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基于监测、流行病学和最终结果(SEER)数据库构建诊断列线图模型,以预测临床T1或T2期结肠癌患者发生淋巴结转移的风险

Construction of a diagnostic nomogram model for predicting the risk of lymph node metastasis in clinical T1 or T2 colon cancer based on the SEER database.

作者信息

Zeng Weichao, Xu Jianhua, Liao Zhengrong, Sun Yafeng

机构信息

Department of Gastrointestinal Surgery, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Department of Gastrointestinal Surgery, the Second Clinical School of Medicine, Fujian Medical University, Quanzhou, China.

出版信息

Transl Cancer Res. 2024 Feb 29;13(2):1016-1025. doi: 10.21037/tcr-23-1451. Epub 2024 Jan 25.

Abstract

BACKGROUND

There are few methods related to predicting lymph node metastasis (LNM) in patients with clinically staged T1 or T2 colon cancer. In this study, we aimed to discover independent risk factors for patients with pathologic T-stage 1 (pT1) or pT2 colon cancer with LNM and to develop a nomogram for predicting the probability of LNM for patients with clinically staged T1 or T2 colon cancer.

METHODS

All data were drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors for LNM were identified using univariate and multivariate logistic regression analyses, and these factors were used to construct a nomogram. The discriminatory power, accuracy, and clinical utility of the model were evaluated using receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA), respectively.

RESULTS

According to the inclusion and exclusion criteria, 32,803 patients with stage pT1 or pT2 colon cancer who had undergone surgery were selected from the SEER database. The data showed that the incidence of LNM in patients with pT1 and pT2 colon cancer was 17.11%. The age, histological grade, histological type, T classification, M classification, and tumour location were independent risk factors identified through univariate and multivariate analyses, and these factors were used to construct a nomogram. The ROC curve analysis showed that the area under the curve (AUC) of the ROC of the predictive nomogram for LNM risk was 0.6714 [95% confidence interval (CI): 0.6621-0.6806] in the training set and 0.6567 (95% CI: 0.6422-0.6712) in the validation set, indicative of good discriminatory power of the model. Calibration curve analysis demonstrated good agreement between the nomogram prediction and actual observation. DCA showed excellent clinical utility of the prediction model.

CONCLUSIONS

The incidence of LNM was high in patients with pT1 and pT2 colon cancer. The nomogram established in this study can accurately predict the risk of LNM in patients with clinically staged T1 or T2 colon cancer before further clinical intervention, which allows clinicians to develop optimal treatment.

摘要

背景

临床上T1或T2期结肠癌患者中,预测淋巴结转移(LNM)的方法较少。在本研究中,我们旨在发现病理T分期为1期(pT1)或pT2的结肠癌伴LNM患者的独立危险因素,并开发一种列线图来预测临床分期为T1或T2的结肠癌患者发生LNM的概率。

方法

所有数据均来自监测、流行病学和最终结果(SEER)数据库。使用单因素和多因素逻辑回归分析确定LNM的独立危险因素,并将这些因素用于构建列线图。分别使用受试者操作特征(ROC)、校准和决策曲线分析(DCA)评估模型的鉴别能力、准确性和临床实用性。

结果

根据纳入和排除标准,从SEER数据库中选取了32803例接受手术的pT1或pT2期结肠癌患者。数据显示,pT1和pT2结肠癌患者的LNM发生率为17.11%。年龄、组织学分级、组织学类型、T分类、M分类和肿瘤位置是通过单因素和多因素分析确定的独立危险因素,并将这些因素用于构建列线图。ROC曲线分析显示,LNM风险预测列线图在训练集中的ROC曲线下面积(AUC)为0.6714 [95%置信区间(CI):0.6621 - 0.6806],在验证集中为0.6567(95%CI:0.6422 - 0.6712),表明该模型具有良好的鉴别能力。校准曲线分析表明列线图预测与实际观察结果之间具有良好的一致性。DCA显示预测模型具有出色的临床实用性。

结论

pT1和pT2结肠癌患者的LNM发生率较高。本研究建立的列线图可以在进一步临床干预前准确预测临床分期为T1或T2的结肠癌患者发生LNM的风险,这有助于临床医生制定最佳治疗方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb52/10928598/e8aec9c586de/tcr-13-02-1016-f1.jpg

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