期刊
JOURNAL OF CLINICAL ONCOLOGY
卷 29, 期 35, 页码 4627-4632出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1200/JCO.2010.33.8020
关键词
-
类别
资金
- Oregon Clinical and Translational Research Institute
- American Society of Clinical Oncology
- National Library of Medicine [5K99 LM009889]
Purpose Although adjuvant chemoradiotherapy for resected gallbladder cancer may improve survival for some patients, identifying which patients will benefit remains challenging because of the rarity of this disease. The specific aim of this study was to create a decision aid to help make individualized estimates of the potential survival benefit of adjuvant chemoradiotherapy for patients with resected gallbladder cancer. Methods Patients with resected gallbladder cancer were selected from the Surveillance, Epidemiology, and End Results (SEER) -Medicare database who were diagnosed between 1995 and 2005. Covariates included age, race, sex, stage, and receipt of adjuvant chemotherapy or chemoradiotherapy (CRT). Propensity score weighting was used to balance covariates between treated and untreated groups. Several types of multivariate survival regression models were constructed and compared, including Cox proportional hazards, Weibull, exponential, log-logistic, and lognormal models. Model performance was compared using the Akaike information criterion. The primary end point was overall survival with or without adjuvant chemotherapy or CRT. Results A total of 1,137 patients met the inclusion criteria for the study. The lognormal survival model showed the best performance. A Web browser-based nomogram was built from this model to make individualized estimates of survival. The model predicts that certain subsets of patients with at least T2 or N1 disease will gain a survival benefit from adjuvant CRT, and the magnitude of benefit for an individual patient can vary. Conclusion A nomogram built from a parametric survival model from the SEER-Medicare database can be used as a decision aid to predict which gallbladder patients may benefit from adjuvant CRT.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据