Lung Cancer in the U.S.: Trends and Prevention

描述(由申请人提供)： 在我们目前资助的国际癌症研究中心的资助下，我们一直在开发基于多阶段致癌思想的模型，用于预测肺癌的发病率和死亡率。在这一更新的CISNET应用程序中，我们建议使用这些模型来预测在不同吸烟情况下美国的肺癌风险。我们还将开发用户友好的软件来实施我们的模型。该软件将向感兴趣的科学家免费提供。我们的模型可以明确地容纳个人的详细吸烟史，包括开始吸烟的年龄、每天吸烟的数量、吸烟水平的变化以及戒烟时的年龄。此外，这些模型还可以用来预测个人和人群的风险。因此，该模型可用于预测不同戒烟干预情景下的个体和人群风险。由于这些模型是基于癌症发生、促进和发展的生物学范式，它们可以用来产生关于烟草诱发肺癌机制的生物学假说，并探索预测的风险在多大程度上取决于吸烟诱发肺癌的特定机制方面。我们建议探索与由CISNET支持的其他研究人员的合作，特别是那些有兴趣将我们的模型用作筛查模型的“自然历史”部分的研究人员。

DESCRIPTION (provided by applicant): Under the auspices of our currently funded CISNET grant we have been developing models based on ideas of multistage carcinogenesis for prediction of lung cancer incidence and mortality rates. In this renewal CISNET application we propose to use these models to predict lung cancer risk in the US under diverse smoking scenarios. We will also develop user-friendly software to implement our models. This software will be made freely available to interested scientists. Our models can explicitly accommodate detailed smoking histories on individuals including age at initiation, number of cigarettes smoked per day, changes in levels of smoking, and age at quitting if an ex-smoker. Moreover the models can be used to predict risks both in individuals and populations. Thus the models can be used to predict both individual and population risks under various intervention scenarios for smoking cessation. Since the models are based on the biological paradigm of initiation, promotion and progression in carcinogenesis, they can be used to generate biological hypotheses regarding the mechanism of tobacco induced lung cancer and to explore the extent to which projected risks depend on specific mechanistic aspects of smoking-induced lung cancer. We propose to explore collaboration with other investigators supported by CISNET, particularly those interested in using our model as the 'natural history' component of screening models.