Evidence map›Paper›PMID 41559098›Full record

SynthesisNPJ primary care respiratory medicine2026

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

Zijuan Fan, Manqi Zheng, Ziyun Guan, Hanting Liu, Pengyue Guo, Yang Zhu, Bo Zhang, Luyao Hu, Xianqi Zhao, Tiantian Fu and 7 more

Abstract readSystematic Review
In one paragraph

Synthesis in NPJ primary care respiratory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Zijuan Fan *Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Manqi Zheng *Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Ziyun GuanDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Hanting LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Pengyue GuoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Yang ZhuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Bo ZhangDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Luyao HuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Xianqi ZhaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Tiantian FuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Mengting LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Xinran JiangDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Ningjun RenDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Chunli ZhangDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Wenxi WangThe First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China. danywwx@163.com.
Chun HaoDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China. haochun@mail.sysu.edu.cn.
Jinghua LiDepartment of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macao SAR, China. lijinghua@um.edu.mo.

Funding

Start-up Research Grant of the University of Macau SRG2025-00031-FHS
6 · The paper itself

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.

Indexed as

Cost-Benefit AnalysisEarly Detection of CancerLung NeoplasmsMass ScreeningCost-Effectiveness AnalysisHumansPractice Guidelines as TopicQuality-Adjusted Life YearsRisk AssessmentTomography, X-Ray Computed

Identifiers

PMID41559098
PMCPMC12920648

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.