Evidence map›Paper›PMID 37219444›Full record

ReviewRadiology2023

Opportunistic Screening:

Perry J Pickhardt, Ronald M Summers, John W Garrett, Arun Krishnaraj, Sheela Agarwal, Keith J Dreyer, Gregory N Nicola

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

60 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Review
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  9. Observational
  10. Multi-center validation of automated CT-based L1 vertebral Hounsfield unit.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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  11. Review
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  14. Preventing fragility fractures: AI-based opportunistic osteoporosis detection in routine CT ​.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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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

7 authors.

Perry J PickhardtFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0002-5534-8202
Ronald M SummersFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0001-8081-7376
John W GarrettFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0002-8152-736X
Arun KrishnarajFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).
Sheela AgarwalFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0001-5834-0479
Keith J DreyerFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0003-1207-6443
Gregory N NicolaFrom the Departments of Radiology (P.J.P., J.W.G.) and Medical Physics (J.W.G.), University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Va (A.K.); Lenox Hill Radiology, New York, NY (S.A.); Harvard Medical School and Mass General Brigham, Boston, Mass (K.J.D.); and Hackensack Radiology Group, Hackensack, NJ (G.N.N.).ORCID 0000-0003-3959-2069

Funding

Can machines be trusted? Robustification of deep learning for medical imagingR01LM013151 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI GARRETT, JOHN WILLIAM, MCMILLAN, ALAN BLAIR · 2020 to 2023
$1.3M
Computer Aided Detection for CT ColonographyZIACL040003 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
Computer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
NLM NIH HHS R01 LM013151
6 · The paper itself

Abstract

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.

Indexed as

Artificial IntelligenceRadiologyAlgorithmsHumansMass ScreeningRadiologists

Identifiers

PMID37219444
PMCPMC10315516

What OpenQuestion holds

Textmetadata
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.