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术前结直肠癌淋巴结转移预测模型列线图的建立

Development of a nomogram for predicting lymph node metastasis of colorectal tumors in preoperative period
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摘要 目的构建并验证一个简单、准确的临床列线图,用于预测个体化大肠癌患者术前淋巴结转移情况。方法患者的临床数据来自SEER数据库,使用Logistic回归模型构建预测模型。列线图的准确性通过受试者工作特征曲线(ROC)、校准曲线进行评估和检验。结果本研究入选SEER数据库中78584例符合条件的结直肠癌(CRC)患者。淋巴结转移相关的危险因素包括诊断年龄、T分期、M分期、肿瘤分级和癌胚抗原(CEA)水平。模型显示出较好的准确性,AUC值为0.734(95%CI,0.730~0.741)。在验证队列中,该模型仍然可以达到高AUC值0.728(95%CI,0.722~0.734)。结论年龄、T分期、M分期、肿瘤分级和CEA水平是结直肠癌淋巴结转移的独立危险因素,据此建立的列线图可以预测结直肠癌淋巴结转移概率。 Objective The purpose of this study is to construct and validate a simple and accurate clinical nomogram for predicting preoperative lymph node metastasis(LNM)in individualized patients with colorectal cancer.Methods The clinical data of patients came from SEER database,and Logistic regression model was used to build a prediction model.The accuracy of nomograph was evaluated and tested by receiver operating characteristic curve(ROC)curve and calibration curve.Results 78584 eligible colorectal cancer(CRC)patients in SEER database were enrolled in this study.The risk factors related to lymph node metastasis include diagnostic age,T stage,M stage,tumor grade and carcinoembryonic antigen(CEA)level.The model showed good accuracy,with an AUC value of 0.734(95%CI,0.730-0.741).In the validation queue,the model can still reach a high AUC value of 0.728(95%CI,0.722-0.734).Conclusions Age,T stage,M stage,tumor grade and CEA level are independent risk factors for lymph node metastasis of colorectal cancer.The nomogram established based on these factors can predict the probability of lymph node metastasis of colorectal cancer.
作者 蒋明妮 丁清清 Jiang Mingni;Ding Qingqing(The First Affiliated Hospital of Nanjing Medical University,Nanjing,210029,China;Department of Geriatric Oncology,the First Affiliated Hospital of Nanjing Medical University,Nanjing,210029,China)
出处 《中华老年医学杂志》 CAS CSCD 北大核心 2023年第1期73-79,共7页 Chinese Journal of Geriatrics
基金 国家自然科学基金青年基金项目(202282103373) 国家重点研发计划(2019YFC1604905)。
关键词 结直肠肿瘤 肿瘤转移 预测模型 Colorectal neoplasms Neoplasm metastasis Prediction model
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