Evidence map›Paper›PMID 23649455›Full record

GuidelineChest2013

Screening for lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines.

Frank C Detterbeck, Peter J Mazzone, David P Naidich, Peter B Bach

Registry-linked trialOpen access · bronzeAbstract readPractice GuidelineSystematic Review
In one paragraph

Guideline in Chest, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03487952 (Evaluation of Lung Nodule and Lung Cancer Detection With Artificial Intelligence Assisted Computed Tomography Among People Living in North China), which is not on this map. Cited by 200 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
200citing papers in PubMed, 11 pooled it
38.1field-weighted citation impact, top 1% of its field
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.

NCT03487952 unknown statusnot on this mapstarted 2018, after this paper: background citation

Evaluation of Lung Nodule and Lung Cancer Detection With Artificial Intelligence Assisted Computed Tomography Among People Living in North China: a Prospective Single-arm Multicentre Study of Screening

TypeobservationalSponsorPeking University People's HospitalRan2018 to 2021Enrolled5,000ConditionsSolitary Pulmonary Nodule, Multiple Pulmonary NodulesArmsQuestionnaire Administration
3 · Its place in the literature

Who cites it

200 citing papers in PubMed, 11 syntheses or guidelines pooled it, 403 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Guideline
  4. Pooled it
  5. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.Journal of the National Comprehensive Cancer Network : JNCCN · 2018
    Guideline
  6. Guideline
  7. [Systematic Review of the Methodology Quality in Lung Cancer Screening Guidelines].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2016
    Pooled it
  8. Guideline
  9. Guideline
  10. Guideline
  11. Guideline
  12. Trial
  13. Aiding shared decision making in lung cancer screening: two decision tools.Journal of public health (Oxford, England) · 2021
    Trial
  14. Trial
  15. Trial
  16. Trial
  17. Trial
  18. Article
  19. Article
  20. Article

140 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 1 country.

Frank C DetterbeckYale University School of Medicine, New Haven, CT. Electronic address: frank.detterbeck@yale.edu.
Peter J MazzoneRespiratory Institute, The Cleveland Clinic, Cleveland, OH.
David P NaidichNYU Langone Medical Center, Tisch Hospital, New York, NY.
Peter B BachMemorial Sloan-Kettering Cancer Center, New York, NY.
Cleveland Clinic · USMemorial Sloan Kettering Cancer Center · USTisch Hospital · USYale University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

backgroundLung cancer is by far the major cause of cancer deaths largely because in the majority of patients it is at an advanced stage at the time it is discovered, when curative treatment is no longer feasible. This article examines the data regarding the ability of screening to decrease the number of lung cancer deaths.

methodsA systematic review was conducted of controlled studies that address the effectiveness of methods of screening for lung cancer.

resultsSeveral large randomized controlled trials (RCTs), including a recent one, have demonstrated that screening for lung cancer using a chest radiograph does not reduce the number of deaths from lung cancer. One large RCT involving low-dose CT (LDCT) screening demonstrated a significant reduction in lung cancer deaths, with few harms to individuals at elevated risk when done in the context of a structured program of selection, screening, evaluation, and management of the relatively high number of benign abnormalities. Whether other RCTs involving LDCT screening are consistent is unclear because data are limited or not yet mature.

conclusionsScreening is a complex interplay of selection (a population with sufficient risk and few serious comorbidities), the value of the screening test, the interval between screening tests, the availability of effective treatment, the risk of complications or harms as a result of screening, and the degree with which the screened individuals comply with screening and treatment recommendations. Screening with LDCT of appropriate individuals in the context of a structured process is associated with a significant reduction in the number of lung cancer deaths in the screened population. Given the complex interplay of factors inherent in screening, many questions remain on how to effectively implement screening on a broader scale.

Indexed as

Radiography, ThoracicTomography, X-Ray ComputedCounselingHumansLung NeoplasmsMass ScreeningRiskSmokingSputum

Identifiers

PMID23649455
PMCPMC3749713
OpenAlexW2129663164

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.