Evidence map›Paper›PMID 37192186›Full record

ArticlePloS one2023

Fully automated calcium scoring predicts all-cause mortality at 12 years in the MILD lung cancer screening trial.

Federica Sabia, Maurizio Balbi, Roberta E Ledda, Gianluca Milanese, Margherita Ruggirello, Camilla Valsecchi, Alfonso Marchianò, Nicola Sverzellati, Ugo Pastorino

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.7field-weighted citation impact, top 7% 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

9 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?European respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  9. Long-term outcomes of lung cancer screening in males and females.Lung cancer (Amsterdam, Netherlands) · 2023
    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

9 authors at 2 institutions in 1 country.

Federica SabiaDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Maurizio BalbiDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.ORCID 0000-0002-8436-3655
Roberta E LeddaDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Gianluca MilaneseDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Margherita RuggirelloDepartment of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Camilla ValsecchiDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Alfonso MarchianòDepartment of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Nicola SverzellatiSection of Radiology, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, Parma, Italy.
Ugo PastorinoDepartment of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.ORCID 0000-0001-9974-7902
Fondazione IRCCS Istituto Nazionale dei Tumori · ITUniversity of Parma · IT

Funding

Identifying non-coding RNAs for early detection and prevention of lung cancerU01CA166905 · NCI · OHIO STATE UNIVERSITY · PI CROCE, CARLO M, PASS, HARVEY IRA · 2013 to 2017
$4.5M
NCI NIH HHS U01 CA166905
6 · The paper itself

Abstract

Coronary artery calcium (CAC) is a known risk factor for cardiovascular (CV) events and mortality but is not yet routinely evaluated in low-dose computed tomography (LDCT)-based lung cancer screening (LCS). The present analysis explored the capacity of a fully automated CAC scoring to predict 12-year mortality in the Multicentric Italian Lung Detection (MILD) LCS trial. The study included 2239 volunteers of the MILD trial who underwent a baseline LDCT from September 2005 to January 2011, with a median follow-up of 190 months. The CAC score was measured by a commercially available fully automated artificial intelligence (AI) software and stratified into five strata: 0, 1-10, 11-100, 101-400, and > 400. Twelve-year all-cause mortality was 8.5% (191/2239) overall, 3.2% with CAC = 0, 4.9% with CAC = 1-10, 8.0% with CAC = 11-100, 11.5% with CAC = 101-400, and 17% with CAC > 400. In Cox proportional hazards regression analysis, CAC > 400 was associated with a higher 12-year all-cause mortality both in a univariate model (hazard ratio, HR, 5.75 [95% confidence interval, CI, 2.08-15.92] compared to CAC = 0) and after adjustment for baseline confounders (HR, 3.80 [95%CI, 1.35-10.74] compared to CAC = 0). All-cause mortality significantly increased with increasing CAC (7% in CAC ≤ 400 vs. 17% in CAC > 400, Log-Rank p-value <0.001). Non-cancer at 12 years mortality was 3% (67/2239) overall, 0.8% with CAC = 0, 1.0% with CAC = 1-10, 2.9% with CAC = 11-100, 3.6% with CAC = 101-400, and 8.2% with CAC > 400 (Grey's test p < 0.001). In Fine and Gray's competing risk model, CAC > 400 predicted 12-year non-cancer mortality in a univariate model (sub-distribution hazard ratio, SHR, 10.62 [95% confidence interval, CI, 1.43-78.98] compared to CAC = 0), but the association was no longer significant after adjustment for baseline confounders. In conclusion, fully automated CAC scoring was effective in predicting all-cause mortality at 12 years in a LCS setting.

Indexed as

Coronary Artery DiseaseLung NeoplasmsVascular CalcificationArtificial IntelligenceCalciumEarly Detection of CancerHumansRisk AssessmentRisk FactorsCalcium

Identifiers

PMID37192186
PMCPMC10187890
OpenAlexW4376872553

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.