Less Is More in Lung Cancer Risk Prediction Models
Menée à partir de données portant sur 1 159 participants à un programme de dépistage du cancer du poumon par tomographie numérique à faible dose de rayonnements ionisants, cette étude compare la performance de cinq modèles prédictifs pour distinguer des nodules cancéreux de nodules bénins
Screening of high-risk individuals by low-dose chest computed tomography (CT) reduces lung cancer mortality, as has been shown by 2 large randomized clinical trials. Contrary to other cancer screening programs, such as breast and colorectal cancer screening, individuals eligible for screening are not selected only based on sex and age. Lung cancer is in most cases diagnosed in (former) smokers. To increase the efficacy of a screening program, and to minimize harms to individuals at low risk of the disease, it is most cost-effective to invite only those individuals who have the highest risk of developing lung cancer to undergo annual low-dose chest CT. Risk prediction models aim to assist in identifying these high-risk individuals, and most international lung cancer screening guidelines recommend using a model to optimize selection of the screening population.
JAMA Network Open , commentaire en libre accès, 2019