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CISNET赠款的支持下，我们一直在开发基于多阶段致癌思想的模型，用于预测肺癌发病率和死亡率。在这个更新的CISNET应用程序中，我们建议使用这些模型来预测美国不同吸烟情况下的肺癌风险。我们还将开发用户友好的软件来实现我们的模型。该软件将免费提供给感兴趣的科学家。我们的模型可以明确地适应个人的详细吸烟史，包括开始时的年龄，每天吸烟的香烟数量，吸烟水平的变化，以及戒烟时的年龄。此外，该模型可用于预测个人和群体的风险。因此，该模型可用于预测各种戒烟干预方案下的个体和人群风险。由于模型是基于生物学范式的启动，促进和发展的致癌作用，它们可以用来产生生物学假说的机制，烟草诱导的肺癌，并探讨在何种程度上预测的风险取决于特定的机制方面吸烟诱导的肺癌。我们建议探索与其他研究人员的合作，由CISNET支持，特别是那些有兴趣使用我们的模型作为筛选模型的“自然历史”的组成部分。

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.