MISCAN-Lung (Erasmus)

MIcrosimulation SCreening Analysis (MISCAN) Lung Model

Basic Model Attributes
Cancer site	Lung
Host institution	Erasmus University Medical Center
Purpose	The MISCAN-lung model was designed to simulate population trends in lung cancer (LC) for comprehensive surveillance of the disease, to relate past exposure to risk factors to (observed) LC incidence and mortality, and to estimate the impact of cancer-control interventions. MISCAN-lung employs the technique of stochastic microsimulation of life histories affected by risk factors. It includes the two-stage clonal expansion model for carcinogenesis and a detailed LC progression model; the latter is specifically intended for the evaluation of screenings. (ten Haaf, 2015, Cancer Epidemiology, Biomarkers & Prevention).
Contact	Kevin ten Haaf (K.tenhaaf@erasmusmc.nl)
Profile	CISNET_ModelProfile_LUNG_ERASMUS_001_01132012_83607.pdf
Website	http://www.erasmusmc.nl/mgz/

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

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. Cancer 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.

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.

Detailed Package Attributes
Attribute Category	Attribute
Approach
Primary Purpose	Screening evaluation, Epidemiological analysis, Policy evaluation, Population trends,
Features
Intervention	Prevention, Screening, Treatment,
Natural History	Metastases (We model the American Joint Commission on Cancer (AJCC) 6th edition stage criteria (which incorporate tumor growth and metastases)), Tumor Growth (We model the American Joint Commission on Cancer (AJCC) 6th edition stage criteria (which incorporate tumor growth and metastases)),
Construction
Approach	Micro Simulation,
Methods	Likelihood optimization (Model fit was optimized using the Nelder-Mead optimization algorithm), Stochastic process, Time to Event,
Unit of Analysis	Tumor, Person (We generate person-level data, which can be used to compute (sub-) populations), Population (We generate person-level data, which can be used to compute (sub-) populations),
Data Source	NHS (The two-stage clonal expansion model is based on Nurses' Health Studies (NHS) and Health Professionals' Follow-up Study (HPFS) estimates (Meza, 2008, Cancer Causes Control)), HPFS (The two-stage clonal expansion model is based on Nurses' Health Studies (NHS) and Health Professionals' Follow-up Study (HPFS) estimates (Meza, 2008, Cancer Causes Control)),
Census
Cancer Registry	SEER,
Linked
Clinical Trial	PLCO, NLST,
Survey	NHIS (Used indirectly, as these were used to develop the Smoking History Generator used by our model),
Meta Analysis
Assumptions
Benefit Factors
Screening	Size at diagnosis (We do not specifically model size at diagnosis, but model the American Joint Commission on Cancer (AJCC) 6th edition stages. The benefit is based on the detection at a specific stage, through a stage specific cure rate), Cure Rate (We do not specifically model size at diagnosis, but model the American Joint Commission on Cancer (AJCC) 6th edition stages. The benefit is based on the detection at a specific stage, through a stage specific cure rate), Modality,
Treatment
Vaccination
Inputs
Screening	Attendance, Dissemination, Effect (Screening effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
Diagnosis
Precancer
Treatment	Efficacy, Effect (Treatment effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
Precancer
Survival	Observed, Relative,
Mortality	Other cause, Disease-specific, Tumor Attributes, Smoking History,
Risk Factor	Smoking, Demography, Natural History,
Vaccination
Outputs	Incidence,
Disease	Stage Distribution,
Prevalence
Treatment	Effect (Treatment effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc),
Precancer
Screening	Effect (Screening effects were estimated from data (output) and later given as an input, however the final effect (output) depends on the characteristics of the population, attendance etc), Tumor Attributes,
Risk Factor	Smoking (Incorporates the NCI's Smoking History Generator), Demography, Natural History,
Outcomes	Survival, Life years, Cause-specific Mortality, All-cause Mortality,
Screening	False Positives (Indirectly considered), True Positives, False Negatives (Indirectly considered), True Negatives (Indirectly considered), Biopsies performed (Indirectly considered), Unnecessary biopsies (Indirectly considered), Overdiagnoses, History,
Treatment
Implementation
Development
Tested Platforms	Windows,
Language	Delphi,

