51
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Arzamasov K, Vasilev Y, Vladzymyrskyy A, Omelyanskaya O, Shulkin I, Kozikhina D, Goncharova I, Gelezhe P, Kirpichev Y, Bobrovskaya T, Andreychenko A. An International Non-Inferiority Study for the Benchmarking of AI for Routine Radiology Cases: Chest X-ray, Fluorography and Mammography. Healthcare (Basel) 2023; 11:1684. [PMID: 37372802 DOI: 10.3390/healthcare11121684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83-0.9) versus 0.96 (95% CI 0.94-0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64-0.78) versus 0.91 (95% CI 0.86-0.95) and 0.93 (95% CI 0.89-0.96) versus 0.9 (95% CI 0.85-0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.
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Affiliation(s)
- Kirill Arzamasov
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Yuriy Vasilev
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
- Federal State Budgetary Institution "National Medical and Surgical Center Named after N.I. Pirogov" of the Ministry of Health of the Russian Federation, Nizhnyaya Pervomayskaya Street, 70, 105203 Moscow, Russia
| | - Anton Vladzymyrskyy
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
- Department of Information and Internet Technologies, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Trubetskaya Street, 8, Building 2, 119991 Moscow, Russia
| | - Olga Omelyanskaya
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Igor Shulkin
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Darya Kozikhina
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Inna Goncharova
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Pavel Gelezhe
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Yury Kirpichev
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Tatiana Bobrovskaya
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
| | - Anna Andreychenko
- State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", Petrovka Street, 24, Building 1, 127051 Moscow, Russia
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52
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Arasu VA, Habel LA, Achacoso NS, Buist DSM, Cord JB, Esserman LJ, Hylton NM, Glymour MM, Kornak J, Kushi LH, Lewis DA, Liu VX, Lydon CM, Miglioretti DL, Navarro DA, Pu A, Shen L, Sieh W, Yoon HC, Lee C. Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study. Radiology 2023; 307:e222733. [PMID: 37278627 PMCID: PMC10315521 DOI: 10.1148/radiol.222733] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/18/2023] [Indexed: 06/07/2023]
Abstract
Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Vignesh A. Arasu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laurel A. Habel
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Ninah S. Achacoso
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana S. M. Buist
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Jason B. Cord
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laura J. Esserman
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Nola M. Hylton
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - M. Maria Glymour
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - John Kornak
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Lawrence H. Kushi
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Donald A. Lewis
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Vincent X. Liu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Caitlin M. Lydon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana L. Miglioretti
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Daniel A. Navarro
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Albert Pu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Li Shen
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Weiva Sieh
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Hyo-Chun Yoon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Catherine Lee
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
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53
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Chen JL, Cheng LH, Wang J, Hsu TW, Chen CY, Tseng LM, Guo SM. A YOLO-based AI system for classifying calcifications on spot magnification mammograms. Biomed Eng Online 2023; 22:54. [PMID: 37237394 DOI: 10.1186/s12938-023-01115-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
OBJECTIVES Use of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies. METHODS In this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis. RESULTS We included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868-0.908), with a sensitivity of 88.4% (95% CI 86.9-8.99%), specificity of 80.8% (95% CI 77.6-84%), and an accuracy of 84.6% (95% CI 81.8-87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided. CONCLUSION The AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.
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Affiliation(s)
- Jian-Ling Chen
- Department of Radiology, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City, 220, Taiwan
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei City, 112, Taiwan
| | - Lan-Hsin Cheng
- Institute of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Rd., Tainan City, 701, Taiwan
| | - Jane Wang
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei City, 112, Taiwan
- Department of Radiology, National Taiwan University College of Medicine, No. 1, Jenai Rd., Taipei City, 100, Taiwan
- Department of Nurse-Midwifery and Women Health, and School of Nursing, College of Nursing, National Taipei University of Nursing and Health Sciences, No. 365, Mingde Rd., Beitou Dist., Taipei City, 112, Taiwan
| | - Tun-Wei Hsu
- Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei City, 112, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei City, 112, Taiwan
| | - Chin-Yu Chen
- Department of Radiology, Chi-Mei Medical Center, No. 901, Zhonghua Rd. Yongkang Dist., Tainan City, 710, Taiwan
| | - Ling-Ming Tseng
- Comprehensive Breast Health Center, Taipei-Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei, 112, Taiwan
- Department of Surgery, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., Taipei, 112, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei, 112, Taiwan
| | - Shu-Mei Guo
- Institute of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Rd., Tainan City, 701, Taiwan.
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Ng AY, Glocker B, Oberije C, Fox G, Sharma N, James JJ, Ambrózay É, Nash J, Karpati E, Kerruish S, Kecskemethy PD. Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload. JOURNAL OF BREAST IMAGING 2023; 5:267-276. [PMID: 38416889 DOI: 10.1093/jbi/wbad010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. METHODS Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. RESULTS Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). CONCLUSION The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.
