了评估。通过决策曲线分析(DCA)评估临床效用。结果:远处转移率为44.7%。结果表明,NLR最佳截止值为2.0,PLR最佳截止值为181.5。这两项指标在转移组均显著升高,且与疾病进展呈正相关。LASSO鉴定出PLR、NLR和肿瘤位置是最可靠的决定因素。在多变量分析中,只有多发肿瘤位置和高PLR仍然是独立的预后因素。综合临床因素与NLR和PLR的联合模型显著优于单独的临床特征(AUC: 0.766 vs. 0.619, p=0.0036),乐观校正的c指数为0.708。在0.586的阈值下,该模型实现了82.5%的特异性和62.7%的敏感性,支持保守的“排除”策略。校正斜率为0.659。在15%-90%的风险范围内,DCA显示出优越的净收益;具体来说,在40%的阈值下,模型使每100名患者中有19人免于不必要的干预。结论:本研究在越南队列中建立了NLR和PLR的第一个基线,显示了这些生物标志物与胃癌远处转移的关联。整合PLR、NLR和肿瘤位置的模型优化了资源受限医疗环境下的患者分层和资源分配。
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
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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