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头颈部腺样囊性癌总生存预测列线图的建立与验证。

Development and validation of a nomogram for predicting overall survival of head and neck adenoid cystic carcinoma.

机构信息

Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, Fujian, China.

Medical Record Room, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China.

出版信息

Sci Rep. 2024 Nov 2;14(1):26406. doi: 10.1038/s41598-024-77322-9.

Abstract

This study aimed to develop and validate a nomogram using clinical variables to guide personalized treatment strategies for adenoid cystic carcinoma of the head and neck (ACCHN). Data from 1069 patients with ACCHN diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were used to construct the nomogram. External validation was performed using an independent cohort of 70 patients from Fujian Cancer Hospital. Multivariate Cox regression analysis was conducted using IBM SPSS version 26.0 and R Software version 4.2.3. The concordance index (C-index) and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the nomogram. Age, tumor site, surgery, N stage, M stage, and TNM stage were identified as independent prognostic factors through univariate and multivariate Cox analyses. The nomogram demonstrated superior predictive performance compared to the TNM staging system, effectively stratifying patients into high-risk and low-risk groups. This nomogram offers a valuable tool for predicting overall survival in patients with ACCHN and tailoring individualized treatment approaches.

摘要

本研究旨在开发和验证一种列线图,使用临床变量指导头颈部腺样囊性癌(ACCHN)的个体化治疗策略。使用来自监测、流行病学和最终结果(SEER)数据库的 2004 年至 2015 年间诊断为 ACCHN 的 1069 名患者的数据构建列线图。使用来自福建肿瘤医院的 70 名患者的独立队列进行外部验证。使用 IBM SPSS 版本 26.0 和 R 软件版本 4.2.3 进行多变量 Cox 回归分析。使用一致性指数(C 指数)和接收者操作特征(ROC)曲线评估列线图的预测准确性。通过单变量和多变量 Cox 分析确定年龄、肿瘤部位、手术、N 分期、M 分期和 TNM 分期是独立的预后因素。与 TNM 分期系统相比,该列线图具有更好的预测性能,能够有效地将患者分为高危和低危组。该列线图为预测 ACCHN 患者的总生存率和制定个体化治疗方法提供了有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eac1/11531573/f2b51f179a2e/41598_2024_77322_Fig1_HTML.jpg

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