Prediction of lymph node metastasis in stage I-III colon cancer patients younger than 40 years.

IF 2.5 3区医学 Q2 ONCOLOGY

Clinical & Translational Oncology Pub Date : 2025-09-01 Epub Date: 2025-04-12 DOI:10.1007/s12094-025-03903-3

Wei-Hao Zhang, Meng-Di Huang, Yan-Ling Tu, Kun-Zhai Huang, Chao-Jun Wang, Zhao-Hui Liu, Rui-Sheng Ke

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引用次数: 0

Abstract

Aims: Developing a clinical model to predict the individual risk of lymph node metastasis (LNM) in young colon cancer (CC) patients may address an unmet clinical need.

Methods: A total of 2,360 CC patients under 40 years old were extracted from the SEER database and randomly divided into development and validation cohorts. Risk factors for LNM were identified by using a logistic regression model. A weighted scoring system was built according to beta coefficients (β) calculated by a logistic regression model. Model discrimination was evaluated by C-statistics, model calibration was evaluated by H-L test and calibration plot.

Results: Risk factors were identified as T stage, tumor site, grade and histology. The area under the receiver operating characteristic curve (AUC-ROC) was 0.66 in both cohorts, indicating acceptable discriminatory power. The H-L test showed good calibration in the development cohort (χ²=2.869, P=0.837) and validation cohort (χ²=10.103, P=0.120) which also had been proved by calibration plot. Patients with total risk score of 0-1, 2-3 and 4-6 were considered as low, medium and high risk group.

Conclusion: This clinical risk prediction model is accurate enough to identify young CC patients with high risk of LNM and can further provide individualized clinical reference.

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40岁以下I-III期结肠癌患者淋巴结转移的预测。

目的：建立一个临床模型来预测年轻结肠癌（CC）患者淋巴结转移（LNM）的个体风险，可能会解决一个未被满足的临床需求。方法：从SEER数据库中抽取40岁以下CC患者2360例，随机分为发展组和验证组。使用逻辑回归模型确定LNM的危险因素。根据logistic回归模型计算的β系数（β）建立加权评分体系。采用c统计量评价模型判别，H-L检验和校正图评价模型校正。结果：确定了T分期、肿瘤部位、分级和组织学等危险因素。两组受试者工作特征曲线下面积（AUC-ROC）均为0.66，表明可接受的区分力。H-L检验在发展组（χ2=2.869， P=0.837）和验证组（χ2=10.103， P=0.120）均具有良好的标度，并经标度图验证。总风险评分0 ~ 1、2 ~ 3、4 ~ 6分为低、中、高风险组。结论：该临床风险预测模型能够较准确地识别出LNM高危青年CC患者，并可进一步提供个体化临床参考。

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

Clinical & Translational Oncology 医学-肿瘤学

CiteScore

6.20

自引率

2.90%

发文量

240

审稿时长

1 months

期刊介绍： Clinical and Translational Oncology is an international journal devoted to fostering interaction between experimental and clinical oncology. It covers all aspects of research on cancer, from the more basic discoveries dealing with both cell and molecular biology of tumour cells, to the most advanced clinical assays of conventional and new drugs. In addition, the journal has a strong commitment to facilitating the transfer of knowledge from the basic laboratory to the clinical practice, with the publication of educational series devoted to closing the gap between molecular and clinical oncologists. Molecular biology of tumours, identification of new targets for cancer therapy, and new technologies for research and treatment of cancer are the major themes covered by the educational series. Full research articles on a broad spectrum of subjects, including the molecular and cellular bases of disease, aetiology, pathophysiology, pathology, epidemiology, clinical features, and the diagnosis, prognosis and treatment of cancer, will be considered for publication.