Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Abstract

BackgroundThe National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases.PurposeTo determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans.Materials and methodsThree fully automated machine learning algorithms were used to assess CCS, level of liver steatosis and emphysema percentage in the lung. The algorithms were used on low-dose computed tomography scans acquired from 12,332 participants in NLST.ResultsIn a multivariate analysis, association between the three algorithm scores and CVD mortality have shown an OR of 1.72 (p = 0.003), 2.62 (p ConclusionThe three automated machine learning algorithms could help physicians to assess the incidence and risk of CVD mortality in this specific population. Application of these algorithms to existing LDCT scans can provide valuable health care information and assist in future research.