Lung Cancer

General information

The Cancer Intervention and Surveillance Modeling Network (CISNET) lung group develops and applies population models for lung cancer, quantifying the impact of tobacco control, low-dose computed tomography (LDCT) screening, early detection, and treatment interventions on lung cancer and all-cause mortality. Ten lung cancer natural history models have been developed independently by investigators at eight institutions:*

• BC Cancer (Lung Cancer Screening Model; BCCRI-LunCan)
• BC Cancer (Lung Cancer Smoking Model; BCCRI-Smoking)
• Erasmus Medical Center (MIcrosimulation SCreening ANalysis Lung Model; MISCAN-Lung)
• Georgetown University (Smoking-Lung Cancer Macro Model)
• Massachusetts General Hospital and Harvard Medical School (Lung Cancer Policy Model; LCPM)
• MD Anderson Cancer Center
• Mount Sinai
• OncoSim-Lung (CPAC-StatCan)
• Stanford University (Lung Cancer Outcomes Simulator; LCOS)
• Yale University (Yale Lung Cancer Model for population rates; YLCM)

Each CISNET Lung model includes a dose-response module that relates individual detailed smoking histories to lung cancer risk and outcomes. A central component of the CISNET Lung modeling is the smoking history generator (SHG), which simulates individual smoking histories based on the US population’s historical smoking patterns.^1,2 These individual smoking histories, combined with simulations of the age at death from causes other than lung cancer, serve as key inputs for the CISNET Lung models. Each model then uses these smoking histories to simulate lung cancer incidence and mortality individually and at the population level under various tobacco control³ or LDCT screening scenarios.^4-10

Data used by the models

The SHG was developed based on multiple data sources: National Health Interview Survey (NHIS), the Cancer Prevention Studies I & II (CPS-I & CPS-II), and the Human Mortality Database (HMD). The CISNET Lung models’ dose-response modules were developed using data from various prospective cohort studies, such as the CPS II11 and the Nurses’ Health Study (NHS) and the Health Professionals’ Follow-up Study (HPFS),^12,13 or a set of logistic regression models and tumor progression functions based on Surveillance, Epidemiology, and End Results (SEER) data.¹⁴

For the simulations of LDCT screening impact, each CISNET Lung model incorporated data from the two largest US lung cancer screening trials: the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the National Lung Screening Trial (NLST).^15,16 The data obtained from the PLCO and the NLST were used to derive information about the natural history of lung cancer by histology and the effectiveness of screening for lung cancer with chest radiography and LDCT.

Major contributions to public health policies

The CISNET Lung group has made a number of major contributions to knowledge about lung cancer, and its work has informed the development of prevention and control strategies against lung cancer. The group’s work reconstructing smoking patterns in the US by birth cohort, age, and calendar year have provided new insights into the impact of tobacco control policies on smoking prevalence in the US.^17,18 The group also showed the considerable impact of tobacco control on reducing lung cancer mortality and smoking-related mortality in the US since 1964.^3,19 Other projects include evaluating the health effects of raising the minimum age for purchasing tobacco products²⁰ and the development of a tobacco policy tool to project the impact of policies on smoking and health outcomes in the US.

The CISNET Lung group has also investigated the long-term benefits and harms of hundreds of population-level lung cancer screening strategies that vary the screening frequency and eligibility criteria.^4,6-10 These analyses were conducted in collaboration with the United States Preventive Services Task Force (USPSTF) and supported their revised lung cancer screening guidelines.^4,5,8 Current work is extending the models to consider the impact of duration-based screening strategies, the impact of biomarkers, and the impact of tobacco control and screening across US states.²¹

In-depth model information

Smoking dose-response models

The LCOS, MISCAN-Lung, Smoking-Lung Cancer Macro Model, BCCRI-LunCan, BCCRI-Smoking, and YLCM models incorporate versions of the Two-Stage Clonal Expansion (TSCE) model as their central smoking dose-response model.^11-13 The TSCE model is a biologically based mechanistic model, which models the effects of age and smoking on lung cancer development²² Although six CISNET Lung models used the TSCE model as a dose-response module, each group used it with a different parameterization. The LCPM model includes a probabilistic model (by histology) as a dose-response module. The Mount Sinai model uses logistic functions obtained from calibration as dose-response. The MD Anderson model uses the Bach, PLCOm2012, or the PLCO_ENGAGE risk models. The Oncosim model uses the PLCOall2014 model, and the BCCRI-LunCan can also use the PLCOm2012, Bach or LCRAT models as dose-response.

Histologic types

Lung cancer can be classified into several different histologic types. The LCOS model includes adenocarcinoma, large cell, squamous, and small cell lung carcinoma (SCLC). The LCPM model includes six lung cancer cell types: adenocarcinoma, bronchioloalveolar carcinoma (BAC), large cell, squamous, SCLC, and other. The MISCAN-Lung model incorporates the following types: adenocarcinoma/BAC, large cell, squamous, SCLC, and O-NSCLC. The BCCRI-LunCan and MD Anderson models includes adenocarcinoma/BAC, SQ, SCLC, and O-NSCLC. Mount Sinai includes AD, AIS, large cell, SQ, SCLC and other. Oncosim-Lung includes SCLC and NSCLC.

Stage progression

The LCOS, LCPM, and Mount Sinai models incorporate stage progression based on tumor volume and metastatic burden. The MISCAN-Lung, BCCRI-LunCan, and MD Anderson models incorporate Markov state-transition by histology as the stage progression model.

Screening sensitivity

The LCOS and Mount Sinai models incorporate sensitivity by size/histologic features, and the LCPM model by size and location in the lung (correlates with histologic features). The MISCAN-Lung and BCCRI-LunCan models incorporate stage/histology-specific sensitivities, while the MD Anderson model also considers nodule type and sex.

Screening effectiveness mechanism

The MISCAN-Lung model incorporates a cure model. The BCCRI-LunCan, Oncosim-Lung, and Mount Sinai models incorporate a stage shift model with adjustments for age of detection while the LCOS, MD Anderson and LCPM models do not incorporate a stage shift model.

* These models were denoted as follows in previous publications:

• Model E (MISCAN-Lung, Erasmus Medical Center)
• Model S (LCOS, Stanford University)
• Model M (LCPM, Massachusetts General Hospital)
• Model U (BCCRI-LunCan; formerly UM-LCSc, University of Michigan)

References

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2. Holford TR, Levy DT, McKay LA, et al. Patterns of Birth Cohort–Specific Smoking Histories, 1965–2009. Am J Prev Med. 2014;46(2):e31-e37.
3. 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.
4. de Koning HJ, Meza R, Plevritis SK, et al. Benefits and harms of CT lung cancer screening programs for high risk populations. AHRQ Publication No. 13-05196-EF-2. Rockville, MD: Agency for Healthcare Research and Quality; 2013.
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