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Cited by 20Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentAI in cancer detection

AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening

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Marcinkiewicz, Anna M., Buchwald, Mikolaj, Shanbhag, Aakash, Bednarski, Bryan P., Killekar, Aditya, Miller, Robert J.H., Builoff, Valerie, Lemley, Mark +3 more·2024·Radiology
DOI 10.1148/radiol.240541PMC PMC11427857
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Abstract

A fully automated artificial intelligence model integrated multistructure segmentation, feature extraction, and quantification methods from chest CT scans to identify high-risk extrapulmonary features and predict mortality.

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  • Radiomics-based approaches sought to refine risk stratification by extracting higher-dimensional imaging features beyond the Agatston score, including plaque texture, spatial distribution, density characteristics and lesion morphology.

    “Radiomics-based approaches sought to further refine risk stratification by extracting higher-dimensional imaging features beyond the Agatston score, including plaque texture, spatial distribution, density characteristics and lesion morphology.252635–37”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [25]

    The Methods section reports extracting 15 radiomic features from each of 32 CT-visible structures, and the Discussion links quantitative imaging features, including coronary artery calcium, to mortality prediction. The paper does not establish the claimed comparison with the Agatston score or specify plaque texture, spatial distribution, density characteristics, and lesion morphology as the features used for that purpose. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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References 24 · Cited by 20

Research works printed but not linked: 22

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  15. [17]17. Miller RJH, Shanbhag A, Killekar A, et al. AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. NPJ Digit Med 2024;7(1):24.
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Authors
Marcinkiewicz, A. M., Buchwald, M., Shanbhag, A., Bednarski, B. P., Killekar, A., Miller, R. J. H., Builoff, V., Lemley, M., Berman, D. S., Dey, D., & Slomka, P. J.
Affiliations
  • University of Southern California (US)
  • From the Departments of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Los Angeles, CA 90048 (A.M.M., M.B., A.S., B.P.B., A.K., R.J.H.M., V.B., M.L., D.S.B., D.D., P.J.S.); Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, Calif (A.S.); and Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada (R.J.H.M.).
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2024-09-01
Volume
312
Issue
3
Pages
e240541-e240541
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.240541
PMC
PMC11427857
OpenAlex
W4402556846
Cited by
20
References
24
Citations by year
2026: 8 · 2025: 8 · 2024: 4
FWCI
5.10
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Radiology (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion)
Funders
  • National Institutes of Health (R35HL161195)
  • National Heart, Lung, and Blood Institute (R35HL161195)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
lung cancer screening, incidental finding, mortality prediction, chest CT, artificial intelligence, high-risk features
MeSH terms
Aged, Artificial Intelligence, Female, Humans, Lung, Lung Neoplasms, Male, Middle Aged, Radiography, Thoracic, Tomography, X-Ray Computed, Incidental Findings, Early Detection of Cancer

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