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Affiliation(s)
- Annie Y Ng
- Kheiron Medical Technologies, London, UK
| | - Ben Glocker
- Kheiron Medical Technologies, London, UK
- Imperial College London, Department of Computing, London, UK
| | | | | | - Nisha Sharma
- Leeds Teaching Hospital NHS Trust, Department of Radiology, Leeds, UK
| | - Jonathan J James
- Nottingham University Hospitals NHS Trust, Nottingham Breast Institute, Nottingham, UK
| | - Éva Ambrózay
- MaMMa Egészségügyi Zrt., Breast Diagnostic Department, Kecskemét, Hungary
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55
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Nguyen HT, Nguyen HQ, Pham HH, Lam K, Le LT, Dao M, Vu V. VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography. Sci Data 2023; 10:277. [PMID: 37173336 PMCID: PMC10182079 DOI: 10.1038/s41597-023-02100-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/24/2023] [Indexed: 05/15/2023] Open
Abstract
Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.
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Affiliation(s)
| | - Ha Q Nguyen
- Institute of Big Data, Hanoi, Vietnam
- College of Engineering and Computer Science (CECS), VinUniversity, Hanoi, Vietnam
| | - Hieu H Pham
- Institute of Big Data, Hanoi, Vietnam.
- College of Engineering and Computer Science (CECS), VinUniversity, Hanoi, Vietnam.
- VinUni-Illinois Smart Health Center, Hanoi, Vietnam.
| | - Khanh Lam
- Hospital 108, Department of Radiology, Hanoi, Vietnam
| | - Linh T Le
- Hanoi Medical University Hospital, Department of Radiology, Hanoi, Vietnam
| | - Minh Dao
- Institute of Big Data, Hanoi, Vietnam
| | - Van Vu
- Institute of Big Data, Hanoi, Vietnam
- Yale University, Department of Mathematics, New Heaven, CT, 06511, USA
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Arce S, Vijay A, Yim E, Spiguel LR, Hanna M. Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography. Cureus 2023; 15:e38770. [PMID: 37303390 PMCID: PMC10249706 DOI: 10.7759/cureus.38770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Early breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival. This study aims to evaluate the ability of an artificial intelligence computer-aided detection (AI CAD) system to detect biopsy-proven invasive lobular carcinoma (ILC) on digital mammography. Methods This retrospective study reviewed mammograms of patients who were diagnosed with biopsy-proved ILC between January 1, 2017, and January 1, 2022. All mammograms were analyzed using cmAssist® (CureMetrix, San Diego, California, United States), which is an AI CAD for mammography. The AI CAD sensitivity for detecting ILC on mammography was calculated and further subdivided by lesion type, mass shape, and mass margins. To account for the within-subject correlation, generalized linear mixed models were implemented to investigate the association between age, family history, and breast density and whether the AI detected a false positive or true positive. Odds ratios, 95% confidence intervals, and p-values were also calculated. Results A total of 124 patients with 153 biopsy-proven ILC lesions were included. The AI CAD detected ILC on mammography with a sensitivity of 80%. The AI CAD had the highest sensitivity for detecting calcifications (100%), masses with irregular shape (82%), and masses with spiculated margins (86%). However, 88% of mammograms had at least one false positive mark with an average number of 3.9 false positive marks per mammogram. Conclusion The AI CAD system evaluated was successful in marking the malignancy in digital mammography. However, the numerous annotations confounded the ability to determine its overall accuracy and this reduces its potential use in real-life practice.
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Affiliation(s)
- Sylvia Arce
- Department of Radiology, University of Florida College of Medicine, Gainesville, USA
| | - Arunima Vijay
- Department of Radiology, University of Florida College of Medicine, Gainesville, USA
| | - Eunice Yim
- Department of Radiology, University of Florida College of Medicine, Gainesville, USA
| | - Lisa R Spiguel
- Department of Surgery, University of Florida College of Medicine, Gainesville, USA
| | - Mariam Hanna
- Department of Radiology, University of Florida College of Medicine, Gainesville, USA
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Noor J, Chaudhry A, Batool S. Microfluidic Technology, Artificial Intelligence, and Biosensors As Advanced Technologies in Cancer Screening: A Review Article. Cureus 2023; 15:e39634. [PMID: 37388583 PMCID: PMC10305590 DOI: 10.7759/cureus.39634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 07/01/2023] Open
Abstract
Cancer screening techniques aim to detect premalignant lesions and enable early intervention to delay the onset of cancer while keeping incidence constant. Technology advancements have led to the development of powerful tools such as microfluidic technology, artificial intelligence, machine learning algorithms, and electrochemical biosensors to aid in early cancer detection. Non-invasive cancer screening methods like virtual colonoscopy and endoscopic ultrasonography have also been developed to provide comprehensive pictures of organs and detect cancer early. This review article provides an overview of recent advances in cancer screening in microfluidic technology, artificial intelligence, and biomarkers through a narrative literature search. Microfluidic devices enable easy handling of sub-microliter volumes and have become a promising tool for cancer detection, drug screening, and modeling angiogenesis and metastasis in cancer research. Machine learning and artificial intelligence have shown high accuracy in oncology-related diagnostic imaging, reducing the manual steps in lesion detection and providing standardized and accurate results, with potential for global standardization in areas like colon polyps, breast cancer, and primary and metastatic brain cancer. A biomarker-based cancer diagnosis is promising for early detection and effective therapy, and electrochemical biosensors integrated with nanoparticles offer multiplexing and amplification capabilities. Understanding these advanced technologies' basics, achievements, and challenges is crucial for advancing their use in oncology.
