Modeling the transition of lung cancer from early to advanced stage.

We present a stochastic parametric model of the natural history of lung cancer that predicts the primary tumor volume at the moment the disease transits from early to advanced stage. Our model also produces estimates for the probability of symptomatic detection as a function of tumor volume and clinical stage. We estimate model parameters by likelihood maximization using data from the Mayo Lung Project (MLP), which was a clinical trial that evaluated screening for lung cancer in the 1970s. Mayo Lung Project cancer cases reported in Stage III or greater, according to the 1979 AJCC staging for lung cancer, were considered advanced stage. Our estimator distinguishes between the cases detected because of clinical symptoms and cases detected by screening. For nonsmall cell lung cancer cases detected in MLP, we estimate that the median primary tumor diameter at the onset of advanced stage disease was 4.1 cm. In addition, we estimate that the rate of patients symptomatically detected with their disease increases as their primary tumor increases in size, and for patients with a primary tumor of a given size, the rate of symptomatic detection is 12.8 times greater among patients with advanced stage disease compared to patients with early stage disease.