Evidence map›Paper›PMID 38586302›Full record

ArticleJTO clinical and research reports2024

Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study.

Olav Toai Duc Nguyen, Ioannis Fotopoulos, Maria Markaki, Ioannis Tsamardinos, Vincenzo Lagani, Oluf Dimitri Røe

Abstract read
In one paragraph

Article in JTO clinical and research reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

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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

6 authors.

Olav Toai Duc NguyenDepartment of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Ioannis FotopoulosDepartment of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece.
Maria MarkakiDepartment of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece.
Ioannis TsamardinosDepartment of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece.
Vincenzo LaganiBiological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Oluf Dimitri RøeDepartment of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Improving the method for selecting participants for lung cancer (LC) screening is an urgent need. Here, we compared the performance of the Helseundersøkelsen i Nord-Trøndelag (HUNT) Lung Cancer Model (HUNT LCM) versus the Dutch-Belgian lung cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON)) and 2021 United States Preventive Services Task Force (USPSTF) criteria regarding LC risk prediction and efficiency. Methods: We used linked data from 10 Norwegian prospective population-based cohorts, Cohort of Norway. The study included 44,831 ever-smokers, of which 686 (1.5%) patients developed LC; the median follow-up time was 11.6 years (0.01-20.8 years). Results: Within 6 years, 222 (0.5%) individuals developed LC. The NELSON and 2021 USPSTF criteria predicted 37.4% and 59.5% of the LC cases, respectively. By considering the same number of individuals as the NELSON and 2021 USPSTF criteria selected, the HUNT LCM increased the LC prediction rate by 41.0% and 12.1%, respectively. The HUNT LCM significantly increased sensitivity ( Conclusions: The HUNT LCM was significantly more efficient than the NELSON and 2021 USPSTF criteria, improving the prediction of LC diagnosis, and may be used as a validated clinical tool for screening selection.

Indexed as

CONORHUNTLung cancer screeningRisk prediction modelsScreenee selection

Identifiers

PMID38586302
PMCPMC10998221

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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.