Although the primary results of the National Lung Screening Trial (NLST) were published only 7 years ago, lung cancer screening today is rapidly advancing (1). In this issue of Translational Lung Cancer Research (TLCR) dedicated to CT screening for lung cancer, we include reviews of many of those advances—some already in clinical use, and some still in development.
We open this issue with a review by de Groot et al. of global trends in the epidemiology of lung cancer, including smoking rates, indoor air pollution and occupational exposures, the history and carcinogenesis of tobacco smoking, as well as environmental and genetic risk factors for lung cancer (2). Dr. Pinsky provides us with a world-wide perspective on lung cancer incidence and mortality, a review of randomized controlled trials and demonstration projects of low-dose CT screening for lung cancer, and a summary of screening guidelines and position papers from various countries (3).
Selecting the appropriate population for screening is arguably the most urgent issue in screening. Eligibility criteria for screening have been primarily based on age and smoking history (e.g., NLST criteria ages 55–74, current or former smoker with at least 30 pack-year smoking history, quit date in former smokers within the last 15 years). We know, however, that these criteria will include individuals who are at relatively low risk of developing lung cancer, but will also fail to include a significant proportion of lung cancers (4). Pinsky and Berg suggest that only 26.7% of incident lung cancers would be captured using the original NLST eligibility criteria (5). The challenge is not only to reduce the risks of LDCT screening in patients who are unlikely to die from lung cancer, but also to increase the detection of early stage lung cancer in those at highest risk. As you will see in this issue of TLCR, refining screening eligibility criteria on the basis of additional patient characteristics, medical history, family history, and the presence of COPD/emphysema can select a higher risk population and reduce the potential harms of screening in a lower risk population.
Comorbid medical conditions are a confounding issue in patient selection. A patient who meets eligibility criteria may be more likely to die of competing causes than from lung cancer, especially those that are related to heavy tobacco use, including cardiovascular disease, chronic obstructive pulmonary disease and extrapulmonary malignancy. These same conditions may preclude the patient from effective treatment for an early stage lung cancer. As Young and Hopkins point out, the risk of lung cancer as measured by existing clinical models does not equate to who will get the most benefit of screening (6). They have shown that, although the presence of airflow limitation confers a greater risk of lung cancer, it is also associated with more aggressive lung cancer and higher lung cancer mortality, as well as a greater likelihood of death from a competing cause. One can appreciate the ethical dilemma of clinicians who must balance the patient’s lung cancer risk versus their risks of dying of other diseases.
Dr. Tammemägi also addresses the concern by some that lung cancer risk models select a screening population that is more likely to be elderly, in poor health and less likely to benefit from screening compared to the NLST eligibility criteria (7). If the 1/4th of NLST eligible individuals at low risk of lung cancer (6-year incidence proportion =0.008) are excluded, the age, comorbidity count and competing causes of death are similar to those selected by the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO) m2012 model as having a lung cancer risk of ≥1.5%. The number needed to screen to avert one lung cancer death could be significantly reduced with this approach, and fewer low-risk patients would be screened. Risk modeling can also be used to address health care disparities, e.g., the higher rates of lung cancer in African Americans within the US. Expanded risk prediction models can assess lung cancer risks in patients who do not meet current eligibility criteria, including light smokers, never smokers, and current smokers younger than 55 years.
It is likely that annual screening may not be essential in all screening-eligible individuals. Screening trials have been, understandably, of relatively short duration. The NLST included 3 annual screening CTs; the NELSON trial included 3 rounds of screening over a 5-year period (8,9). The NELSON trial provided the opportunity to assess screening intervals of 1, 2 and 2.5 years. As Drs. Heuvelmans and Oudkerk describe in “Appropriate screening intervals in low-dose CT lung cancer screening”, prolonging the screening interval in a selected population with a lower lung cancer risk profile could reduce the harms and costs of screening without an unacceptable increase in interval lung cancers (10).
