Cost-effectiveness of lung cancer screening: insights from risk stratification, guidelines, and emerging technologies-a systematic review.

Journal: NPJ primary care respiratory medicine
Published Date: Jan 20, 2026

Abstract

Lung cancer is the leading cause of cancer-related mortality worldwide, with most patients diagnosed at advanced stages. Early detection through screening can significantly reduce mortality, making cost-effectiveness evidence crucial for guiding policy decisions. This systematic review aimed to evaluate the cost-effectiveness of lung cancer screening across various modalities, populations, and settings. A comprehensive search of PubMed, EMBASE, Web of Science, and Cochrane Library was conducted for studies up to March 18, 2025, adhering to PRISMA guidelines. A total of 79 studies from 21 countries were included, with model-based analyses prevalent and 89.9% rated as high quality. Low-dose computed tomography (LDCT) emerged as the primary screening modality, although evidence on artificial intelligence (AI) and biomarkers is limited. Fourteen studies comparing LDCT with no screening showed incremental cost-effectiveness ratios (ICERs) ranging from $8376 to $200,921 per quality-adjusted life-year (QALY) gained. Notably, 90.3% of LDCT strategies were cost-effective by national thresholds, particularly in older adults and high-risk groups. Biennial screening often proved more cost-effective than annual in many scenarios. Overall, LDCT screening demonstrated favorable cost-effectiveness, necessitating further evaluation for emerging technologies in underserved regions.

Authors

  • Zijuan Fan
    Department of Orthopaedic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Road No. 79, Hangzhou, China.
  • Manqi Zheng
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Ziyun Guan
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Hanting Liu
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Pengyue Guo
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Yang Zhu
    McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Luyao Hu
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Xianqi Zhao
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Tiantian Fu
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Mengting Liu
    Department of Ophthalmology, The Second Xiangya Hospital, Hunan Clinical Research Centre of Ophthalmic Disease, Central South University, Changsha, Hunan, China.
  • Xinran Jiang
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Ningjun Ren
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Chunli Zhang
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Wenxi Wang
    Department of Magnetic Resonance Imaging, First Hospital of Qinhuangdao, Qinhuangdao, China.
  • Chun Hao
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China. [email protected].
  • Jinghua Li
    Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China. Electronic address: [email protected].

Keywords

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