A predictive nomogram for predicting liver metastasis in early-onset colon cancer: a population-based study.

IF 1.5 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2025-01-31 Epub Date: 2025-01-21 DOI:10.21037/tcr-24-1159

Weichao Zeng, Yafeng Sun, Zhengrong Liao, Jianhua Xu

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引用次数: 0

Abstract

Background: The risk of liver metastasis (LM) may be estimated using predictive nomograms. While the nomogram has recently been applied in oncology, there are relatively few studies concentrating on predicting LM in patients with early-onset colon cancer. We aimed to identify independent risk factors for LM in patients with early-onset colon cancer and develop a nomogram for predicting the probability of LM in these patients.

Methods: Our study encompassed 4,890 early-onset colon cancer patients with LM who were registered in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. These patients were randomly allocated into training and validation cohorts at a ratio of 7:3. Univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors for LM, and a nomogram was developed using these factors. The model's discriminatory power, accuracy, and clinical utility were evaluated using receiver operating characteristics (ROC), calibration, and decision curve analyses.

Results: Overall, 4,890 patients with early-onset colon cancer and LM were selected from the SEER database. LM incidence in these patients was 18.4%. Univariate and multivariate analyses revealed histological type, T stage, N stage, and carcinoembryonic antigen (CEA) level as independent risk factors. ROC curve analysis revealed that the predictive nomogram for LM risk had an area under the curve of 0.812 [95% confidence interval (CI): 0.795-0.829] and 0.809 (95% CI: 0.784-0.834) in the training and validation sets, respectively, demonstrating good discriminatory ability of the model. Calibration curve analysis showed good agreement between predicted values from the nomogram and actual observations, and the decision curve analysis (DCA) demonstrated the high clinical utility of the nomogram.

Conclusions: LM incidence was higher in patients with early-onset colon cancer. Our nomogram demonstrates a high level of efficacy in predicting the risk of LM in patients with early-onset colon cancer, thereby assisting clinicians in making well-informed treatment decisions prior to further intervention.

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预测早发性结肠癌肝转移的预测图：一项基于人群的研究。

背景：肝转移（LM）的风险可以用预测图来估计。虽然最近nomogram在肿瘤学上得到了应用，但是关于预测早发性结肠癌患者LM的研究相对较少。我们的目的是确定早发性结肠癌患者发生LM的独立危险因素，并制定预测这些患者发生LM的概率的线图。方法：我们的研究纳入了4,890例早发性结肠癌LM患者，这些患者于2010年至2015年在监测、流行病学和最终结果（SEER）数据库中登记。这些患者以7:3的比例随机分配到训练组和验证组。进行单因素和多因素logistic回归分析，以确定LM的独立危险因素，并利用这些因素制定了nomogram。使用受试者工作特征（ROC）、校准和决策曲线分析来评估模型的区分能力、准确性和临床实用性。结果：总体而言，从SEER数据库中选择了4,890例早发性结肠癌和LM患者。这些患者的LM发病率为18.4%。单因素和多因素分析显示，组织学类型、T分期、N分期和癌胚抗原（CEA）水平是独立的危险因素。ROC曲线分析显示，LM风险的预测模态图在训练集和验证集的曲线下面积分别为0.812[95%置信区间（CI）： 0.795-0.829]和0.809 (95% CI: 0.784-0.834)，表明模型具有较好的判别能力。校正曲线分析显示，nomogram预测值与实际观测值吻合良好，决策曲线分析（decision curve analysis， DCA）显示nomogram具有较高的临床应用价值。结论：早发性结肠癌患者LM发生率较高。我们的nomogram （x线图）在预测早发性结肠癌患者发生LM的风险方面具有很高的有效性，从而帮助临床医生在进一步干预之前做出明智的治疗决策。

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

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.