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Affiliation(s)
- Jawad Noor
- Internal Medicine, St. Dominic Hospital, Jackson, USA
| | | | - Saima Batool
- Pathology, Nishtar Medical University, Multan, PAK
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Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. J Med Internet Res 2023; 25:e43832. [PMID: 36862499 PMCID: PMC10020907 DOI: 10.2196/43832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
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Affiliation(s)
- Peng Xue
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongxu Qin
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingrui Wei
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Zichen Ye
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sumeng Wang
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Ding
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenling Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anying Bai
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Dang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuqian Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science & Technology of China, Sichuan, China
| | - Remila Rezhake
- Affiliated Cancer Hospital, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University, Xinjiang, China
| | - Shaokai Zhang
- Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Henan, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yimin Qu
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Cushnan D, Young KC, Ward D, Halling-Brown MD, Duffy S, Given-Wilson R, Wallis MG, Wilkinson L, Lyburn I, Sidebottom R, McAvinchey R, Lewis EB, Mackenzie A, Warren LM. Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set. Br J Radiol 2023; 96:20211104. [PMID: 36607283 PMCID: PMC9975375 DOI: 10.1259/bjr.20211104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE To pilot a process for the independent external validation of an artificial intelligence (AI) tool to detect breast cancer using data from the NHS breast screening programme (NHSBSP). METHODS A representative data set of mammography images from 26,000 women attending 2 NHS screening centres, and an enriched data set of 2054 positive cases were used from the OPTIMAM image database. The use case of the AI tool was the replacement of the first or second human reader. The performance of the AI tool was compared to that of human readers in the NHSBSP. RESULTS Recommendations for future external validations of AI tools to detect breast cancer are provided. The tool recalled different breast cancers to the human readers. This study showed the importance of testing AI tools on all types of cases (including non-standard) and the clarity of any warning messages. The acceptable difference in sensitivity and specificity between the AI tool and human readers should be determined. Any information vital for the clinical application should be a required output for the AI tool. It is recommended that the interaction of radiologists with the AI tool, and the effect of the AI tool on arbitration be investigated prior to clinical use. CONCLUSION This pilot demonstrated several lessons for future independent external validation of AI tools for breast cancer detection. ADVANCES IN KNOWLEDGE Knowledge has been gained towards best practice procedures for performing independent external validations of AI tools for the detection of breast cancer using data from the NHS Breast Screening Programme.
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Affiliation(s)
| | | | - Dominic Ward
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | | | - Stephen Duffy
- Queen Mary University London, London, United Kingdom
| | | | - Matthew G Wallis
- Cambridge Breast Unit and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Trust, Cambridge, United Kingdom
| | - Louise Wilkinson
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | | | | | - Emma B Lewis
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | | | - Lucy M Warren
- Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
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Barros V, Tlusty T, Barkan E, Hexter E, Gruen D, Guindy M, Rosen-Zvi M. Virtual Biopsy by Using Artificial Intelligence-based Multimodal Modeling of Binational Mammography Data. Radiology 2023; 306:e220027. [PMID: 36283109 DOI: 10.1148/radiol.220027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of biopsy procedures, remains a challenge. Purpose To develop an AI-based model to identify breast lesion subtypes with mammograms and linked electronic health records labeled with histopathologic information. Materials and Methods In this retrospective study, 26 569 images were collected in 9234 women who underwent digital mammography to pretrain the algorithms. The training data included individuals who had at least 1 year of clinical and imaging history followed by biopsy-based histopathologic diagnosis from March 2013 to November 2018. A model that combined convolutional neural networks with supervised learning algorithms was independently trained to make breast lesion predictions with data from 2120 women in Israel and 1642 women in the United States. Results were reported using the area under the receiver operating characteristic curve (AUC) with the 95% DeLong approach to estimate CIs. Significance was tested with bootstrapping. Results The Israeli model was validated in 456 women and tested in 441 women (mean age, 51 years ± 11 [SD]). The U.S. model was validated in 350 women and tested in 344 women (mean age, 60 years ± 12). For predicting malignancy in the test sets (consisting of 220 Israeli patient examinations and 126 U.S. patient examinations with ductal carcinoma in situ or invasive cancer), the algorithms obtained an AUC of 0.88 (95% CI: 0.85, 0.91) and 0.80 (95% CI: 0.74, 0.85) for Israeli and U.S. patients, respectively (P = .006). These results may not hold for other cohorts of patients, and generalizability across populations should be further investigated. Conclusion The results offer supporting evidence that artificial intelligence applied to clinical and mammographic images can identify breast lesion subtypes when the data are sufficiently large, which may help assess diagnostic workflow and reduce biopsy sampling errors. Published under a CC BY 4.0 license. Online supplemental material is available for this article.
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Affiliation(s)
- Vesna Barros
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - Tal Tlusty
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - Ella Barkan
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - Efrat Hexter
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - David Gruen
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - Michal Guindy
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
| | - Michal Rosen-Zvi
- From the AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (V.B., T.T., E.B., E.H., M.R.Z.); The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel (V.B., M.R.Z.); IBM Watson Health, Cambridge, Mass (D.G.); RadPartners, Jefferson Radiology, East Hartford, Conn (D.G.); Department of Imaging, Assuta Medical Center, Tel Aviv, Israel (M.G.); and Ben-Gurion University Medical School, Be'er Sheva, Israel (M.G.)
