Evidence map›Paper›PMID 36305647›Full record

ArticleInternational journal of cancer2023

Assessment of the EarlyCDT-Lung test as an early biomarker of lung cancer in ever-smokers: A retrospective nested case-control study in two prospective cohorts.

Wendy Yi-Ying Wu, Zahra Haider, Xiaoshuang Feng, Alicia K Heath, Anne Tjønneland, Antonio Agudo, Giovanna Masala, Hilary A Robbins, José-María Huerta, Marcela Guevara and 14 more

Open access · hybridAbstract read
In one paragraph

Article in International journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 40% 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.

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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

24 authors at 15 institutions in 8 countries.

Wendy Yi-Ying WuDepartment of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.ORCID 0000-0002-6169-5155
Zahra HaiderDepartment of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.
Xiaoshuang FengGenomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
Alicia K HeathDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.ORCID 0000-0001-6517-1300
Anne TjønnelandDiet, Cancer and Health, Danish Cancer Society Research Center, Copenhagen, Denmark.
Antonio AgudoUnit of Nutrition and Cancer, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Barcelona, Spain.
Giovanna MasalaInstitute for Cancer Research Prevention and Clinical Network (ISPRO), Florence, Italy.
Hilary A RobbinsGenomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.ORCID 0000-0001-6041-6866
José-María HuertaDepartment of Epidemiology, Murcia Regional Health Council, Murcia, Spain.
Marcela GuevaraCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Matthias B SchulzeGerman Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
Miguel Rodriguez-BarrancoCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Paolo VineisMRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
Rosario TuminoHyblean Association for Epidemiological Research, AIRE ONLUS, Ragusa, Italy.
Rudolf KaaksDivision of Cancer Epidemiology, German Cancer Research Center (DFKZ), Heidelberg, Germany.
Renée T FortnerDivision of Cancer Epidemiology, German Cancer Research Center (DFKZ), Heidelberg, Germany.
Sabina SieriEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano Via Venezian, Milan, Italy.ORCID 0000-0001-5201-172X
Salvatore PanicoDipartimento di Medicina Clinica e Chirurgia Federico II Universioty, Naples, Italy.
Therese Haugdahl NøstDepartment of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
Torkjel M SandangerDepartment of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
Tonje BraatenDepartment of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
Mattias JohanssonGenomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
Beatrice MelinDepartment of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.
Mikael JohanssonDepartment of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.ORCID 0000-0003-4182-8923
Centre international de recherche sur le cancer · FRUiT The Arctic University of Norway · NOUmeå University · SEGerman Cancer Research Center · DEBellvitge University Hospital · ESCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública · ESFederico II University Hospital · ITFondazione IRCCS Istituto Nazionale dei Tumori · ITImperial College London · GBInstituto de Investigación Biosanitaria de Granada · ESInstituto de Salud Pública de Navarra · ESItalian Association for Cancer Research · ITPublic Health England · GBUniversity of Copenhagen · DKUniversity of Potsdam · DE

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Rayjean J. Hung · 2017 to 2026
$23.7M
The Lung EArly Proteins project: A LEAP toward implementing biomarkers in lung cancer screeningR01CA262164 · NCI · INTERNATIONAL AGENCY FOR RES ON CANCER · PI ROBBINS, HILARY A. · 2022 to 2025
$2.0M
Cancer Research UK 14136Cancer Research UK C8221/A29017Medical Research Council 1000143Medical Research Council G0401527Medical Research Council G1000143Medical Research Council MR/M012190/1Medical Research Council MR/N003284/1Medical Research Council MR/S019669/1NCI NIH HHS R01 CA262164NCI NIH HHS U19 CA203654World Health Organization 001
6 · The paper itself

Abstract

The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample <3 years prior to diagnosis. Conditional logistic regression was used to estimate the association between EarlyCDT-Lung test results and lung cancer risk. Sensitivity and specificity of the EarlyCDT-Lung test were calculated in all subjects and subgroups based on age, smoking history, lung cancer stage, sample collection time before diagnosis and year of sample collection. The overall lung cancer odds ratios were 0.89 (95% CI: 0.34-2.30) for a moderate risk EarlyCDT-Lung test result and 1.09 (95% CI: 0.48-2.47) for a high-risk test result compared to no significant test result. The overall sensitivity was 8.4% (95% CI: 4.6-14) and overall specificity was 92% (95% CI: 87-96) when considering a high-risk result as positive. Stratified analysis indicated higher sensitivity (17%, 95% CI: 7.2-32.1) in subjects with blood drawn up to 1 year prior to diagnosis. In conclusion, our study does not support a role of the EarlyCDT-Lung test in identifying the high-risk subjects in ever-smokers for lung cancer screening in the EPIC and NSHDS cohorts.

Indexed as

Lung NeoplasmsBiomarkersCase-Control StudiesEarly Detection of CancerHumansLungProspective StudiesRetrospective StudiesSmokersBiomarkersbiomarkersEarlyCDT-Lung testlung cancerprediagnostic samples

Identifiers

PMID36305647
PMCPMC10157531
OpenAlexW4307494183

What OpenQuestion holds

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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.