Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support.

Journal: JCO clinical cancer informatics
PMID: 40334175

Abstract

PURPOSE: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagnostic procedures present significant challenges. This study proposes an advanced pipeline that integrates machine learning (ML) and causal inference techniques to optimize lung cancer screening decisions.

Authors

  • Hao Dai
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL.
  • Yu Huang
    School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China.
  • Xing He
    University of Florida, Gainesville, Florida, USA.
  • Tiancheng Zhou
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL.
  • Yuxi Liu
    Beijing Key Laboratory for Green Catalysis and Separation, Key Laboratory of Beijing on Regional Air Pollution Control, Key Laboratory of Advanced Functional Materials, Education Ministry of China, Laboratory of Catalysis Chemistry and Nanoscience, Department of Environmental Chemical Engineering, School of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
  • Xuhong Zhang
    School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN.
  • Yi Guo
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Jingchuan Guo
    Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.

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