Comparing Artificial Intelligence and Traditional Regression Models in Lung Cancer Risk Prediction Using A Systematic Review and Meta-Analysis.

Journal: Journal of the American College of Radiology : JACR
Published Date: Mar 4, 2025

Abstract

PURPOSE: Accurately identifying individuals who are at high risk of lung cancer is critical to optimize lung cancer screening with low-dose CT (LDCT). We sought to compare the performance of traditional regression models and artificial intelligence (AI)-based models in predicting future lung cancer risk.

Authors

  • Sierra Leonard
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Meet A Patel
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Zili Zhou
  • Ha Le
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Prosanta Mondal
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Scott J Adams
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. Electronic address: scott.adams@usask.ca.

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