结合CT影像和呼出气体等多标识物的肺癌早期快速诊断及预测模型研究

项目名称： 结合CT影像和呼出气体等多标识物的肺癌早期快速诊断及预测模型研究

项目编号： No.81201166

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 影像医学与生物医学工程

项目作者： 陈星

作者单位： 浙江大学

项目金额： 23万元

中文摘要： 如何更有效的发现早期肺癌病人是降低肺癌死亡率的关键。国际癌症机构(NLST)的最新研究结果表明使用螺旋CT对肺癌高危人群筛查可以降低20%的死亡率。然而从CT发现异常到活检确诊所花费的时间较长，并存在确诊时间不可控，诊断创伤较大及不能覆盖所有的影像学疑似病人等缺点。本研究结合CT筛查和呼吸标识物组合诊断的方法，构建新型的肺癌早期诊断模型。以医学仿真建模的方法，结合肺致癌模型，生长模型及生存率模型构建肺癌生存率仿真系统。从诊断率和生存率两方面研究该新型诊断模型在临床中的作用。所建立起的生存率仿真系统，进一步结合其它早期肺癌检测技术，如冷凝物或血液中生物标识物的检测技术，提供一种可预测其临床生存率的科学方法。本课题的创新之处在于把肺癌呼出气体及其冷凝物检测与CT影像学筛查相结合，构建新型的肺癌早期诊断模型，以实现对肺癌高危人群快速、实时、无损的诊断和预测，提高肺癌病人的整体生存率。

中文关键词： 肺癌；VOCs标志物；CT筛查；建模仿真；早期诊断

英文摘要： The diagnosis of lung cancer at its early stage is a solution to reduce the mortality. Recently, National Lung Screening Trial (NLST) has shown that low-dose CT screening results in 20% mortality reduction in individuals at high LC risk. However, the diagnosis procedure after the abnormal CT still takes a longer time than it is expected, especially when the mass is small. Furthermore, invasive diagnosis procedures, such as biopsy and surgery, could bring an unnecessary harm to the person who did not have a lung cancer. And not all of the LC candidates can be diagnosed using these existing procedures. CT screening combined breath biomarkers is a novel method for a fast, non-invasive diagnosis of lung cancer at its early stage. We developed a model framework for using an existing carcinogenesis model, a model of the natural history of tumor growth and progression, a diagnosis model, and a survival model to predict the distribution of lung cancer survival. After simulating the survival distribution by using this model framework, we can estimate the diagnosis rate and survival rate of the population that were diagnosed through this novel method. This model framework could also be used to estimate the survival distribution of the population that were diagnosed through other detection methods, such as the biomarkers f

英文关键词： Lung Cancer；VOCs Biomarkers；CT Screening；Modeling and Simulation；Early Diagnosis