Andrea Borondy-Kitts, MS, MPH
Many in the lung cancer field have been waiting for a definitive prospective trial to confirm results from retrospective studies that show lung cancer risk prediction tools do a better job in the selection of individuals for lung cancer screening compared to categorical criteria alone, such as age, smoking history, and years since quitting.1
That day has finally arrived.
The Tammemagi et al. publication of the interim results from the International Lung Screening Trial (ILST), comparing the PLCOm2012 model to the 2013 US Preventive Services Task Force (USPSTF) selection criteria show the model does a better job in selecting individuals for lung cancer screening compared to categorical criteria.2
The PLCOm2012 tool is a validated lung cancer risk prediction model based on data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO).3
Risk prediction models include individualized variables known to increase the risk for lung cancer in addition to the categorical variables of age and smoking history. The PLCOm2012 model variables include age, race/ethnicity, education level attained, body mass index, personal history of having a smoking-related cancer, family history of lung cancer, and personal history of having COPD or emphysema. The additional variables help to estimate the risk of lung cancer beyond determining if an individual is eligible for screening.
Tammemagi and colleagues compared PLCOm2012 risk-based eligibility to the USPSTF 2013 categorical eligibility criteria. At the USPSTF equivalent 6-year risk threshold of 1.7% or greater, the model-eligible sample resulted in 15.8% more lung cancers detected for the same number of individuals screened. In addition, more women with lung cancer were identified by the model. Ninety-eight women were diagnosed with lung cancer in the study sample. Of those, only 72 qualified for screening using the USPSTF criteria compared to 94 who qualified for screening using the PLCOm2012 model.
This illustrates one of the shortfalls of the USPSTF 2013 criteria—disparity in eligibility. This has been shown for Black individuals as compared to white individuals4 and for women as compared to men.5
In 2021, the USPSTF did update their lung cancer screening recommendations. The recommendations now include younger individuals, ages 50 to 80 versus the 2013 criteria of 55 to 80 years old. The USPSTF also reduced the smoking history eligibility from a minimum of 30 pack years of smoking to a minimum of 20 pack years.6 However, the criteria regarding length of time since someone quit smoking has not changed. Individuals still need to have been smoke free for 15 years or less to be eligible for screening.
These changes have helped to reduce the screening eligibility disparity for Black individuals and women compared to the 2013 criteria.4,5,7 However, a comparison of the PLCOm2012 model to the 2021 USPSTF criteria continues to show improved sensitivity using the risk model for screening selection.5,7
“It is time for institutions and jurisdictions to use the PLCOm2012 or a comparably validated risk prediction model for selection of individuals for lung cancer screening in addition to, or in-place of, categorial criteria to maximize the benefits of screening for all individuals at high risk of lung cancer.”
—Andrea Borondy Kitts, MS, MPH, Rescue Lung Society
Using risk prediction models improves identification of individuals who are still at high risk of lung cancer despite being smoke free for 15 years or more. In one cohort study, 10% to 15% of people with lung cancer who quit smoking between 15 and 30 years ago will be missed by limiting eligibility criteria to 15 years.8
In a secondary analysis of individuals in the Framingham Heart Study, 40.8% of lung cancers in people who used to smoke were found in those who had quit more than 15 years previously, and lung cancer risk in those who had been smoke free for 25 years or more was more than three times that of people who never smoked.9
The PLCOm2012 risk prediction model has been used in Ontario, Canada, to select screening participants in the Ontario Health (Cancer Care Ontario) lung cancer screening pilot.10 Results from the pilot demonstrated effective implementation of the PLCOm2012 risk model to identify screening eligible. Risk prediction models, both the Liverpool Lung Project (LLP) model and the PLCOm2012 model, were also successfully used to identify high-risk individuals for screening in three of five UK lung cancer screening programs including two programs that used mobile screening vans to reach underserved populations.11
The evidence for successful implementation and superior performance of the PLCOm2012 risk prediction model for selection of high-risk individuals to screen compared to categorical age-smoking variables has now been demonstrated by the ILST in a prospective study in multiple countries and multiple institutions.2 It confirms the results from other studies showing the model finds more lung cancers and reduces disparities for Black people and women, and it shows that the model can be successfully implemented in clinical practice.
It is time for institutions and jurisdictions to use the PLCOm2012 or a comparably validated risk prediction model for selection of individuals for lung cancer screening in addition to, or in-place of, categorial criteria to maximize the benefits of screening for all individuals at high risk of lung cancer.
References
- 1. ten Haaf K, Jeon J, Tammemägi MC, et al. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med 2017; 14: e1002277.
- 2. MC Tammemägi, M Ruparel, A Tremblay, et al. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study. Lancet Oncol 2022;23(1):138-148,ISSN 1470-2045, https://doi.org/10.1016/S1470-2045(21)00590-8.
- 3. Gohagan JK, Prorok PC, Greenwald P, Kramer BS. The PLCO Cancer Screening Trial: Background, Goals, Organization, Operations, Results. Rev Recent Clin Trials. 2015;10(3):173-180. doi:10.2174/1574887110666150730123004
- 4. Aldrich MC, Mercaldo SF, Sandler KL, Blot WJ, Grogan EL, Blume JD. Evaluation of USPSTF lung cancer screening guidelines among African American adult smokers. JAMA Oncol. 2019;5(9):1318-1324. Medline:31246249 doi:10.1001/jamaoncol.2019.1402
- 5. Pasquinelli MM, Tammemägi MC, Kovitz KL, et al. Addressing Gender Disparities in Lung Cancer Screening Eligibility: USPSTF versus PLCOm2012 Criteria. Chest. 2021 Jul 9:S0012-3692(21)01316-7. doi: 10.1016/j.chest.2021.06.066. Epub ahead of print. PMID: 34252436
- 6. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA 2021; 325: 962–70.
- 7. Pasquinelli MM, Tammemägi MC, Kovitz KL, et al. Risk Prediction Model versus United States Preventive Services Task Force Lung Cancer Screening Eligibility Criteria – Reducing Race Disparities. Journal of Thoracic Oncology; 2020. https://doi.org/10.1016/j.jtho.2020.08.006
- 8. Luo YH, Luo L, Wampfler JA, et al. 5-year overall survival in patients with lung cancer eligible or ineligible for screening according to US Preventive Services Task Force criteria: a prospective, observational cohort study. Lancet Oncol. 2019; 20(8):1098-1108. doi: 10.1016/S1470-2045(19)30329-8. Epub 2019 Jun 26. PMID: 31255490; PMCID: PMC6669095.
- 9. Tindle HA, Stevenson Duncan M, Greevy RA, et al. Lifetime Smoking History and Risk of Lung Cancer: Results From the Framingham Heart Study [published correction appears in J Natl Cancer Inst. 2018 Oct 1;110(10):1153]. J Natl Cancer Inst. 2018;110(11):1201-1207. doi:10.1093/jnci/djy041
- 10. MC Tammemägi, GE Darling, H Schmidt, et al. Selection of individuals for lung cancer screening based on risk prediction model performance and economic factors—the Ontario experience. Lung Cancer 2021;156:31-40. https://doi.org/10.1016/j.lungcan.2021.04.005
- 11. Balata H, Ruparel M, O’Dowd E, et al. Analysis of the baseline performance of five UK lung cancer screening programmes. Lung Cancer 2021;161:136-140. ISSN 0169-5002, https://doi.org/10.1016/j.lungcan.2021.09.012.
