Browsing by Author "Goldwasser, Deborah L."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Parameter estimation in mathematical models of lung cancer(2010) Goldwasser, Deborah L.; Kimmel, MarekThe goal of this thesis is to improve upon existing mathematical models of lung cancer that inform policy decisions related to lung cancer screening. Construction of stochastic, population-based models of lung cancer relies upon careful statistical estimation of biological parameters from diverse data sources. In this thesis, we focus specifically on two distinct aspects of parameter estimation. First, we propose a model-based framework to estimate lung cancer risk due to repeated low-dose radiation exposures using the two-stage clonal expansion (TSCE) model. We incorporate the TSCE model into a Bayesian framework and formulate a likelihood function for randomized screening data. The likelihood function depends on model-based risk correlates and effectively penalizes parameter values that correspond to model-based contradictions. The net result is that both the sensitivity and specificity of parameter estimation relating to excess lung cancer risk is increased. This methodology is applied to data from the Mayo Lung Project and estimates of 10-year excess lung cancer risk as a function of age at enrollment and number of screens are derived. Second, we describe a new statistical approach aimed at improving our understanding of the natural course of lung cancer. Specifically, we are interested in evaluating the evidence for, or against, the hi-modal hypothesis which proposes that lung cancers are of two categories, either slow-growing and non-invasive cancers (tending to over-diagnosis) or rapidly-growing and highly aggressive. We represent the growth trajectory of lung tumors using the evolutionary parameters of cancer stern cell branching fraction (f) and cell mutation rate (mu). While concern over widespread implementation of lung cancer screening has focused primarily on the extent of over-diagnosis, these results are consistent with the presence of a high percentage of rapidly-growing, aggressive cancers.Item Small median tumor diameter at cure threshold (<20 mm) among aggressive non-small cell lung cancers in male smokers predicts both chest X-ray and CT screening outcomes in a novel simulation framework(UICC, 2013) Goldwasser, Deborah L.; Kimmel, MarekThe effectiveness of population-wide lung cancer screening strategies depends on the underlying natural course of lung cancer. We evaluate the expected stage distribution in the Mayo CT screening study under an existing simulation model of non-small cell lung cancer (NSCLC) progression calibrated to the Mayo lung project (MLP). Within a likelihood framework, we evaluate whether the probability of 5-year NSCLC survival conditional on tumor diameter at detection depends significantly on screening detection modality, namely chest X-ray and computed tomography. We describe a novel simulation framework in which tumor progression depends on cellular proliferation and mutation within a stem cell compartment of the tumor. We fit this model to randomized trial data from the MLP and produce estimates of the median radiologic size at the cure threshold. We examine the goodness of model fit with respect to radiologic tumor size and 5-year NSCLC survival among incident cancers in both the MLP and Mayo CT studies. An existing model of NSCLC progression under-predicts the number of advanced-stage incident NSCLCs among males in the Mayo CT study (p-value = 0.004). The probability of 5-year NSCLC survival conditional on tumor diameter depends significantly on detection modality (p-value = 0.0312). In our new model, selected solution sets having a median tumor diameter of 16.2ヨ22.1 mm at cure threshold among aggressive NSCLCs predict both MLP and Mayo CT outcomes. We conclude that the median lung tumor diameter at cure threshold among aggressive NSCLCs in male smokers may be small (<20 mm).