Cost-effectiveness of a machine learning risk prediction model (LungFlag™) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain

Share this article:

Share your details and we'll email you our Publication


Pajares, Soriano JB, Seijo LM, Trujillo JC, Marzo M, Olmedo ME, Higuera O, Arrabal N, Flores A, García JF, Crespo M, Carcedo D, Heuser C, Olghi N, Choman E, Gorospe L


  • Risk Prediction models (RPM) based on individual risk have proved to effectively identify individuals at high risk of lung cancer for screening in comparison to selection criteria based on age and pack-years alone.
  • LungFlag™ is a machine learning-based risk prediction model designed to improve selection of individuals at risk for enrollment in NSCLC screening programs with low dose computed tomography (LDCT), by evaluating routine clinical and laboratory data.
  • The aim of this analysis is to assess the cost-effectiveness of implementing LungFlag in Spain.


Using LungFlag for the selection of high-risk individuals for NSCLC screening in Spain, would be cost-effective compared to a hypothetical scenario screening all individuals meeting USPSTF criteria. This strategy would be dominant versus the current situation of no-screening.