Putila Joseph, Guo Nancy Lan
Department of Environmental and Occupational Health Sciences, School of Public Health, Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.
PLoS One. 2014 Jun 26;9(6):e100994. doi: 10.1371/journal.pone.0100994. eCollection 2014.
Accurate assessment of a patient's risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.
METHODOLOGY/PRINCIPAL FINDINGS: Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations. A Cox model including COPD was estimated on 1,000 bootstrap samples, with the resulting model assessed on ROC, Brier Score, Harrell's C, and Nagelkerke's R2 metrics in order to evaluate improvements in prognostication over a model without COPD. The addition of COPD to the model incorporating cancer stage, age, gender, race, and tumor grade was shown to improve prognostication in multiple patient groups. For lung adenocarcinoma patients, there was an improvement on the prognostication in the overall patient population and in patients without receiving chemotherapy, including those receiving surgery only. For squamous cell carcinoma, an improvement on prognostication was seen in both the overall patient population and in patients receiving multiple types of chemotherapy. COPD condition was able to stratify patients receiving the same treatments into significantly (log-rank p<0.05) different prognostic groups, independent of cancer stage.
CONCLUSION/SIGNIFICANCE: Combining patient information on COPD, cancer stage, age, gender, race, and tumor grade could improve prognostication and prediction of treatment response in individual non-small cell lung cancer patients. This model enables refined prognosis and estimation of clinical outcome of comprehensive treatment regimens, providing a useful tool for personalized clinical decision-making.
准确评估患者的复发风险和治疗反应是肺癌个体化治疗的重要前提。本研究通过使用链接的监测、流行病学和最终结果(SEER)-医疗保险数据,增加化疗和合并症因素,扩展了先前描述的非小细胞肺癌预后模型。
方法/主要发现:使用34203例肺腺癌患者和26967例肺鳞状细胞癌患者的数据,确定慢性阻塞性肺疾病(COPD)在30种治疗组合中对预后的影响。在1000个自抽样样本上估计包含COPD的Cox模型,并根据受试者工作特征曲线(ROC)、Brier评分、Harrell's C和Nagelkerke's R2指标评估所得模型,以评估与不包含COPD的模型相比,预后是否有所改善。在纳入癌症分期、年龄、性别、种族和肿瘤分级的模型中加入COPD,可改善多个患者群体的预后。对于肺腺癌患者,总体患者群体以及未接受化疗(包括仅接受手术的患者)的患者的预后均有所改善。对于肺鳞状细胞癌,总体患者群体以及接受多种化疗的患者的预后均有所改善。COPD状况能够将接受相同治疗的患者分层为预后显著不同(对数秩检验p<0.05)的组,与癌症分期无关。
结论/意义:结合患者的COPD信息、癌症分期、年龄、性别、种族和肿瘤分级,可改善个体非小细胞肺癌患者的预后和治疗反应预测。该模型能够更精确地预测综合治疗方案的预后和临床结果,为个体化临床决策提供了有用工具。