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New lung cancer model identifies who to screen
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A new screening tool — the HUNT Lung Cancer Risk Model — predicted lung cancer with almost 88% accuracy, according to findings recently published in EBioMedicine.
“Several multivariable risk prediction models have been proposed to improve the selection of people for [lung cancer] screening,” researchers wrote.
“However, these models and corresponding studies also have a variety of potential issues such as age cutoffs, inclusion of mainly heavy smokers, restricted and/or empirical inclusion of predictors and listwise exclusion of cases with missing data, all of which call into question the transferability of these models to clinical practice,” they added.
To come up with a new model that removed those issues, researchers reviewed data from 65,237 residents of Norway aged older than 20 years during 1995 to 1997. After a median of 15.2 years, 583 had lung cancer and of these, 552 were ever smokers. Researchers used this group to develop a model of risk factors to determine lung cancer risk. They validated the model in a group of 45,341 ever-smokers, of whom 675 had been diagnosed with lung cancer after a median follow-up of 11.6 years.
The model included five previously known risk factors — age, BMI, number of cigarettes smoked daily, pack years, and years since the person quit — as well as two new independent risk factors for lung cancer: periodic daily cough (beta coefficient = 0.4059355, P < .001) and number of hours exposed to indoor smoke daily (beta coefficient = 0.1663201, P < .002).
Researchers found that their formula including the beta coefficients of these seven risk factors revealed a 0.879 concordance index (95% CI, 0.866-0.891) with an area under the curve of 0.87 (95% CI, 0.85-0.89) within 6 years, and that 22% of ever-smokers would require screening to identify 81% to 85% of all lung cancers in that same period of time.
“The method can reduce the number of people exposed to radiation from unnecessary CT scans, and maximize identification of persons with true risk,” Oluf Dimitri Røe, MD, PhD, a senior oncologist at the Norwegian University of Science and Technology, added in a press release. “It is also the first model that can accurately predict lung cancer in light smokers, younger people, and people who quit smoking many years before.” – by Janel Miller
Disclosures: The authors report no relevant financial disclosures.
Perspective
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Gregory C. Kane, MD
Since the publication of the National Lung Cancer Screening Trial in 2011, clinicians and researchers have both recognized that the broad inclusion criteria of the National Lung Cancer Screening Trial are lacking in specificity for persons at highest risk for lung cancer.
Current screening guidelines include current or former smokers older than 55 years who have smoked within the last 15 years and have at least 30 pack years of cumulative exposure. We are well aware that within this broad population some patients have an even greater risk whereas others remain at relatively lower risk. Since that time several investigators have provided insights which refine the risk model and propose an alternate mechanism of identifying patients at greatest risk.
The recent publication by Markaki and colleagues in EBioMedicine provides important data through the HUNT Lung Cancer Model which helps identify patients at greatest risk. Surprisingly, the total hours and duration of exposure to smoke indoors and the presence of a daily cough highlighted a population with an even higher risk for lung cancer compared with other smokers in this Norwegian cohort.
The researchers’ model could be integrated into an electronic medical record which might prompt primary care physicians to recommend certain at-risk patients for participation in lung cancer screening, or more simply, primary care clinicians caring for patients with advanced COPD and a daily cough should place special emphasis on referring that specific population.
As we move further into era of lung cancer screening and early lung cancer detection, we should expect further refinement of our current inclusion criteria in order to identify cancers in those patients at highest risk with a yield which will be much greater and benefits that will be even more substantial.
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