Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 4% 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
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
10 authors at 3 institutions in 1 country.
Jiye G KimFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0000-0002-5232-7748
Bryan HaslamFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0009-0005-1546-7433
Abdul Rahman DiabFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0009-0007-2273-2658
Ashwin SakhareFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0009-0002-2409-2109
Giorgia GrisotFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0000-0003-4349-1201
Hyunkwang LeeFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0000-0002-4806-6709
Jacqueline HoltFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0009-0008-4361-5001
Christoph I LeeFrom DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).
William Lotter *From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0000-0002-9427-8833
A Gregory Sorensen *From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.).ORCID 0000-0001-7750-8206
Fred Hutch Cancer Center · USHarvard University · USUniversity of Washington · 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
Towards Clinically Effective AI for Screening MammographyR44CA240022 · NCI · DEEPHEALTH, INC. · PI LOTTER, WILLIAM EDWARD · 2019 to 2021
Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08;
Indexed as
Breast NeoplasmsArtificial IntelligenceEarly Detection of CancerFemaleHumansMammographyMiddle AgedRadiologistsRetrospective StudiesArtificial IntelligenceBreast CancerComputer-aided DiagnosisConvolutional Neural Network (CNN)Digital Breast TomosynthesisScreeningScreening Mammography
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.
Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. · full record | OpenQuestion