Evidence map›Paper›PMID 37903855›Full record

ArticleScientific reports2023

Role of sex in lung cancer risk prediction based on single low-dose chest computed tomography.

Judit Simon, Peter Mikhael, Ismail Tahir, Alexander Graur, Stefan Ringer, Amanda Fata, Yang Chi-Fu Jeffrey, Jo-Anne Shepard, Francine Jacobson, Regina Barzilay and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 15% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
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

13 authors at 4 institutions in 1 country.

Judit Simon *Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Peter Mikhael *Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Ismail TahirDivision of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Alexander GraurDivision of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Stefan RingerDivision of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Amanda FataDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Yang Chi-Fu JeffreyHarvard Medical School, Boston, MA, USA.
Jo-Anne ShepardDivision of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
Francine JacobsonHarvard Medical School, Boston, MA, USA.
Regina BarzilayDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Lecia V SequistHarvard Medical School, Boston, MA, USA.
Lydia E PaceHarvard Medical School, Boston, MA, USA.
Florian J FintelmannDivision of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. fintelmann@mgh.harvard.edu.
Massachusetts General Hospital · USHarvard University · USBrigham and Women's Hospital · USMassachusetts Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A validated open-source deep-learning algorithm called Sybil can accurately predict long-term lung cancer risk from a single low-dose chest computed tomography (LDCT). However, Sybil was trained on a majority-male cohort. Use of artificial intelligence algorithms trained on imbalanced cohorts may lead to inequitable outcomes in real-world settings. We aimed to study whether Sybil predicts lung cancer risk equally regardless of sex. We analyzed 10,573 LDCTs from 6127 consecutive lung cancer screening participants across a health system between 2015 and 2021. Sybil achieved AUCs of 0.89 (95% CI: 0.85-0.93) for females and 0.89 (95% CI: 0.85-0.94) for males at 1 year, p = 0.92. At 6 years, the AUC was 0.87 (95% CI: 0.83-0.93) for females and 0.79 (95% CI: 0.72-0.86) for males, p = 0.01. In conclusion, Sybil can accurately predict future lung cancer risk in females and males in a real-world setting and performs better in females than in males for predicting 6-year lung cancer risk.

Indexed as

Lung NeoplasmsArtificial IntelligenceEarly Detection of CancerFemaleHumansMaleRiskTomography, X-Ray Computed

Identifiers

PMID37903855
PMCPMC10616081
OpenAlexW4388021581

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.