Evidence map›Paper›PMID 39076687›Full record

ArticleResearch in diagnostic and interventional imaging2023

Potential added value of an AI software with prediction of malignancy for the management of incidental lung nodules.

Bastien Michelin, Aïssam Labani, Pascal Bilbault, Catherine Roy, Mickaël Ohana

Abstract read
In one paragraph

Article in Research in diagnostic and interventional imaging, 2023. 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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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bastien MichelinDepartment of Diagnostic Imaging (Radio B), Hôpitaux universitaires de Strasbourg, Strasbourg 67000, France.
Aïssam LabaniDepartment of Diagnostic Imaging (Radio B), Hôpitaux universitaires de Strasbourg, Strasbourg 67000, France.
Pascal BilbaultEmergency Department, Hpitaux universitaires de Strasbourg, Strasbourg 67000, France.
Catherine RoyDepartment of Diagnostic Imaging (Radio B), Hôpitaux universitaires de Strasbourg, Strasbourg 67000, France.
Mickaël OhanaDepartment of Diagnostic Imaging (Radio B), Hôpitaux universitaires de Strasbourg, Strasbourg 67000, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To determine the impact of an artificial intelligence software predicting malignancy in the management of incidentally discovered lung nodules. Materials and methods: In this retrospective study, all lung nodules ≥ 6 mm and ≤ 30 mm incidentally discovered on emergency CT scans performed between June 1, 2017 and December 31, 2017 were assessed. Artificial intelligence software using deep learning algorithms was applied to determine their likelihood of malignancy: most likely benign (AI score < 50%), undetermined (AI score 50-75%) or probably malignant (AI score > 75%). Predictions were compared to two-year follow-up and Brock's model. Results: Ninety incidental pulmonary nodules in 83 patients were retrospectively included. 36 nodules were benign, 13 were malignant and 41 remained indeterminate at 2 years follow-up.AI analysis was possible for 81/90 nodules. The 34 benign nodules had an AI score between 0.02% and 96.73% (mean = 48.05 ± 37.32), while the 11 malignant nodules had an AI score between 82.89% and 100% (mean = 93.9 ± 2.3). The diagnostic performance of the AI software for positive diagnosis of malignant nodules using a 75% malignancy threshold was: sensitivity = 100% [95% CI 72%-100%]; specificity = 55.8% [38-73]; PPV = 42.3% [23-63]; NPV = 100% [82-100]. With its apparent high NPV, the addition of an AI score to the initial CT could have avoided a guidelines-recommended follow-up in 50% of the benign pulmonary nodules (6/12 nodules). Conclusion: Artificial intelligence software using deep learning algorithms presents a strong NPV (100%, with a 95% CI 82-100), suggesting potential use for reducing the need for follow-up of nodules categorized as benign.

Indexed as

Artificial intelligenceDeep learningLung cancerLung noduleRadiomics

Identifiers

PMID39076687
PMCPMC11265191

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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.