PDF(Pubmed)
Abstract：
UNASSIGNED: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means.
UNASSIGNED: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations.
UNASSIGNED: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency.
UNASSIGNED: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.
UNASSIGNED: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations.
UNASSIGNED: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency.
UNASSIGNED: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.
摘要：
■血清肿瘤标志物（STMs）在评估非小细胞肺癌（NSCLC）患者表皮生长因子受体（EGFR）突变中的预测价值，特别是那些非IA阶段的人，仍然知之甚少。这项研究的目的是建立一个包括STMs和其他临床特征的预测模型，旨在通过非侵入性手段实现EGFR突变的精确预测。
■我们回顾性收集了6711例接受EGFR基因检测的非小细胞肺癌患者。最终,分析了3221例IA期患者和1442例非IA期患者，以评估几种临床特征和STM对EGFR突变的潜在预测价值。
■在所有NSCLC患者中的3866例患者(57.9%)中检测到EGFR突变。在IA期患者中，没有一个STM作为预测EGFR突变的重要预测因子。将非IA期患者分为研究组(n=1043)和验证组(n=399)。在研究小组中,单变量分析显示EGFR突变与STMs(癌胚抗原(CEA)，鳞状细胞癌抗原(SCC),和细胞角蛋白-19片段(CYFRA21-1))。包含CEA的列线图，CYFRA21-1,病理学,性别,使用多变量分析的结果构建了预测非IA期EGFR突变的吸烟史。曲线下面积（AUC=0.780）和决策曲线分析显示出良好的预测性能和列线图的临床实用性。此外,随机森林模型还展示了八种机器学习算法中最高的平均C指数0.793，展示卓越的预测效率。
CYFRA21-1和CEA已被确定为预测非IA期NSCLC患者EGFR突变的关键因素。将STM与其他临床因素相结合的列线图和8个机器学习模型可以有效预测EGFR突变的概率。
■我们回顾性收集了6711例接受EGFR基因检测的非小细胞肺癌患者。最终,分析了3221例IA期患者和1442例非IA期患者，以评估几种临床特征和STM对EGFR突变的潜在预测价值。
■在所有NSCLC患者中的3866例患者(57.9%)中检测到EGFR突变。在IA期患者中，没有一个STM作为预测EGFR突变的重要预测因子。将非IA期患者分为研究组(n=1043)和验证组(n=399)。在研究小组中,单变量分析显示EGFR突变与STMs(癌胚抗原(CEA)，鳞状细胞癌抗原(SCC),和细胞角蛋白-19片段(CYFRA21-1))。包含CEA的列线图，CYFRA21-1,病理学,性别,使用多变量分析的结果构建了预测非IA期EGFR突变的吸烟史。曲线下面积（AUC=0.780）和决策曲线分析显示出良好的预测性能和列线图的临床实用性。此外,随机森林模型还展示了八种机器学习算法中最高的平均C指数0.793，展示卓越的预测效率。
CYFRA21-1和CEA已被确定为预测非IA期NSCLC患者EGFR突变的关键因素。将STM与其他临床因素相结合的列线图和8个机器学习模型可以有效预测EGFR突变的概率。
