Modeling the natural history and detection of lung cancer based on smoking behavior

Abstract

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.

Figure 1. Probability density functions of nodal and distant metastases from the time of tumor onset, using the estimated parameters ξ = 0.01,

 = 8.05×10⁻⁹,  = 2.78×10⁻⁹, K = 3.80 and  = 1.15.

Figure 2. Comparison of the model fit in the period of 1995–1999 to the data of SEER 2004–2008.

(a) the stage distribution conditional on tumor size and (b) the tumor size distribution; (c) Comparison of the predictive model 1988–1999 and SEER 1988–1999, where the data is summarized as stage distribution conditional on tumor size.

Figure 3. Tumor size distribution in predictive models, (a) Stage N0,M0 in SEER (2004–2008) and model (1988–1999), stage Localized by SEER standard in SEER (1988–1999), (b) Stage Nx,M0 (x≥1) in SEER (2004–2008) and model (1988–1999), stage Regional by SEER standard in SEER (1988–1999), (c) Stage M1 in SEER (2004–2008) and model (1988–1999), stage Distant by SEER standard in SEER (1988–1999).