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Cadrin-Chênevert A. Unleashing the Power of Deep Learning for Breast Cancer Detection through Open Mammography Datasets. Radiol Artif Intell 2023; 5:e220294. [PMID: 37035433 PMCID: PMC10077079 DOI: 10.1148/ryai.220294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Affiliation(s)
- Alexandre Cadrin-Chênevert
- From the CISSS Lanaudière-Medical Imaging, 200 Louis-Vadeboncoeur Saint-Charles-Borromee, Saint Charles Borromee, QC, Canada J6E 6J2
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Marinovich ML, Wylie E, Lotter W, Lund H, Waddell A, Madeley C, Pereira G, Houssami N. Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection. EBioMedicine 2023; 90:104498. [PMID: 36863255 PMCID: PMC9996220 DOI: 10.1016/j.ebiom.2023.104498] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall and workload for simulated AI-radiologist reading. METHODS External validation of a commercially-available AI algorithm in a retrospective cohort of 108,970 consecutive mammograms from a population-based screening program, with ascertained outcomes (including interval cancers by registry linkage). Area under the ROC curve (AUC), sensitivity and specificity for AI were compared with radiologists who interpreted the screens in practice. CDR and recall were estimated for simulated AI-radiologist reading (with arbitration) and compared with program metrics. FINDINGS The AUC for AI was 0.83 compared with 0.93 for radiologists. At a prospective threshold, sensitivity for AI (0.67; 95% CI: 0.64-0.70) was comparable to radiologists (0.68; 95% CI: 0.66-0.71) with lower specificity (0.81 [95% CI: 0.81-0.81] versus 0.97 [95% CI: 0.97-0.97]). Recall rate for AI-radiologist reading (3.14%) was significantly lower than for the BSWA program (3.38%) (-0.25%; 95% CI: -0.31 to -0.18; P < 0.001). CDR was also lower (6.37 versus 6.97 per 1000) (-0.61; 95% CI: -0.77 to -0.44; P < 0.001); however, AI detected interval cancers that were not found by radiologists (0.72 per 1000; 95% CI: 0.57-0.90). AI-radiologist reading increased arbitration but decreased overall screen-reading volume by 41.4% (95% CI: 41.2-41.6). INTERPRETATION Replacement of one radiologist by AI (with arbitration) resulted in lower recall and overall screen-reading volume. There was a small reduction in CDR for AI-radiologist reading. AI detected interval cases that were not identified by radiologists, suggesting potentially higher CDR if radiologists were unblinded to AI findings. These results indicate AI's potential role as a screen-reader of mammograms, but prospective trials are required to determine whether CDR could improve if AI detection was actioned in double-reading with arbitration. FUNDING National Breast Cancer Foundation (NBCF), National Health and Medical Research Council (NHMRC).
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Affiliation(s)
- M Luke Marinovich
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
| | | | - William Lotter
- Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Helen Lund
- BreastScreen WA, Perth, Western Australia, Australia
| | | | | | - Gavin Pereira
- Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Nehmat Houssami
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
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Zizaan A, Idri A. Machine learning based Breast Cancer screening: trends, challenges, and opportunities. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2172615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Asma Zizaan
- Mohammed VI Polytechnic University, Benguerir, Morocco
| | - Ali Idri
- Mohammed VI Polytechnic University, Benguerir, Morocco
- Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
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64
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Alsharif WM. The utilization of artificial intelligence applications to improve breast cancer detection and prognosis. Saudi Med J 2023; 44:119-127. [PMID: 36773967 PMCID: PMC9987701 DOI: 10.15537/smj.2023.44.2.20220611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023] Open
Abstract
Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.
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Affiliation(s)
- Walaa M. Alsharif
- From the Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah; and from the Society of Artificial Intelligence in Healthcare, Riyadh, Kingdom of Saudi Arabia.
- Address correspondence and reprint request to: Dr. Walaa M. Alsharif, Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah, Kingdom of Saudi Arabia. E-mail: ORCID ID: https//:orcid.org/0000-0001-7607-3255
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65
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Mooney SD. Technology Platforms and Approaches for Building and Evaluating Machine Learning Methods in Healthcare. J Appl Lab Med 2023; 8:194-202. [PMID: 36610427 PMCID: PMC10729736 DOI: 10.1093/jalm/jfac113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/18/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Artificial intelligence (AI) methods are becoming increasingly commonly implemented in healthcare as decision support, business intelligence tools, or, in some cases, Food and Drug Administration-approved clinical decision-makers. Advanced lab-based diagnostic tools are increasingly becoming AI driven. The path from data to machine learning methods is an active area for research and quality improvement, and there are few established best practices. With data being generated at an unprecedented rate, there is a need for processes that enable data science investigation that protect patient privacy and minimize other business risks. New approaches for data sharing are being utilized that lower these risks. CONTENT In this short review, clinical and translational AI governance is introduced along with approaches for securely building, sharing, and validating accurate and fair models. This is a constantly evolving field, and there is much interest in collecting data using standards, sharing data, building new models, evaluating models, sharing models, and, of course, implementing models into practice. SUMMARY AI is an active area of research and development broadly for healthcare and laboratory testing. Robust data governance and machine learning methodological governance are required. New approaches for data sharing are enabling the development of models and their evaluation. Evaluation of methods is difficult, particularly when the evaluation is performed by the team developing the method, and should ideally be prospective. New technologies have enabled standardization of platforms for moving analytics and data science methods.