Lung cancer screening in the US became clinically available following the results of the NLST, and reimbursed by third party payers after the US Preventive Services Task Force (USPSTF) recommendation on screening for lung cancer in 2014. In 2015, the Centers for Medicare & Medicaid Services (CMS) issued a coverage memo for lung cancer screening counseling, shared decision making and annual lung cancer screening but only if a number of criteria were met (11). These included the following:
- Determination of beneficiary eligibility including age, absence of signs or symptoms of lung cancer, a specific calculation of cigarette smoking pack-years; and if a former smoker, the number of years since quitting;
- Shared decision making, including the use of one or more decision aids, to include benefits and harms of screening, follow-up diagnostic testing, over-diagnosis, false positive rate, and total radiation exposure;
- Counseling on the importance of adherence to annual lung cancer LDCT screening, impact of comorbidities and ability or willingness to undergo diagnosis and treatment;
- Counseling on the importance of maintaining cigarette smoking abstinence if former smoker; or the importance of smoking cessation if current smoker and, if appropriate, furnishing of information about tobacco cessation interventions; and
- If appropriate, the furnishing of a written order for lung cancer screening with LDCT.
As Dr. Lowenstein et al. point out, the prerequisite of counseling and a shared decision-making visit including the use of decision aids for a preventive health care service is unprecedented in the US (12). The shared decision-making visit is critical in that this is the opportunity to involve the patient in the consideration of his/her personal lung cancer risk, risk of death from other causes, the potential harms of screening and the potential benefits of early detection of lung cancer. Although no one decision aid has been universally adopted, Dr. Lowenstein and her team provide us with a review of currently available decision aids for lung cancer screening, as well as a model of how these can be incorporated into the shared decision-making visit. There are a number of barriers to a successful shared decision-making visit, complicated by the variability in who conducts this visit, and the method and content of the discussion.
The CMS regulations for lung cancer screening stipulate not only that the shared decision-making visit includes counseling on the importance of smoking cessation, but also that the imaging facility make available smoking cessation interventions by current smokers. Current smokers who undergo CT screening for lung cancer express a greater readiness to quit smoking, and report higher quit rates than smokers who are not in screening programs (13). The CT screening process may be considered a “teachable moment” for smoking cessation. Improving quit rates in this population of heavy smokers could have a greater impact on reducing morbidity and mortality than the detection of lung cancer on a screening CT (14). In this issue of TLCR, Dr. Minnix et al. emphasize the role of extended counseling and/or pharmacotherapy, the routine integration of cessation treatment programs into the screening process, and the multiple opportunities for cessation presented in a supportive environment (15).
The primary goal of CT screening for lung cancer is the detection of a pulmonary nodule that may represent lung cancer. The majority of lung nodules are, however, benign, and the distinction between benign and malignant nodules becomes critical in the acceptance of CT screening and its cost-effectiveness. The high false positive rate experienced in the NLST has been reduced to some extent by raising the threshold for actionable nodules from 4 mm in the NLST and from 5 mm in ELCAP to the 6 mm threshold in Lung-RADS v.1.0 (16-19). In this issue of TLCR, Dr. Vlahos et al. review the technical parameters that can influence nodule detection, the features associated with benign nodules, and imaging techniques that may be useful in further characterizing indeterminate nodules (20). Drs. Kadir and Gleeson then further explore radiomics/texture analysis and deep machine learning approaches to characterizing indeterminate nodules as either benign or malignant (21). Dr. Clay and his team describe their quantitative analysis tool, Computer Aided Analysis and Risk Yield (CANARY), that allows non-invasive assessment of tumor characteristics, and potentially risk stratification of pulmonary adenocarcinomas (22).
Biomarkers may play a tremendous role in the future in not only selecting patients for CT screening, but also for diagnosis in patients with symptoms or with indeterminate nodules. In this issue of TLCR, Drs. Hanash et al. review the variety of biomarkers and biomarker panels currently being developed for use in lung cancer screening (23).
Finally, low dose CT lung cancer screening can result in other clinically significant findings which may impact treatment decisions, morbidity and mortality (24-26). Drs. Ravenel and Nance review detection of coronary artery calcification on CT scans performed for lung cancer screening, and Dr. Godoy et al. cover extrathoracic malignancies that can be incidentally found while screening for lung cancer (27,28). It is clear that lung cancer screening with CT provides a unique opportunity to detect other potentially life-threatening disease.
We are pleased to provide this comprehensive review and update of lung cancer screening. It is an exciting time in lung cancer screening and we hope that these advances in screening will result in further reduction in lung cancer mortality, a decrease in the harms of screening, and improved patient care.
- National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409. [Crossref] [PubMed]
- de Groot PM, Wu CC, Carter BW, et al. The epidemiology of lung cancer. Transl Lung Cancer Res 2018;7:220-33.
