title: Lung cancer growth dynamics.
abstract: Mathematical modeling furnishes valuable and otherwise unattainable insights, some counterintuitive, into the natural history of lung cancer. We modeled lung cancer growth dynamics to show that: (1) early diagnosis of lethal lung cancer by means of radiographic or CT screening is an unattainable goal. (2) At a given dimension and constant tumor volume doubling time, the rate of diameter increase is an exponential function (base 1.26) of the number of tumor volume doublings, and the rate of increase in volume, a quadratic function of the radius. (3) This methodology delineates the magnitude of diameter increase in small nodules required to discern volume growth. (4) Under the assumption of a high degree of compliance with sequential screens in large-scale trials, mean tumor volume doubling time can be estimated from the prevalence:incidence ratio. For example, in the Mayo Clinic computerized tomography trial, stage IA tumor volume doubling time was 230 days.
authors.affiliation: Thoracic Oncology Program, Earle A Chiles Research Center, Department of Mathematics and Statistics, Portland State University, Portland, OR 97225-7007, United States. Reichje@dnamail.com
authors.forename: Jerome M
authors.initials: JM
authors.lastname: Reich
authors.forename: Jong-Sung
authors.initials: JS
authors.lastname: Kim
mesh_terms: D049108:Cell Enlargement; D003198:Computer Simulation; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008954:Models, Biological; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*