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Affiliation(s)
- Sean D Mooney
- Institute for Medical Data Science and Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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66
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Jeong JJ, Vey BL, Bhimireddy A, Kim T, Santos T, Correa R, Dutt R, Mosunjac M, Oprea-Ilies G, Smith G, Woo M, McAdams CR, Newell MS, Banerjee I, Gichoya J, Trivedi H. The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images. Radiol Artif Intell 2023; 5:e220047. [PMID: 36721407 PMCID: PMC9885379 DOI: 10.1148/ryai.220047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/04/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023]
Abstract
Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.
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67
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Ziegelmayer S, Reischl S, Havrda H, Gawlitza J, Graf M, Lenhart N, Nehls N, Lemke T, Wilhelm D, Lohöfer F, Burian E, Neumann PA, Makowski M, Braren R. Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images. JAMA Netw Open 2023; 6:e2253370. [PMID: 36705919 DOI: 10.1001/jamanetworkopen.2022.53370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
IMPORTANCE Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. OBJECTIVE To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. MAIN OUTCOMES AND MEASURES To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. RESULTS A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). CONCLUSIONS AND RELEVANCE The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.
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Affiliation(s)
- Sebastian Ziegelmayer
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Stefan Reischl
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Hannah Havrda
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Joshua Gawlitza
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Markus Graf
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Nicolas Lenhart
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Nadja Nehls
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Tristan Lemke
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Dirk Wilhelm
- Department of Surgery, Technical University of Munich, School of Medicine, Munich, Germany
| | - Fabian Lohöfer
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Egon Burian
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Marcus Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
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Castro E, Costa Pereira J, Cardoso JS. Symmetry-based regularization in deep breast cancer screening. Med Image Anal 2023; 83:102690. [PMID: 36446314 DOI: 10.1016/j.media.2022.102690] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains.
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Affiliation(s)
- Eduardo Castro
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
| | - Jose Costa Pereira
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Huawei Technologies R&D, Noah's Ark Lab, Gridiron building, 1 Pancras Square, 5th floor, London N1C 4AG, United Kingdom
| | - Jaime S Cardoso
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
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Sezgin E. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. Digit Health 2023; 9:20552076231186520. [PMID: 37426593 PMCID: PMC10328041 DOI: 10.1177/20552076231186520] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently contributing to improved diagnostic accuracy, optimized treatment planning, and improved patient outcomes. The rapid evolution of AI, especially generative AI and large language models (LLMs), have reignited the discussions about their potential impact on the healthcare industry, particularly regarding the role of healthcare providers. Concerning questions, "can AI replace doctors?" and "will doctors who are using AI replace those who are not using it?" have been echoed. To shed light on this debate, this article focuses on emphasizing the augmentative role of AI in healthcare, underlining that AI is aimed to complement, rather than replace, doctors and healthcare providers. The fundamental solution emerges with the human-AI collaboration, which combines the cognitive strengths of healthcare providers with the analytical capabilities of AI. A human-in-the-loop (HITL) approach ensures that the AI systems are guided, communicated, and supervised by human expertise, thereby maintaining safety and quality in healthcare services. Finally, the adoption can be forged further by the organizational process informed by the HITL approach to improve multidisciplinary teams in the loop. AI can create a paradigm shift in healthcare by complementing and enhancing the skills of healthcare providers, ultimately leading to improved service quality, patient outcomes, and a more efficient healthcare system.
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Affiliation(s)
- Emre Sezgin
- Center for Biobehavioral Health, Abigail
Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of
Medicine, Columbus, OH, USA
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Walsh R, Tardy M. A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer. Diagnostics (Basel) 2022; 13:diagnostics13010067. [PMID: 36611358 PMCID: PMC9818528 DOI: 10.3390/diagnostics13010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy cases, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment.
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Affiliation(s)
- Ricky Walsh
- ISTIC, Campus Beaulieu, Université de Rennes 1, 35700 Rennes, France
- Hera-MI SAS, 44800 Saint-Herblain, France
- Correspondence: (R.W.); (M.T.)
| | - Mickael Tardy
- Hera-MI SAS, 44800 Saint-Herblain, France
- Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, 44000 Nantes, France
- Correspondence: (R.W.); (M.T.)
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71
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Dahlblom V, Dustler M, Tingberg A, Zackrisson S. Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence. Eur Radiol 2022; 33:3754-3765. [PMID: 36502459 PMCID: PMC10121528 DOI: 10.1007/s00330-022-09316-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 10/12/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Abstract
Objectives
Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.
Methods
An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.
Results
By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected—26% (25/95) more than DM screening (p < 0.001)—while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).
Conclusion
AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.
Key Points
• Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload.
• Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader.
