Longitudinal Multistage Observation Model
The Longitudinal Multistage Observation LMO Model (Fred Hutchinson Cancer Research Center) was developed to estimate the impact of alternative screening protocols to reduce lung cancer mortality in the US. This longitudinal multistage observation model of lung cancer development and detection includes six pathways representing distinct histologic subtypes: bronchioloalveolar, adenocarcinoma, large cell, squamous, other non-small cell, and small cell. Using maximum-likelihood methods, the LMO Model was calibrated to individual-level smoking histories and longitudinal outcomes data in the National Lung Screening Trial (NLST), including separate pathways representing the six histologic subtypes. Parameters for each pathway were estimated separately by gender. After calibrating the model, it was validated based on independent data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial.
The LMO Model assumes that each histologic subtype of lung cancer may respond differently to cigarette smoke (Figure 1). Normal pulmonary stem cells may undergo two pathway-specific sequential mutational or epigenetic events to generate a premalignant cell. Premalignant cells may divide and undergo apoptosis (programmed cell death) or differentiation, leading to clonal expansion, or frequently to extinction of the clone due to the stochastic nature of the clonal expansion process where cell division and death events are assumed to occur at random. Premalignant cells in the developing clone may also mutate to generate a malignant cell. Malignant cells also divide and die, typically at faster rates, causing increased rates of clonal expansion. If the malignant clone does not become extinct, it may grow large enough to be detected through screening or other observation processes. The model estimates a rate for malignant cells to become metastatic, and it estimates clonal expansion rates of metastatic cells.
Figure 1 Illustration of the LMO model
The LMO Model employed stochastic observation processes for possible longitudinal outcomes, which include negative screening tests, screen-based detection and diagnosis or symptomatic diagnosis, and subsequent outcomes such as cure, lung cancer death, censoring, or other-cause death. Each individual outcome, such as a negative or positive screening test, conditions the probability for subsequent outcomes. Thus, for example, a negative CT screening test indicates that the multistage process does not include large tumors at the time of screening, so the probability of symptomatic detection immediately following the test is low. However, the observation probability increases with time as small tumors not detected by the screen may grow larger.
The probability of a positive outcome from an observation depends on the tumor size and the sensitivity of the observation. We used likelihood methods to estimate all of the cell kinetic rates in the LMO, assuming separate sensitivities for tumor detection by low-dose helical computed tomography (CT) and chest X-ray (CXR). If a cancer is not detected through screening, it may grow to a larger size that causes symptomatic detection, modeled as an observation process with lower sensitivity. Calibration to the stage distribution at diagnosis and subsequent survival was based on malignant tumor size and the contribution from metastatic cells. After diagnosis, the cancer may be cured with different probability depending on histology. If the cancer is not cured, it may undergo further growth and metastatic spread, ultimately causing death. The LMO Model also allows for the possibility of overdiagnosis due to growth of indolent cancers, which may be detected through screening but are unlikely to undergo further growth that would lead to symptomatic diagnosis or death (1–4).
Tip: Hover your cursor over the dashed attribute links below for more information. View the details of this model in a grid with other lung models.