MoUnt Sinai LUng Cancer Model (MULU) Icahn School of Medicine at Mount Sinai

Overview

The MoUnt Sinai LUng Cancer Model (MULU) builds on our previous work.^1-12 It is a state-transition microsimulation model that simulates the development, progression, detection, screening, treatment, and survival of individuals with lung cancer (LC). MULU incorporates smoking patterns using the Smoking History Generator (SHG) developed by CISNET.¹³ A schematic of the model is shown in Figure 1.

Figure 1. Lung cancer progression, diagnosis, and treatment in MULU

The simulation begins with a population of disease-free individuals who transition through various health states based on monthly probabilities. During each monthly cycle, individuals may develop benign or malignant pulmonary nodules. Depending on growth rates, likelihood of progression, and competing risks of death, a malignant nodule may represent either: 1) an indolent LC that would not have manifested clinically without screening (i.e., slow-growing cancer) or 2) a clinically significant cancer.

Pulmonary nodules may be detected through symptoms, screening, or incidentally. Nodules identified via screening are evaluated according to Lung-RADS v2022,¹⁴ while non-screen-detected nodules are followed up based on Fleischner Society guidelines.¹⁵ In the model, malignant nodules can fall into one of five major histological subtypes: adenocarcinoma (including adenocarcinoma in situ), large-cell carcinoma, squamous cell carcinoma, other non-small cell lung cancers, and small-cell carcinoma. Existing cancers can grow over time and may spread to lymph nodes or develop distant metastases. To estimate cancer risk from radiation exposure due to low-dose CT (LDCT) and other imaging tests, we incorporated risk equations from the Biologic Effects of Ionizing Radiation VII (BEIR VII) report into MULU.¹⁶ The cumulative radiation dose is then used to estimate the number of radiation-induced cancers and related deaths.^17,18

To estimate the probability of complications from diagnostic evaluations related to lung cancer screening, we used data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.¹⁹ We identified participants who completed baseline questionnaires that included risk factors and health history and who were eligible for screening under the 2021 USPSTF lung cancer screening recommendations. We then identified individuals who underwent one or more diagnostic procedures, defined as surgical biopsy (including thoracotomy, thoracoscopy, and resection), needle biopsy (including thoracentesis), bronchoscopy (with or without biopsy), mediastinoscopy, or other procedures. Complications were categorized by severity (major, intermediate, and minor) and by comorbidity status. We defined complications as those occurring within 14 days of needle biopsy or bronchoscopy and within 60 days of mediastinoscopy or surgical biopsy.

The detailed anatomical tracking in MULU allows for cancer staging according to the current AJCC criteria.²⁰ Once staged, each simulated patient is mapped to an appropriate treatment strategy based on current NCCN guidelines.^17,18 These treatments include lobectomy, limited resection (wedge or segmentectomy), standard radiotherapy (RT), stereotactic body radiotherapy (SBRT), chemotherapy, chemoradiotherapy (chemoRT), targeted therapy (e.g., tyrosine kinase inhibitors), and immunotherapy (e.g., checkpoint inhibitors). Each treatment is associated with a set of input parameters derived from look-up tables that specify the probability of treatment effectiveness based on data from randomized controlled trials (RCTs).^22-27 The treatment module also tracks treatment-related complications.

Lung cancer-specific mortality in MULU is a result of lung cancer progression and/or recurrence. Competing risks are modeled with age, sex, race, smoking, and comorbidity-related subhazards that determine the monthly probability of non-lung cancer death. MULU was recently used to estimate the benefits and harms of lung cancer screening in individuals with comorbidities.¹⁹