• Retrospective study based on paired mammography and digital breast tomosynthesis screening data.
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Affiliation(s)
- Victor Dahlblom
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Carl-Bertil Laurells gata 9, 205 02, Malmö, Sweden.
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden.
| | - Magnus Dustler
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Carl-Bertil Laurells gata 9, 205 02, Malmö, Sweden
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Anders Tingberg
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
- Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Carl-Bertil Laurells gata 9, 205 02, Malmö, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
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72
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Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations. JAMA Intern Med 2022; 182:1306-1312. [PMID: 36342705 PMCID: PMC10623674 DOI: 10.1001/jamainternmed.2022.4969] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Importance Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain. Objectives To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system. Evidence Review Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021. Findings Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices. Conclusions and Relevance The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.
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Affiliation(s)
| | - Joseph S Ross
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
| | - Sanjay Aneja
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut
| | - Cary P Gross
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Ilana B Richman
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut
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Hsu SY, Wang CY, Kao YK, Liu KY, Lin MC, Yeh LR, Wang YM, Chen CI, Kao FC. Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography. Healthcare (Basel) 2022; 10:healthcare10122382. [PMID: 36553906 PMCID: PMC9778490 DOI: 10.3390/healthcare10122382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.
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Affiliation(s)
- Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
| | - Chi-Yuan Wang
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Yi-Kai Kao
- Division of Colorectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Ming-Chia Lin
- Department of Nuclear Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
- Department of Medical Imaging and Radiology, Shu-Zen College of Medicine and Management, Kaohsiung City 82144, Taiwan
| | - Yi-Ming Wang
- Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
- Department of Critical Care Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Chih-I Chen
- Division of Colon and Rectal Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
- Division of General Medicine Surgery, Department of Surgery, E-DA Hospital, Kaohsiung City 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung City 82445, Taiwan
- The School of Chinese Medicine for Post Baccalaureate, I-Shou University, Kaohsiung City 82445, Taiwan
- Correspondence: (C.-I.C.); (F.-C.K.)
| | - Feng-Chen Kao
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung City 82445, Taiwan
- Department of Orthopedics, E-DA Hospital, Kaohsiung City 82445, Taiwan
- Department of Orthopedics, Dachang Hospital, Kaohsiung City 82445, Taiwan
- Correspondence: (C.-I.C.); (F.-C.K.)
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74
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Lee SE, Kim GR, Yoon JH, Han K, Son WJ, Shin HJ, Moon HJ. Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography. Acta Radiol 2022; 64:1808-1815. [PMID: 36426409 DOI: 10.1177/02841851221140556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Background Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. Purpose To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. Material and Methods From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance ( P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance. Conclusion AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.
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Affiliation(s)
- Si Eun Lee
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Ga Ram Kim
- Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung Hyun Yoon
- Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiologic Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won Jeong Son
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hye Jung Shin
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hee Jung Moon
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
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75
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Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics. Eur Radiol 2022; 32:7400-7408. [PMID: 35499564 DOI: 10.1007/s00330-022-08718-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/03/2022] [Accepted: 03/02/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVE To evaluate how breast cancers are depicted by artificial intelligence-based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors. MATERIALS AND METHODS From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10. RESULTS The median abnormality score of 930 breasts was 87.4 (range 0-99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p < 0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers. CONCLUSION Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage. KEY POINTS • High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology. • Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD. • Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.
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Hsu W, Hippe DS, Nakhaei N, Wang PC, Zhu B, Siu N, Ahsen ME, Lotter W, Sorensen AG, Naeim A, Buist DSM, Schaffter T, Guinney J, Elmore JG, Lee CI. External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence. JAMA Netw Open 2022; 5:e2242343. [PMID: 36409497 PMCID: PMC9679879 DOI: 10.1001/jamanetworkopen.2022.42343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/02/2022] [Indexed: 11/23/2022] Open
Abstract
Importance With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings. Objective To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population. Design, Setting, and Participants In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammography Challenge, was used to predict the cancer status of an individual using a standard set of screening mammography images. This study was conducted using retrospective patient data collected between 2010 and 2020 from women aged 40 years and older who underwent a routine breast screening examination and participated in the Athena Breast Health Network at the University of California, Los Angeles (UCLA). Main Outcomes and Measures Performance of the challenge ensemble method (CEM) and the CEM combined with radiologist assessment (CEM+R) were compared with diagnosed ductal carcinoma in situ and invasive cancers within a year of the screening examination using performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results Evaluated on 37 317 examinations from 26 817 women (mean [SD] age, 58.4 [11.5] years), individual model AUROC estimates ranged from 0.77 (95% CI, 0.75-0.79) to 0.83 (95% CI, 0.81-0.85). The CEM model achieved an AUROC of 0.85 (95% CI, 0.84-0.87) in the UCLA cohort, lower than the performance achieved in the Kaiser Permanente Washington (AUROC, 0.90) and Karolinska Institute (AUROC, 0.92) cohorts. The CEM+R model achieved a sensitivity (0.813 [95% CI, 0.781-0.843] vs 0.826 [95% CI, 0.795-0.856]; P = .20) and specificity (0.925 [95% CI, 0.916-0.934] vs 0.930 [95% CI, 0.929-0.932]; P = .18) similar to the radiologist performance. The CEM+R model had significantly lower sensitivity (0.596 [95% CI, 0.466-0.717] vs 0.850 [95% CI, 0.766-0.923]; P < .001) and specificity (0.803 [95% CI, 0.734-0.861] vs 0.945 [95% CI, 0.936-0.954]; P < .001) than the radiologist in women with a prior history of breast cancer and Hispanic women (0.894 [95% CI, 0.873-0.910] vs 0.926 [95% CI, 0.919-0.933]; P = .004). Conclusions and Relevance This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.