- Pinsky PF. Lung cancer screening with low-dose CT: a world-wide view. Transl Lung Cancer Res 2018;7:234-42.
- Kovalchik SA, Tammemagi M, Berg CD, et al. Targeting of low-dose CT screening according to the risk of lung-cancer death. N Engl J Med 2013;369:245-54. [Crossref] [PubMed]
- Pinsky PF, Berg CD. Applying the National Lung Screening Trial eligibility criteria to the US population: what percent of the population and of incident lung cancers would be covered? J Med Screen 2012;19:154-6. [Crossref] [PubMed]
- Young RP, Hopkins RJ. Chronic obstructive pulmonary disease (COPD) and lung cancer screening. Transl Lung Cancer Res 2018;7:347-60.
- Tammemägi MC. Selecting lung cancer screenees using risk prediction models—where do we go from here. Transl Lung Cancer Res 2018;7:243-53.
- National Lung Screening Trial Research Team, Aberle DR, Berg CD, et al. The National Lung Screening Trial: overview and study design. Radiology 2011;258:243-53. [Crossref] [PubMed]
- Horeweg N, Scholten ET, de Jong PA, et al. Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers. Lancet Oncol 2014;15:1342-50. [Crossref] [PubMed]
- Heuvelmans MA, Oudkerk M. Appropriate screening intervals in low-dose CT lung cancer screening. Transl Lung Cancer Res 2018;7:281-7.
- Available online: https://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274#. (Accessed May 30, 2018).
- Lowenstein LM, Deyter GM, Nishi S, et al. Shared decision-making conversations and smoking cessation interventions: critical components of lowdose CT lung cancer screening programs. Transl Lung Cancer Res 2018;7:254-71.
- Taylor KL, Cox LS, Zincke N, et al. Lung cancer screening as a teachable moment for smoking cessation. Lung Cancer 2007;56:125-34. [Crossref] [PubMed]
- Tanner NT, Kanodra NM, Gebregziabher M, et al. The Association between Smoking Abstinence and Mortality in the National Lung Screening Trial. Am J Respir Crit Care Med 2016;193:534-41. [Crossref] [PubMed]
- Minnix JA, Karam-Hage M, Blalock JA, et al. The importance of incorporating smoking cessation into lung cancer screening. Transl Lung Cancer Res 2018;7:272-80.
- Gierada DS, Pinsky P, Nath H, et al. Projected Outcomes Using Different Nodule Sizes to Define a Positive CT Lung Cancer Screening Examination. J Natl Cancer Inst 2014;106. [Crossref] [PubMed]
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- Yip R, Henschke CI, Yankelevitz DF, et al. CT Screening for Lung Cancer: Alternative Definitions of Positive Test Result Based on the National Lung Screening Trial and International Early Lung Cancer Action Program Databases. Radiology 2014;273:591-6. [Crossref] [PubMed]
- Available online: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads
- Vlahos I, Stefanidis K, Sheard S, et al. Lung cancer screening: nodule identification and characterization. Transl Lung Cancer Res 2018;7:288-303.
- Kadir T, Gleeson F. Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res 2018;7:304-12.
- Clay R, Rajagopalan S, Karwoski R, et al. Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: radiologic biopsy, risk stratification and future directions. Transl Lung Cancer Res 2018;7:313-26.
- Hanash S, Ostrin EJ, Fahrmann JF. Blood based biomarkers beyond genomics for lung cancer screening. Transl Lung Cancer Res 2018;7:327-35.
- Pinsky PF, Dunn B, Gierada D, et al. Incidental renal tumours on low-dose CT lung cancer screening exams. J Med Screen 2017;24:104-9. [Crossref] [PubMed]
- Chiles C, Paul NS. Beyond lung cancer: a strategic approach to interpreting screening computed tomography scans on the basis of mortality data from the national lung screening trial. J Thorac Imaging 2013;28:347-54. [Crossref] [PubMed]
- Mets OM, de Jong PA, Prokop M. Computed tomographic screening for lung cancer: an opportunity to evaluate other diseases. JAMA 2012;308:1433-4. [Crossref] [PubMed]
- Ravenel JG, Nance JW. Coronary artery calcification in lung cancer screening. Transl Lung Cancer Res 2018;7:361-7.
- Godoy MC, White CS, Erasmus JJ, et al. Extrapulmonary neoplasms in lung cancer screening. Transl Lung Cancer Res 2018;7:368-75.