Purpose

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.

Methods

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.

Results

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).

Conclusion

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.

中文翻译：

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非小细胞肺癌患者脑转移高危人群的鉴定

目的

在非小细胞肺癌 (NSCLC) 患者中识别出高危脑转移 (BM) 组可能会导致早期干预并可能带来更好的预后。该研究的目的是通过生成具有公认和可访问的风险因素的多变量模型来识别这一群体。

方法

2010-2020 年期间在单个中心看到的患者的回顾性队列，分为训练 (TD) 和验证 (VD) 数据集，与 BM 的关联在 TD 中用 logit 测量，显着相关的变量用于生成多变量模型。使用 AUC/C 统计量、Akaike 信息标准和 Brier 分数来衡量模型的性能。

结果

从符合严格资格标准的 570 名 NSCLC 患者中随机组合 TD 和 VD，在两个数据集中没有发现显着差异。多变量 logit 分析中与 BM 相关的变量是年龄 [P 0.001，OR 0.96 (95% CI 0.93–0.98)]；突变状态阳性 [P 0.027, OR 1.96 (95% CI 1.07–3.56); 和癌胚抗原水平 [P 0.016, OR 1.001 (95% CI 1.000–1.003)。BM 在整个队列的 24% 中被诊断出来。模型简化后分层为高风险组，显示 BM 的频率为 63% (P < 0.001)。

结论

包含年龄、癌胚抗原水平和突变状态的多变量模型允许识别 NSCLC 患者中真正的高风险 BM 组。