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Affiliation(s)
- William Hsu
- Medical and Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at University California, Los Angeles
| | - Daniel S. Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Noor Nakhaei
- Medical and Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at University California, Los Angeles
| | - Pin-Chieh Wang
- Department of Medicine, David Geffen School of Medicine at University California, Los Angeles
| | - Bing Zhu
- Medical and Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at University California, Los Angeles
| | - Nathan Siu
- Medical Informatics Home Area, Graduate Programs in Biosciences, David Geffen School of Medicine at University California, Los Angeles, Los Angeles, California
| | - Mehmet Eren Ahsen
- Gies College of Business, University of Illinois at Urbana-Champaign
| | - William Lotter
- DeepHealth, RadNet AI Solutions, Cambridge, Massachusetts
| | | | - Arash Naeim
- Center for Systematic, Measurable, Actionable, Resilient, and Technology-driven Health, Clinical and Translational Science Institute, David Geffen School of Medicine at University California, Los Angeles
| | - Diana S. M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | | | | | - Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine at University California, Los Angeles
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Department of Health Services, University of Washington School of Public Health, Seattle
- Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Center, Seattle, Washington
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77
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Retson TA, Watanabe AT, Vu H, Chim CY. Multicenter, Multivendor Validation of an FDA-approved Algorithm for Mammography Triage. JOURNAL OF BREAST IMAGING 2022; 4:488-495. [PMID: 38416951 DOI: 10.1093/jbi/wbac046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Indexed: 03/01/2024]
Abstract
OBJECTIVE Artificial intelligence (AI)-based triage algorithms may improve cancer detection and expedite radiologist workflow. To this end, the performance of a commercial AI-based triage algorithm on screening mammograms was evaluated across breast densities and lesion types. METHODS This retrospective, IRB-exempt, multicenter, multivendor study examined 1255 screening 4-view mammograms (400 positive and 855 negative studies). Images were anonymized by providing institutions and analyzed by a commercially available AI algorithm (cmTriage, CureMetrix, La Jolla, CA) that performed retrospective triage at the study level by flagging exams as "suspicious" or not. Sensitivities and specificities with confidence intervals were derived from area under the curve (AUC) calculations. RESULTS The algorithm demonstrated an AUC of 0.95 (95% CI: 0.94-0.96) for case identification. Area under the curve held across densities (0.95) and lesion types (masses: 0.94 [95% CI: 0.92-0.96] or microcalcifications: 0.97 [95% CI: 0.96-0.99]). The algorithm has a default sensitivity of 93% (95% CI: 95.6%-90.5%) with specificity of 76.3% (95% CI: 79.2%-73.4%). To evaluate real-world performance, a sensitivity of 86.9% (95% CI: 83.6%-90.2%) was tested, as observed for practicing radiologists by the Breast Cancer Surveillance Consortium (BCSC) study. The resulting specificity was 88.5% (95% CI: 86.4%-90.7%), similar to the BCSC specificity of 88.9%, indicating performance comparable to real-world results. CONCLUSION When tested for lesion detection, an AI-based triage software can perform at the level of practicing radiologists. Drawing attention to suspicious exams may improve reader specificity and help streamline radiologist workflow, enabling faster turnaround times and improving care.
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Affiliation(s)
- Tara A Retson
- University of California School of Medicine, Department of Radiology, La Jolla, CA, USA
| | - Alyssa T Watanabe
- University of Southern California Keck School of Medicine, Department of Radiology, Los Angeles, CA, USA
- CureMetrix, Inc., La Jolla, CA, USA
| | - Hoanh Vu
- CureMetrix, Inc., La Jolla, CA, USA
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Kuo PL, Wu YJ, Wu FZ. Pros and Cons of Applying Deep Learning Automatic Scan-Range Adjustment to Low-Dose Chest CT in Lung Cancer Screening Programs. Acad Radiol 2022; 29:1552-1554. [PMID: 35410801 DOI: 10.1016/j.acra.2022.02.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 02/18/2022] [Accepted: 02/19/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Pei-Lun Kuo
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yun-Ju Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Fu-Zong Wu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Education, National Sun Yat-sen University, 70, Lien-Hai Road, Kaohsiung 80424, Taiwan.
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Garrucho L, Kushibar K, Jouide S, Diaz O, Igual L, Lekadir K. Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study. Artif Intell Med 2022; 132:102386. [PMID: 36207090 DOI: 10.1016/j.artmed.2022.102386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 08/07/2022] [Accepted: 08/19/2022] [Indexed: 11/02/2022]
Abstract
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection.
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Affiliation(s)
- Lidia Garrucho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain.
