Predictive value of a nomogram for melanomas with brain metastases at initial diagnosis
Predictive value of a nomogram for melanomas with brain metastases at initial diagnosis
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DOI：
10.1002/cam4.2644
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发表时间：
2019-10-27
期刊：
影响因子：
4
通讯作者：
He, Xue-Xin
中科院分区：
文献类型：
--
作者：
Liu, Hong;Xu, Yan-Bo;He, Xue-Xin
背景 目前的研究缺乏基于大量队列的初步诊断时对脑转移黑色素瘤 (MBM) 的发病率和预后的估计。本研究旨在为新诊断的 MBM 构建有效的预后列线图。材料和方法 2010 年至 2014 年期间通过监测、流行病学和最终结果计划诊断为黑色素瘤的患者纳入我们的研究。使用逻辑回归分析确定了预测脑转移（BM）的危险因素。进行 Cox 回归分析以确定总生存期 (OS) 的预后因素。基于 Cox 回归分析建立了用于估计 6 个月、9 个月和 12 个月 OS 的列线图。使用 C 统计、校准图和 Kaplan-Meier 曲线测试列线图的辨别能力和校准。结果 共纳入 62,369 名黑色素瘤患者，其中 928 名 BM 患者。性别、婚姻状况、保险状况、亚部位、原发部位手术、放疗、化疗、骨转移、肝转移和肺转移与初次诊断时的 MBM 相关。在多变量 Cox 回归中，以下八个变量被纳入 OS 预测中：年龄、未婚状况、未对原发部位进行手术或未知、未进行放射治疗或未知、未进行化疗或未知、有骨转移、有肝转移和有肺转移。从判别能力和校准结果来看，列线图显示出良好的预测能力，C 统计值为 0.716（95% CI，0.695-0.737）。结论 这项研究基于大型队列，很好地估计了 MBM 患者的发病率和预后。列线图表现良好，可能是预测预后的有用工具。
Background Estimation of incidence and prognosis of melanomas with brain metastases (MBM) at initial diagnosis based on a large cohort is lacking in current research. This study aims to construct an effective prognostic nomogram for newly diagnosed MBM. Materials and Methods Patients diagnosed with melanomas from Surveillance, Epidemiology, and End Results program between 2010 and 2014 were enrolled in our study. Risk factors predicting brain metastases (BM) were identified using logistic regression analysis. Cox regression analysis was performed to identify prognostic factors of overall survival (OS). Nomogram for estimating 6-, 9-, and 12-month OS was established based on Cox regression analysis. The discriminative ability and calibration of the nomogram were tested using C statistics, calibration plots, and Kaplan-Meier curves. Results Sixty-two thousand three hundred and sixty-nine melanoma patients were enrolled, including 928 with BM. Sex, marital status, insurance status, subsite, surgery of primary sites, radiation, chemotherapy, bone metastases, liver metastases, and lung metastases were associated with MBM at initial diagnosis. On multivariable Cox regression, the following eight variables were incorporated in the prediction of OS: age, unmarried status, absence of surgery to primary sites or unknown, absence of radiation or unknown, absence of chemotherapy or unknown, with bone metastases, with liver metastases, and with lung metastases. The nomogram showed good predictive ability as indicated by discriminative ability and calibration, with the C statistics of 0.716 (95% CI, 0.695-0.737). Conclusions The incidence and prognosis of MBM patients were well estimated in this study based on a large cohort. The nomogram performed well and could be a useful tool to predict prognosis.