2023

Meza R, Cao P, de Nijs K, Jeon J, Smith RA, Ten Haaf K, de Koning H, Assessing the impact of increasing lung screening eligibility by relaxing the maximum years-since-quit threshold. A simulation modeling study., Cancer, Nov. 1, 2023 [Abstract]
Toumazis I, Cao P, de Nijs K, Bastani M, Munshi V, Hemmati M, Ten Haaf K, Jeon J, Tammemägi M, Gazelle GS, et al., Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis., Ann Intern Med, March 1, 2023 [Abstract]

2022

Aredo JV, Choi E, Ding VY, Tammemägi MC, Ten Haaf K, Luo SJ, Freedman ND, Wilkens LR, Le Marchand L, Wakelee HA, et al., Racial and Ethnic Disparities in Lung Cancer Screening by the 2021 USPSTF Guidelines Versus Risk-Based Criteria: The Multiethnic Cohort Study., JNCI Cancer Spectr, May 2, 2022 [Abstract]

2021

Toumazis I, de Nijs K, Cao P, Bastani M, Munshi V, Ten Haaf K, Jeon J, Gazelle GS, Feuer EJ, de Koning HJ, et al., Cost-effectiveness Evaluation of the 2021 US Preventive Services Task Force Recommendation for Lung Cancer Screening., JAMA Oncol, Dec. 1, 2021 [Abstract]
Taksler GB, Peterse EFP, Willems I, Ten Haaf K, Jansen EEL, de Kok IMCM, van Ravesteyn NT, de Koning HJ, Lansdorp-Vogelaar I, Modeling Strategies to Optimize Cancer Screening in USPTSF Guideline-Noncompliant Women., JAMA Oncol, April 29, 2021 [Abstract]
Meza R, Jeon J, Toumazis I, Ten Haaf K, Cao P, Bastani M, Han SS, Blom EF, Jonas DE, Feuer EJ, et al., Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force., JAMA, March 9, 2021 [Abstract]

2020

Blom EF, Haaf KT, de Koning HJ, Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer., Pharmacoeconomics, Aug. 5, 2020 [Abstract]
Blom EF, Ten Haaf K, Arenberg DA, de Koning HJ, Uptake of minimally invasive surgery and stereotactic body radiation therapy for early stage non-small cell lung cancer in the USA: an ecological study of secular trends using the National Cancer Database., BMJ Open Respir Res, May 1, 2020 [Abstract]
Blom EF, Ten Haaf K, de Koning HJ, Trends in lung cancer risk and screening eligibility affect overdiagnosis estimates., Lung Cancer, Jan. 1, 2020 [Abstract]

2019

Criss SD, Cao P, Bastani M, Ten Haaf K, Chen Y, Sheehan DF, Blom EF, Toumazis I, Jeon J, de Koning HJ, et al., Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study., Ann Intern Med, Dec. 3, 2019 [Abstract]
Blom EF, Ten Haaf K, Arenberg DA, de Koning HJ, Disparities in Receiving Guideline-Concordant Treatment for Lung Cancer in the United States, Ann Am Thorac Soc, Nov. 1, 2019 [Abstract]
Ten Haaf K, Bastani M, Cao P, Jeon J, Toumazis I, Han SS, Plevritis SK, Blom EF, Kong CY, Tammemägi MC, Feuer EJ, et al., A comparative modeling analysis of risk-based lung cancer screening strategies, J Natl Cancer Inst, Sept. 30, 2019 [Abstract]
Tammemägi MC, Ten Haaf K, Toumazis I, Kong CY, Han SS, Jeon J, Commins J, Riley T, Meza R, Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial, JAMA Netw Open, March 1, 2019 [Abstract]
Blom EF, Ten Haaf K, Arenberg DA, de Koning HJ, Treatment capacity required for full-scale implementation of lung cancer screening in the United States, Cancer, Feb. 27, 2019 [Abstract]

2018

Han SS, Ten Haaf K, Hazelton WD, Jeon J, Meza R, Kong CY, Feuer EJ, de Koning HJ, Plevritis SK, Re: Think before you leap, Int J Cancer, April 1, 2018 [Abstract]

