Evidence map›Paper›PMID 35809038›Full record

SynthesisInternational journal of cancer2022

The influence of postscreening follow-up time and participant characteristics on estimates of overdiagnosis from lung cancer screening trials.

Mengmeng Li, Li Zhang, Hadrien Charvat, Matthew E Callister, Peter Sasieni, Evangelia Christodoulou, Rudolf Kaaks, Mattias Johansson, Andre L Carvalho, Salvatore Vaccarella and 1 more

Abstract readSystematic Review
In one paragraph

Synthesis in International journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Screening for Lung Cancer, Overdiagnosis, and Healthcare Utilization: A Nationwide Population-Based Study.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
    Article
  6. Article
  7. Review
  8. Article
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

11 authors.

Mengmeng LiDepartment of Cancer Prevention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.ORCID 0000-0001-6630-7468
Li ZhangInternational Agency for Research on Cancer, Lyon, France.
Hadrien CharvatInternational Agency for Research on Cancer, Lyon, France.ORCID 0000-0003-3624-1394
Matthew E CallisterLeeds Teaching Hospitals, Leeds, UK.
Peter SasieniKings College London, London, UK.
Evangelia ChristodoulouDivision of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
Rudolf KaaksDivision of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
Mattias JohanssonInternational Agency for Research on Cancer, Lyon, France.
Andre L CarvalhoInternational Agency for Research on Cancer, Lyon, France.
Salvatore VaccarellaInternational Agency for Research on Cancer, Lyon, France.
Hilary A RobbinsInternational Agency for Research on Cancer, Lyon, France.ORCID 0000-0001-6041-6866

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Evaluating risk prediction models for use in lung cancer screening in diverse populations around the worldR03CA245979 · NCI · INTERNATIONAL AGENCY FOR RES ON CANCER · PI ROBBINS, HILARY A. · 2020 to 2020
$112k
Cancer Research UK 25356NCI NIH HHS R03 CA245979NCI NIH HHS U19 CA203654NIH HHS R03 CA245979World Health Organization 001
6 · The paper itself

Abstract

We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with postscreening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from -27.0% (ITALUNG, 8.3 years follow-up) to 67.2% (DLCST, 5.0 years follow-up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow-up time. The annual changes in Ps were -3.5% and -3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from -14.9% (55-59 years) to 21.7% (70-74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow-up and population characteristics.

Indexed as

Early Detection of CancerLung NeoplasmsFollow-Up StudiesHumansMass ScreeningMiddle AgedOverdiagnosislung cancer screeningoverdiagnosisrandomised controlled trial

Identifiers

PMID35809038
PMCPMC10157369

What OpenQuestion holds

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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.