MISCAN-Lung (Erasmus) Erasmus University Medical Center

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: Kevin ten Haaf K.tenhaaf@erasmusmc.nl

Model Overview

The Microsimulation Screening Analysis (MISCAN)-Lung Model (Erasmus University Medical Center) is a microsimulation model that simulates a population of individual life histories, development of preclinical and clinical lung cancer, survival of clinically detected lung cancer, death due to lung cancer, and death due to other causes in the presence and absence of screening. For each individual, a smoking history is generated using the Smoking History Generator (SHG), which was built by utilizing data on smoking habits in the US population (1).

Lung cancer is modeled through a multistep procedure. Once a person’s age at death from causes other than lung cancer is generated by the SHG, which is influenced by the person’s smoking exposure characteristics, the Two-Stage Clonal Expansion (TSCE) model is used to determine whether lung cancer develops in that individual (2,3). MISCAN-Lung distinguishes four histological types of lung cancers: squamous cell carcinoma, adenocarcinoma (which consists of the types adenocarcinoma, large-cell carcinoma, and bronchioloalveolar carcinoma), other (remaining non-small cell carcinoma), and small-cell carcinoma. Once lung cancer has developed, it will progress from less advanced to more advanced preclinical stages until it is clinically detected (Figure 1). Lung cancers can be detected either clinically (due to symptoms) or by screening. The incidence of clinically detected lung cancers depends on the onset, sojourn times and the probability of clinical detection, which both vary by cancer stage and histology (and by gender for the sojourn times). If the screening component of the model has not been activated, lung cancers can only be clinically detected. If a person dies of lung cancer before dying from other causes, his or her age of death is adjusted accordingly.

Figure 1. Lung cancer progression in the MISCAN-Lung model

CISNET-DFCI Model: Natural History of Breast Cancer

Upon activating the screening component, properties of the screening test (such as the sensitivity and change in prognosis due to early detection) and screening policies (such as the starting age, stopping age, intervals between screening, requirements with regards to smoking, adherence to screening) can be specified. Preclinical lung cancers may be detected by screening, which may alter a person’s life history. Early detection by screening may cure a patient, allowing him/her to resume his/her normal (cancer free) life history. The probability of having such changes may differ by the stage of cancer at detection. MISCAN-Lung accounts for the effects of lead-time and over-diagnosis, i.e., detection of cancer due to screening which would have not been detected clinically during the person’s lifetime. Upon clinical or screen detection (without a history change) of lung cancer, the patient is assigned a histological type, stage- and gender-specific survival, which follows piecewise uniform distribution. The probability of curation of the disease (by stage) due to early detection by screening is based on data from the National Lung Screening Trial (NLST). The output of the MISCAN-Lung model consists of various simulated events in the presence and absence of screening, such as the number of diagnosed cases, the mortality due to the disease and other causes, and the number of life-years lost.

The MISCAN-Lung model has been used to evaluate the impact of tobacco control on US lung cancer mortality (4). Data from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) were used to calibrate MISCAN-Lung, from which information on the natural history and screen-detectability of lung cancer was derived (5-8). MISCAN-Lung contributed to the analyses of the CISNET Lung group in investigating the benefits and harms of 576 different lung cancer screening strategies (9). These analyses were used to inform the USPSTF for their recommendations for lung cancer screening (10). MISCAN-Lung was recently used to evaluate the cost-effectiveness of lung cancer screening in Ontario, Canada (11).

References

Jeon J, Meza R, Krapcho M, Clarke LD, Byrne J, Levy DT. Chapter 5: Actual and Counterfactual Smoking Prevalence Rates in the U.S. Population via Microsimulation. Risk Anal. 2012;32:S51-S68.
Meza R, Hazelton WD, Colditz GA, Moolgavkar SH. Analysis of lung cancer incidence in the nurses' health and the health professionals' follow-up studies using a multistage carcinogenesis model. Causes Control. 2008;19(3):317-328.
Hazelton WD, Jeon J, Meza R, Moolgavkar SH. Chapter 8: The FHCRC lung cancer model. Risk Anal. 2012;32 Suppl 1:S99-S116.
Schultz FW, Boer R, de Koning HJ. Chapter 7: Description of MISCAN-Lung, the Erasmus MC Lung Cancer Microsimulation Model for Evaluating Cancer Control Interventions. Risk Anal. 2012;32:S85-S98.
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.
Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409.
Oken MM, Hocking WG, Kvale PA, et al. Screening by chest radiograph and lung cancer mortality: The prostate, lung, colorectal, and ovarian (PLCO) randomized trial. JAMA. 2011;306(17):1865-1873.
ten Haaf K, van Rosmalen J, de Koning HJ. Lung cancer detectability by test, histology, stage and gender: estimates from the NLST and the PLCO trials. Cancer Epidemiol Biomarkers Prev. 2015;24(1):154-61.
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.
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.
ten Haaf K, Tammemägi MC, Bondy SJ, et al.Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada. PLoS Med. 2017;14(2):e1002225.