Longitudinal Multistage Observation Model

Basic Model Attributes
Cancer siteLung
Host institution Fred Hutchinson Cancer Research Center
Purpose To provide a mechanistic mathematical model of lung cancer development by histology for estimating the impact of alternative screening protocols to reduce lung cancer mortality in the US. The longitudinal multistage observation (LMO) model of lung cancer development and detection includes six pathways representing distinct histological subtypes (bronchioloalveolar, adenocarcinoma, large cell, squamous, other non-small cell, and small cell). On each pathway, the model represents five stages that may occur during progression from normal stem cells to malignant, then metastatic cells. The model assumes normal stem cells in the bronchus and lung may undergo at random two successive mutations to form a premalignant cell of a specific histological type. Premalignant cells may undergo clonal expansion or extinction through cell division and death (apoptosis), with a non-linear dose-response relationship accounting for the effects of cigarette smoking on the clonal expansion rate. The premalignant cells may undergo further mutation to become malignant cells. Maligant cells also undergo clonal expansion through division and apoptosis at rates that are generally faster than for premalignant cells. Malignant clones may be detected before metastasis through a stochastic (size based) observation process, or through further mutation events may generate metastatic cells, which also undergo clonal expansion and possibly observation. The stochastic observation processes relate the size of the malignant tumor to the probability of detection and stage, with different size-based sensitivities for X-ray screening, computerized tomography (CT) screening, symptomatic detection, and death from lung cancer. The model was fit to individual longitudinal data including lung cancer screening, incidence, and death outcomes from the National Lung Screening Trial (NLST) and the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trials. The probability for successive (longitudinal) events depends on prior exposures and observation outcomes. Maximum likelihood methods were used to estimate the mutation, division, death, and observation parameters for each histological subtype. The model was then applied to simulated current, former, and never smokers generated by a Smoking Simulator program provided by the National Cancer Institute to estimate the effects of alternative CT screening protocols in the US population.
Contact William Hazelton (hazelton@fhcrc.org)
Propose changes to this package