Document Type
Article
Publication Date
4-4-2014
Publication Title
PloS One
Department
Geisel School of Medicine
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.
DOI
10.1371/journal.pone.0093430
Dartmouth Digital Commons Citation
Chen, Xing; Foy, Millennia; Kimmel, Marek; and Gorlova, Olga Y., "Modeling the Natural History and Detection of Lung Cancer Based on Smoking Behavior" (2014). Dartmouth Scholarship. 2976.
https://digitalcommons.dartmouth.edu/facoa/2976
