Cacho-Díaz Bernardo, Cuapaténcatl Laura Denisse, Rodríguez Jose Antonio, Garcilazo-Reyes Ytel Jazmin, Reynoso-Noverón Nancy, Arrieta Oscar
Neuro Oncology Unit, National Cancer Institute, San Fernando 22, Tlalpan, 14080, Mexico City, ZC, Mexico.
Research Department, National Cancer Institute, Mexico City, Mexico.
J Neurooncol. 2021 Oct;155(1):101-106. doi: 10.1007/s11060-021-03849-w. Epub 2021 Sep 21.
Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors.
A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score.
From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001).
A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.
识别非小细胞肺癌（NSCLC）患者中脑转移（BM）的高危组，这可能会带来早期干预并可能改善预后。本研究的目的是通过构建一个包含公认且易于获取的风险因素的多变量模型来识别该组患者。
对2010年至2020年在单一中心就诊的患者进行回顾性队列研究，将其分为训练（TD）和验证（VD）数据集，在TD中用逻辑回归测量与BM的关联，使用与BM显著相关的变量生成多变量模型。用AUC/C统计量、赤池信息准则和布里尔评分来衡量模型的性能。
从570例符合严格纳入标准的NSCLC患者中随机组建了TD和VD，两个数据集之间未发现显著差异。多变量逻辑回归分析中与BM相关的变量有年龄[P = 0.001，OR = 0.96（95%CI 0.93 - 0.98）]；突变状态为阳性[P = 0.027，OR = 1.96（95%CI 1.07 - 3.56）]；以及癌胚抗原水平[P = 0.016，OR = 1.001（95%CI 1.000 - 1.003）]。整个队列中有24%被诊断为BM。模型简化后分层为高危组，BM的发生率为63%（P < 0.001）。
一个包含年龄、癌胚抗原水平和突变状态的多变量模型能够识别NSCLC患者中真正的BM高危组。