References

1. Chen Y, Watson TR, Criss SD, et al. A simulation study of the effect of lung cancer screening in China, Japan, Singapore, and South Korea. PLoS One. 2019;14(7):e0220610.
2. Criss SD, Cao P, Bastani M, et al. Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study. Ann Intern Med. 2019;171(11):796-804.
3. Criss SD, Mooradian MJ, Sheehan DF, et al. Cost-effectiveness and Budgetary Consequence Analysis of Durvalumab Consolidation Therapy vs No Consolidation Therapy After Chemoradiotherapy in Stage III Non-Small Cell Lung Cancer in the Context of the US Health Care System. JAMA Oncol. 2019;5(3):358-365.
4. Criss SD, Mooradian MJ, Watson TR, Gainor JF, Reynolds KL, Kong CY. Cost-effectiveness of Atezolizumab Combination Therapy for First-Line Treatment of Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States. JAMA Netw Open. 2019;2(9):e1911952.
5. Criss SD, Sheehan DF, Palazzo L, Kong CY. Population impact of lung cancer screening in the United States: Projections from a microsimulation model. PLoS Med. 2018;15(2):e1002506.
6. Hammer MM, Palazzo LL, Eckel AL, Barbosa EM, Jr., Kong CY. A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules. Radiology. 2019;290(2):506-513.
7. Hammer MM, Palazzo LL, Paquette A, et al. Cost-Effectiveness of Follow-Up for Subsolid Pulmonary Nodules in High-Risk Patients. J Thorac Oncol. 2020;15(8):1298-1305.
8. Jeon J, Holford TR, Levy DT, et al. Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach. Ann Intern Med. 2018;169(10):684-693.
9. Kong CY, Sheehan DF, McMahon PM, Gazelle GS, Pandharipande P. Combined Biomarker and Computed Tomography Screening Strategies for Lung Cancer: Projections of Health and Economic Tradeoffs in the US Population. MDM Policy Pract. 2016;1(1).
10. Kong CY, Sigel K, Criss SD, et al. Benefits and harms of lung cancer screening in HIV-infected individuals with CD4+ cell count at least 500 cells/mul. AIDS. 2018;32(10):1333-1342.
11. 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.
12. Tramontano AC, Sheehan DF, McMahon PM, et al. 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. 2016;6(2):e010227.
13. 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 analysis : an official publication of the Society for Risk Analysis. 2012;32 Suppl 1(Suppl 1):S51-68.
14. Christensen J, Prosper AE, Wu CC, et al. ACR Lung-RADS v2022: Assessment Categories and Management Recommendations. Chest. 2024;165(3):738-753.
15. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017;284(1):228-243.
16. National Research C, Committee to Assess Health Risks from Exposure to Low Level of Ionizing R. Health risks from exposure to low levels of ionizing radiation : BEIR VII Phase 2. Washington, D.C.: National Academies Press; 2006.
17. Kong CY, Lee JM, McMahon PM, et al. Using radiation risk models in cancer screening simulations: important assumptions and effects on outcome projections. Radiology. 2012;262(3):977-984.
18. 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.
19. Kale MS, Sigel K, Arora A, Ferket BS, Wisnivesky J, Kong CY. The Benefits and Harms of Lung Cancer Screening in Individuals With Comorbidities. JTO Clin Res Rep. 2024;5(3):100635.
20. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016;11(1):39-51.
21. Ettinger DS, Aisner DL, Wood DE, et al. NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 5.2018. J Natl Compr Canc Netw. 2018;16(7):807-821.
22. Ginsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. The Annals of thoracic surgery. 1995;60(3):615-622; discussion 622-613.
23. Pignon JP, Tribodet H, Scagliotti GV, et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J Clin Oncol. 2008;26(21):3552-3559.
24. Shultz DB, Diehn M, Loo BW, Jr. To SABR or not to SABR? Indications and contraindications for stereotactic ablative radiotherapy in the treatment of early-stage, oligometastatic, or oligoprogressive non-small cell lung cancer. Semin Radiat Oncol. 2015;25(2):78-86.
25. Tandberg DJ, Tong BC, Ackerson BG, Kelsey CR. Surgery versus stereotactic body radiation therapy for stage I non-small cell lung cancer: A comprehensive review. Cancer. 2018;124(4):667-678.
26. Waller D, Peake MD, Stephens RJ, et al. Chemotherapy for patients with non-small cell lung cancer: the surgical setting of the Big Lung Trial. European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery. 2004;26(1):173-182.
27. Winton T, Livingston R, Johnson D, et al. Vinorelbine plus cisplatin vs. observation in resected non-small-cell lung cancer. N Engl J Med. 2005;352(25):2589-2597.