The Lung Cancer Policy Model (LCPM; Massachusetts General Hospital) was originally developed to evaluate effectiveness and cost-effectiveness of imaging-based lung cancer screening programs. Because of pervasive concerns that helical computed tomography (CT) examinations would detect many small pulmonary nodules followed by prompt invasive workups for benign or indolent disease, we explicitly model benign nodules, multiple lung cancer histologies, and detailed clinical events. Over time, new features and components have been added to the model to enable projections of lung cancer trends in the US and to evaluate treatments’ effects on lung cancer risk.
The LCPM is a microsimulation model of lung cancer development, growth, progression, detection, and survival. Individuals can develop multiple lung cancers of different histologic types. A 'true' disease stage is assigned based on the individual's simulated disease characteristics (tumor size, nodal involvement, distant spread), and it is updated every cycle (month). An observed disease stage is also assigned, based on the individual's true disease state and the results from diagnostic or staging tests. Detection of a pulmonary nodule that is suspicious for lung cancer may be prompted by symptoms, incidental detection during a thoracic imaging examination for reasons unrelated to lung cancer (e.g., trauma), or a screening examination.
The LCPM simulates benign pulmonary nodules because they are not always distinguishable from lung cancers on imaging exams and may prompt follow-ups, despite potential attendant risks and costs. Since the LCPM simulates detailed clinical events including specific staging examinations and treatment modalities, the model can be used to compare a wide range of patient management strategies. Therefore, it can be used to evaluate the full spectrum of cancer control from prevention (smoking cessation) to screening and treatment.
Inputs for the LCPM are described in detail in the Model Profile and prior publications (1-8). Key inputs include those that characterize the population being simulated (e.g., birth years, smoking histories, other-cause mortality risks) and those that specify the scenario (e.g., test characteristics, screening program characteristics, response rates for treatments). Parameters governing unobservable events in the development and progression of lung cancer (e.g., metastasis) were estimated by calibrating the LCPM predictions against observed outcomes based on tumor registries (the NCI’s Surveillance, Epidemiology, and End Results [SEER]) and clinical trial data (4,5).
Individual-level outputs include demographics (age at death, smoking history) and lung cancer outcomes (age and stage at diagnosis, location in the lung, histologic type, treatment provided, and survival). By aggregating individual outcomes for a birth cohort, the LCPM can predict rates of events such as iatrogenic deaths attributable to a screening program. By simulating multiple birth cohorts, the LCPM can predict population-level outputs including lung cancer incidence and mortality and number of lung cancer deaths.
References
- Moolgavkar SH, Holford TR, Levy DT, et al. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst. 2012;104(7):541-548.
- de Koning HJ, Meza R, Plevritis SK, et al. Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force. Ann Intern Med. 2014;160(5):311-320.
- McMahon PM, Meza R, Plevritis SK, et al. Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling. PLoS ONE. 2014;9(6):e99978.
- Meza R, ten Haaf K, Kong CY, et al. Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials. Cancer. 2014;120(11):1713- 1724.
- Kong CY, McMahon PM, Gazelle GS. Calibration of disease simulation model using an engineering approach. Value Health. 2009;12(4):521-529.
- McMahon PM, Kong CY, Bouzan C, et al. Cost-effectiveness of computed tomography screening for lung cancer in the United States. J Thorac Oncol. 2011;6(11):1841-1848.
- Tramontano AC, Sheehan DF, McMahon PM, Dowling EC, Holford TR, Ryczak K, Lesko SM, Levy DT, Kong CY. Evaluating the impacts of screening and smoking cessation programmes on lung cancer in a high-burden region of the USA: a simulation modelling study. BMJ open. Feb 29 2016;6(2):e010227.
- Sheehan DF, Criss SD, Gazelle GS, Pandharipande PV, Kong CY. Evaluating lung cancer screening in China: Implications for eligibility criteria design from a microsimulation modeling approach. PloS one. 2017;12(3):e0173119
