89674Zhongnan Hospital of Wuhan University, Wuhan, China.
Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221078732. doi: 10.1177/15330338221078732.
We aimed to determine the epidermal growth factor receptor () genetic profile of lung cancer in Asians, and develop and validate a non-invasive prediction scoring system for mutation before treatment. This was a single-center retrospective cohort study using data of patients with lung cancer who underwent detection (n = 1450) from December 2014 to October 2020. Independent predictors were filtered using univariate and multivariate logistic regression analyses. According to the weight of each factor, a prediction scoring system for mutation was constructed. The model was internally validated using bootstrapping techniques and temporally validated using prospectively collected data (n = 210) between November 2020 and June 2021. In 1450 patients with lung cancer, 723 single mutations and 51 compound mutations were observed in . Thirty-nine cases had two or more synchronous gene mutations. We developed a scoring system according to the independent clinical predictors and stratified patients into risk groups according to their scores: low-risk (score <4), moderate-risk (score 4-8), and high-risk (score >8) groups. The C-statistics of the scoring system model was 0.754 (95% CI 0.729-0.778). The factors in the validation group were introduced into the prediction model to test the predictive power of the model. The results showed that the C-statistics was 0.710 (95% CI 0.638-0.782). The Hosmer-Lemeshow goodness-of-fit showed that χ = 6.733, P = 0.566. The scoring system constructed in our study may be a non-invasive tool to initially predict the mutation status for those who are not available for gene detection in clinical practice.
我们旨在确定亚洲人群肺癌中表皮生长因子受体（）的遗传特征，并开发和验证一种非侵入性的预测评分系统，用于治疗前预测 突变。这是一项单中心回顾性队列研究，使用了 2014 年 12 月至 2020 年 10 月期间接受检测的肺癌患者的数据（n=1450）。使用单变量和多变量逻辑回归分析筛选独立预测因素。根据每个因素的权重，构建了一个用于预测突变的评分系统。使用自举技术对模型进行内部验证，并使用 2020 年 11 月至 2021 年 6 月期间前瞻性收集的数据（n=210）进行时间验证。在 1450 例肺癌患者中，在中观察到 723 个单突变和 51 个复合突变。39 例患者有两个或更多同步基因突变。我们根据独立的临床预测因素制定了评分系统，并根据患者的评分将其分为风险组：低危组（评分<4）、中危组（评分 4-8）和高危组（评分>8）。评分系统模型的 C 统计量为 0.754（95%CI 0.729-0.778）。验证组中的因素被引入预测模型，以检验模型的预测能力。结果表明，C 统计量为 0.710（95%CI 0.638-0.782）。Hosmer-Lemeshow 拟合优度检验显示 χ²=6.733，P=0.566。我们构建的评分系统可能是一种非侵入性工具，可用于预测临床实践中无法进行基因检测的患者的突变状态。
