Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan.
National Research Institute of Chinese Medicine, Ministry of Health and Welfare, Taipei, Taiwan.
PLoS One. 2019 Aug 16;14(8):e0220730. doi: 10.1371/journal.pone.0220730. eCollection 2019.
Molecular markers are important variables in the selection of treatment for cancer patients and highly associated with their survival. Therefore, a nomogram that can predict survival probability by incorporating epidermal growth factor receptor mutation status and treatments for patients with advanced adenocarcinoma would be highly valuable. The aim of the study is to develop and validate a novel nomogram, incorporating epidermal growth factor receptor mutation status and treatments, for predicting 1-year and 2-year survival probability of patients with advanced adenocarcinoma.
Data on 13,043 patients between June 1, 2011, and December 31, 2014 were collected. Seventy percent of them were randomly assigned to the training cohort for nomogram development, and the remaining 30% assigned to the validation cohort. The most important factors for constructing the nomogram were identified using multivariable Cox regression analysis. The discriminative ability and calibration of the nomograms were tested using C-statistics, calibration plots, and Kaplan-Meier curves.
In the training cohort, 1-year and 2-year OS were 52.8% and 28.5% in EGFR(-) patients, and 73.9% and 44.1% in EGFR(+) patients, respectively. In EGFR(+) group, factors selected were age, gender, congestive heart failure, renal disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, first-line chemotherapy, ECOG performance status, malignant pleural effusion, and smoking. In EGFR(-) group, factors selected were age, gender, myocardial infarction, cerebrovascular disease, chronic pulmonary disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, ECOG performance status, malignant pleural effusion, and a history of smoking. Two nomograms show good accuracy in predicting OS, with a concordance index of 0.83 in EGFR(+) and of 0.88 in EGFR(-).
The survival prediction models can be used to make individualized predictions with different EGFR mutation status and a useful tool for selecting regimens for treating advanced adenocarcinoma.
分子标志物是癌症患者治疗选择的重要变量，与患者的生存高度相关。因此，建立一个能够结合表皮生长因子受体突变状态和治疗方案来预测晚期腺癌患者生存概率的列线图将具有重要价值。本研究旨在建立并验证一个新的列线图，纳入表皮生长因子受体突变状态和治疗方案，用于预测晚期腺癌患者 1 年和 2 年的生存概率。
收集了 2011 年 6 月 1 日至 2014 年 12 月 31 日期间的 13043 名患者的数据。其中 70%的数据被随机分配到训练队列中用于列线图的开发，其余 30%的数据分配到验证队列中。使用多变量 Cox 回归分析确定构建列线图的最重要因素。使用 C 统计量、校准图和 Kaplan-Meier 曲线测试列线图的判别能力和校准度。
在训练队列中，EGFR(-)患者的 1 年和 2 年 OS 分别为 52.8%和 28.5%，EGFR(+)患者的 1 年和 2 年 OS 分别为 73.9%和 44.1%。在 EGFR(+)组中，选择的因素包括年龄、性别、充血性心力衰竭、肾脏疾病、检查的淋巴结数量、肿瘤分期、手术干预、放疗、一线化疗、ECOG 表现状态、恶性胸腔积液和吸烟。在 EGFR(-)组中，选择的因素包括年龄、性别、心肌梗死、脑血管疾病、慢性肺部疾病、检查的淋巴结数量、肿瘤分期、手术干预、放疗、ECOG 表现状态、恶性胸腔积液和吸烟史。两个列线图在预测 OS 方面均具有较好的准确性，EGFR(+)组的一致性指数为 0.83，EGFR(-)组的一致性指数为 0.88。
该生存预测模型可用于根据不同的 EGFR 突变状态进行个体化预测，是治疗晚期腺癌的一种有用工具。
