Chen Xing, Foy Millennia, Kimmel Marek, Gorlova Olga Y
Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang, China; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
PLoS One. 2014 Apr 4;9(4):e93430. doi: 10.1371/journal.pone.0093430. eCollection 2014.
In this study, we developed a method for modeling the progression and detection of lung cancer based on the smoking behavior at an individual level. The model allows obtaining the characteristics of lung cancer in a population at the time of diagnosis. Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were used to fit the lung cancer progression and detection model. The fitted model combined with a smoking based carcinogenesis model was used to predict the distribution of age, gender, tumor size, disease stage and smoking status at diagnosis and the results were validated against independent data from the SEER database collected from 1988 to 1999. The model accurately predicted the gender distribution and median age of LC patients of diagnosis, and reasonably predicted the joint tumor size and disease stage distribution.
在本研究中，我们开发了一种基于个体吸烟行为对肺癌进展和检测进行建模的方法。该模型能够获取人群在肺癌诊断时的特征。使用2004年至2008年期间从监测、流行病学和最终结果（SEER）数据库收集的肺癌数据来拟合肺癌进展和检测模型。将拟合模型与基于吸烟的致癌模型相结合，用于预测诊断时年龄、性别、肿瘤大小、疾病分期和吸烟状况的分布，并根据1988年至1999年从SEER数据库收集的独立数据对结果进行验证。该模型准确预测了肺癌患者诊断时的性别分布和年龄中位数，并合理预测了联合肿瘤大小和疾病分期分布。
