Abstract B02: Modeling the natural history and detection of lung cancer
Authors: Chen Xing , Foy Millennia , Kimmel Marek , Gorlova Olga Y. 
Published: January 2014

Abstract
Background: Lung cancer is the most deadly cancer in men and women worldwide. The major reason for its high mortality rate is that it is less likely than many other cancers to be detected at an early stage. Age, gender, smoking status, tumor size, and disease stage at the time of diagnosis are highly related to the prognosis of patients with lung cancer. In this study, we developed a method for simulating the characteristics of, at an individual level, lung cancer in a population at the time of diagnosis, according to the progression and detection of the disease.
Model framework: In this framework, the carcinogenesis, tumor-progression, and detection models work together with data on the lifespan of an individual from his/her birth to the time of lung cancer diagnosis. The carcinogenesis model combined with data on the patients' smoking duration and intensity generated by the Smoking History Generator (SHG) was used to predict the incidence of lung cancer in a person's lifetime, as well as to calculate the age of the patient at the initiation of lung cancer. The tumor growth (with parameters: ξ,μ_n,μ_m,k,θ) and detection models (with parameters: α,W_0,W_1 and W_2) were applied jointly to predict the age, tumor size, and disease stage at the time of diagnosis for the modeled individual.
Estimation and prediction: Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were reconstructed to fit the lung cancer progression and detection model. We derive the least-squares function: F(f(x| ξ,μ_n,μ_m,k,θ,α,W_0,W_1,W_2)-Y ^(x)), where f is the simulated joint distribution of tumor size and stage and Y ^ is the observed joint distribution of SEER data, based on these parameters, and apply the Nelder-Mead method to achieve the best fit parameters in the model. The fitted model combined with a previously fitted carcinogenesis model was used to predict the distribution of age, gender, tumor size, and disease stage at diagnosis and the results were validated against independent data from the SEER database collected from 1988 to 1999.
Result: The model predicted both the gender distribution (58.1% are male) among LC patients and median age (69 years old) at the time of diagnosis, whereas 58.8% male patients and the same median age were presented in SEER 1988-1999. The model predicted that about 26.5%, 22.4%, and 51.1% would be staged as N0M0, N1M0, and M1, respectively, whereas about 17.0%, 25.4%, 45.7, and 11.9% were actually staged as N0M0, N1M0, M1, and missing stage, respectively. The predicted tumor size (diameter; mean, 4.57; Standard Deviation (SD), 3.21) is comparable to the tumor size (diameter; mean, 4.44; SD, 5) in SEER.
Conclusion: The model accurately predicted the gender distribution and median age of LC patients of diagnosis, and reasonably (but not exactly) predicted the tumor size and disease stage distribution.
Citation Format: Xing Chen, Millennia Foy, Marek Kimmel, Olga Y. Gorlova. Modeling the natural history and detection of lung cancer. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr B02.