Evidence map›Paper›PMID 35065909›Full record

SynthesisJournal of the American College of Radiology : JACR2022

Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review.

Anna W Anderson, M Luke Marinovich, Nehmat Houssami, Kathryn P Lowry, Joann G Elmore, Diana S M Buist, Solveig Hofvind, Christoph I Lee

Open access · greenAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of the American College of Radiology : JACR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 pooled it
3.0field-weighted citation impact, top 8% 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

34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.

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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

8 authors at 6 institutions in 5 countries.

Anna W AndersonDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington.
M Luke MarinovichCurtin School of Population Health, Curtin University, Bentley, Australia.
Nehmat HoussamiThe Daffodil Centre, University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia; NBCF Chair in Breast Cancer Prevention, University of Sydney, Sydney, Australia; Coeditor, The Breast.
Kathryn P LowryDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington.
Joann G ElmoreDavid Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California; Director, UCLA's National Clinician Scholars Program, University of California, Los Angeles, Los Angeles, California; Editor-in-Chief of Adult Primary Care, UpToDate.
Diana S M BuistKaiser Permanente Washington Health Research Institute, Seattle, Washington; Director of Research and Strategic Partnerships, Kaiser Permanente Washington Health Research Institute, Seattle, Washington.
Solveig HofvindSection Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
Christoph I LeeDepartment of Radiology, University of Washington School of Medicine, Seattle, Washington; Director, Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington, Seattle, Washington; Deputy Editor, JACR. Electronic address: stophlee@uw.edu.
University of Washington · USCancer Registry of Norway · NOCurtin University · AUKaiser Permanente Washington Health Research Institute · USThe University of Sydney · AUUniversity of California, Los Angeles · US

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical TranslationR37CA240403 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI CHRISTOPH I LEE · 2020 to 2026
$3.8M
NCI NIH HHS P01 CA154292NCI NIH HHS R37 CA240403
6 · The paper itself

Abstract

purposeThe aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography.

methodsA systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

resultsAfter data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage.

conclusionsTo date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.

Indexed as

Artificial IntelligenceBreast NeoplasmsAlgorithmsEarly Detection of CancerFemaleHumansMammographyRetrospective Studies

Identifiers

PMID35065909
PMCPMC8857031
OpenAlexW4206777594

What OpenQuestion holds

Textmetadata
LicenceTDM
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.