2017

Han SS, Ten Haaf K, Hazelton WD, Munshi VN, Jeon J, Erdogan SA, Johanson C, McMahon PM, Meza R, Kong CY, Feuer EJ, et al., The impact of overdiagnosis on the selection of efficient lung cancer screening strategies, Int J Cancer, June 1, 2017 [Abstract]
Ten Haaf K, Jeon J, Tammemägi MC, Han SS, Kong CY, Plevritis SK, Feuer EJ, de Koning HJ, Steyerberg EW, Meza R, Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study, PLoS Med, April 1, 2017 [Abstract]
Ten Haaf K, Tammemägi MC, Bondy SJ, van der Aalst CM, Gu S, McGregor SE, Nicholas G, de Koning HJ, Paszat LF, 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, Feb. 1, 2017 [Abstract]

2016

ten Haaf K, de Koning HJ, Clarifying Assumptions and Outcomes in Cost-effectiveness Analyses, JAMA Oncol, Feb. 1, 2016 [Abstract]

2015

Ten Haaf K, de Koning HJ, Overdiagnosis in lung cancer screening: why modelling is essential, J Epidemiol Community Health, Nov. 1, 2015 [Abstract]
Ten Haaf K, de Koning HJ, Should Never-Smokers at Increased Risk for Lung Cancer Be Screened?, J Thorac Oncol, Sept. 1, 2015 [Abstract]
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, Jan. 1, 2015 [Abstract]

2014

de Koning HJ, Meza R, Plevritis SK, Raising the bar for the U.S. Preventive Services Task Force, Ann Intern Med, Oct. 7, 2014 [Abstract]
Meza R, ten Haaf K, Kong CY, Erdogan A, Black WC, Tammemagi MC, Choi SE, Jeon J, Han SS, Munshi V, van Rosmalen J, et al., Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials, Cancer, June 1, 2014 [Abstract]
de Koning HJ, Meza R, Plevritis SK, ten Haaf K, Munshi VN, Jeon J, Erdogan SA, Kong CY, Han SS, van Rosmalen J, Choi SE, 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, March 4, 2014 [Abstract]
McMahon PM, Meza R, Plevritis SK, Black WC, Tammemagi CM, Erdogan A, ten Haaf K, Hazelton W, Holford TR, Jeon J, Clarke L, 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, Jan. 1, 2014 [Abstract]

2012

Bloch M, Backinger CL, Compton WM, Conway K, Standing on the threshold of change, Risk Anal, July 1, 2012 [Abstract]
Anderson CM, Burns DM, Dodd KW, Feuer EJ, Chapter 2: Birth-cohort-specific estimates of smoking behaviors for the U.S. population, Risk Anal, July 1, 2012 [Abstract]
Boer R, Moolgavkar SH, Levy DT, Chapter 15: Impact of tobacco control on lung cancer mortality in the United States over the period 1975-2000--summary and limitations, Risk Anal, July 1, 2012 [Abstract]
Rosenberg MA, Feuer EJ, Yu B, Sun J, Henley SJ, Shanks TG, Anderson CM, McMahon PM, Thun MJ, Burns DM, Chapter 3: Cohort life tables by smoking status, removing lung cancer as a cause of death, Risk Anal, July 1, 2012 [Abstract]
Holford TR, Clark L, Chapter 4: Development of the counterfactual smoking histories used to assess the effects of tobacco control, Risk Anal, July 1, 2012 [Abstract]
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, July 1, 2012 [Abstract]
Feuer EJ, Levy DT, McCarthy WJ, Chapter 1:The impact of the reduction in tobacco smoking on U.S. lung cancer mortality, 1975-2000: an introduction to the problem, Risk Anal, July 1, 2012 [Abstract]
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, July 1, 2012 [Abstract]
Moolgavkar SH, Holford TR, Levy DT, Kong CY, Foy M, Clarke L, Jeon J, Hazelton WD, Meza R, Schultz F, McCarthy W, et al., Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000, J Natl Cancer Inst, April 4, 2012 [Abstract]

2011

Shi L, Tian H, McCarthy WJ, Berman B, Wu S, Boer R, Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project, BMC Cancer, March 7, 2011 [Abstract]