Evidence map›Paper›PMID 42064402›Full record

ArticleBJR artificial intelligence2024

Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice-a simulation study.

Mubarak Patel, Peter Auguste, Jason Madan, Hesam Ghiasvand, Julia Geppert, Asra Asgharzadeh, Emma Helm, Yen-Fu Chen, Daniel Gallacher

Abstract read
In one paragraph

Article in BJR artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mubarak PatelApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Peter AugusteApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID https://orcid.org/0000-0001-5143-3218
Jason MadanApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID https://orcid.org/0000-0003-4316-1480
Hesam GhiasvandResearch Centre for Healthcare & Communities, Research Institute for Health and Wellbeing, Coventry University, Coventry, CV1 5FB, United Kingdom.
Julia GeppertApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Asra AsgharzadehBristol Medical School, University of Bristol, Bristol, BS8 2BN, United Kingdom.ORCID https://orcid.org/0000-0002-1068-8537
Emma HelmRadiology, University Hospitals Coventry and Warwickshire, Coventry, CV2 2DX, United Kingdom.
Yen-Fu ChenApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID https://orcid.org/0000-0002-9446-2761
Daniel GallacherApplied Health, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID https://orcid.org/0000-0003-0506-9384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Accurate measurement of lung nodules is pivotal to lung cancer detection and management. Nodule size forms the main basis of risk categorization in existing guidelines. However, measurements can be highly variable between manual readers. This article explores the impact of potentially improved nodule size measurement assisted by generic artificial intelligence (AI)-derived software on clinical management compared with manual measurement. Methods: The simulation study created a baseline cohort of people with lung nodules, guided by nodule size distributions reported in the literature. Precision and accuracy were simulated to emulate measurement of nodule size by radiologists with and without the assistance of AI-derived software and by the software alone. Nodule growth was modelled over a 4-year time frame, allowing evaluation of management strategies based on existing clinical guidelines. Results: Measurement assisted by AI-derived software increased cancer detection compared to an unassisted radiologist for a combined solid and sub-solid nodule population (62.5% vs 61.4%). AI-assisted measurement also correctly identified more benign nodules (95.8% vs 95.4%); however, it was associated with over an additional month of surveillance on average (5.12 vs 3.95 months). On average, with AI assistance people with cancer are diagnosed faster, and people without cancer are monitored longer. Conclusions: In this simulation, the potential benefits of improved accuracy and precision associated with AI-based diameter measurement is associated with additional monitoring of non-cancerous nodules. AI may offer additional benefits not captured in this simulation, and it is important to generate data supporting these, and adjust guidelines as necessary. Advances in knowledge: This article shows the effects of greater measurement accuracy associated with AI assistance compared with unassisted measurement.

Indexed as

artificial intelligencechest CTlung nodulemeasurement accuracynodule growthprecisionsimulation

Identifiers

PMID42064402
PMCPMC13045710

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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.