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Socayna Jouide
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Oliver Diaz
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Laura Igual
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Faculty of Mathematics and Computer Science, University of Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Barcelona, Spain
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Hendrix N, Lowry KP, Elmore JG, Lotter W, Sorensen G, Hsu W, Liao GJ, Parsian S, Kolb S, Naeim A, Lee CI. Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation. J Am Coll Radiol 2022; 19:1098-1110. [PMID: 35970474 PMCID: PMC9840464 DOI: 10.1016/j.jacr.2022.06.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown. PURPOSE To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation. MATERIALS AND METHODS Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models. RESULTS Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences. CONCLUSION Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.
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Affiliation(s)
- Nathaniel Hendrix
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington.
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - William Lotter
- Chief Technology Officer, DeepHealth Inc, RadNet AI Solutions, Cambridge, Massachusetts
| | - Gregory Sorensen
- Chief Technology Officer, DeepHealth Inc, RadNet AI Solutions, Cambridge, Massachusetts
| | - William Hsu
- Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California, Los Angeles, California; American Medical Informatics Association: Member, Governance Committee; RSNA: Deputy Editor, Radiology: Artificial Intelligence
| | - Geraldine J Liao
- Department of Radiology, Virginia Mason Medical Center, Seattle, Washington
| | - Sana Parsian
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington; Department of Radiology, Kaiser Permanente Washington, Seattle, Washington
| | - Suzanne Kolb
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington
| | - Arash Naeim
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Chief Medical Officer for Clinical Research, UCLA Health; Codirector: Clinical and Translational Science Institute and Center for SMART Health; Associate Director: Institute for Precision Health, Jonsson Comprehensive Cancer Center, Garrick Institute for Risk Sciences
| | - Christoph I Lee
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington; Department of Health Services, School of Public Health, University of Washington, Seattle, Washington; and Deputy Editor, JACR
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Bao C, Shen J, Zhang Y, Zhang Y, Wei W, Wang Z, Ding J, Han L. Evaluation of an artificial intelligence support system for breast cancer screening in Chinese people based on mammogram. Cancer Med 2022; 12:3718-3726. [PMID: 36082949 PMCID: PMC9939225 DOI: 10.1002/cam4.5231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 08/16/2022] [Accepted: 08/30/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND To evaluate the diagnostic performance of radiologists on breast cancer with or without artificial intelligence (AI) support. METHODS A retrospective study was performed. In total, 643 mammograms (average age: 54 years; female: 100%; cancer: 62.05%) were randomly allocated into two groups. Seventy-five percent of mammograms in each group were randomly selected for assessment by two independent radiologists, and the rest were read once. Half of the 71 radiologists could read mammograms with AI support, and the other half could not. Sensitivity, specificity, Youden's index, agreement rate, Kappa value, the area under the receiver operating characteristic curve (AUC) and the reading time of radiologists in each group were analyzed. RESULTS The average AUC was higher if the AI support system was used (unaided: 0.84; with AI support: 0.91; p < 0.01). The average sensitivity increased from 84.77% to 95.07% with AI support (p < 0.01), but the average specificity decreased (p = 0.07). Youden's index, agreement rate and Kappa value were larger in the group with AI support, and the average reading time was shorter (p < 0.01). CONCLUSIONS The AI support system might contribute to enhancing the diagnostic performance (e.g., higher sensitivity and AUC) of radiologists. In the future, the AI algorithm should be improved, and prospective studies should be conducted.
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Affiliation(s)
- Chengzhen Bao
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
| | - Jie Shen
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
| | - Yue Zhang
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
| | - Yan Zhang
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
| | - Wei Wei
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
| | | | | | - Lili Han
- Beijing Obstetrics and Gynecology HospitalCapital Medical University. Beijing Maternal and Child Health Care HospitalBeijingChina
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82
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Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
- *Correspondence: Meredith A. Jones,
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
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83
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Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs. J Imaging 2022; 8:jimaging8090231. [PMID: 36135397 PMCID: PMC9503015 DOI: 10.3390/jimaging8090231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/26/2022] [Accepted: 08/04/2022] [Indexed: 11/30/2022] Open
Abstract
Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.
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Raafat M, Mansour S, Kamal R, Ali HW, Shibel PE, Marey A, Taha SN, Alkalaawy B. Does artificial intelligence aid in the detection of different types of breast cancer? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00868-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Abstract
Background
On mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm.
Results
AI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography.
Conclusions
AI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.
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85
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Malliori A, Pallikarakis N. Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00693-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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86
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Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy. Cancers (Basel) 2022; 14:cancers14163848. [PMID: 36010844 PMCID: PMC9405765 DOI: 10.3390/cancers14163848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/29/2022] [Accepted: 08/04/2022] [Indexed: 11/17/2022] Open
Abstract
In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.
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87
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Inferring pediatric knee skeletal maturity from MRI using deep learning. Skeletal Radiol 2022; 51:1671-1677. [PMID: 35184211 DOI: 10.1007/s00256-022-04010-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/29/2022] [Accepted: 02/04/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Many children who undergo MR of the knee to evaluate traumatic injury may not undergo a separate dedicated evaluation of their skeletal maturity, and we wished to investigate how accurately skeletal maturity could be automatically inferred from knee MRI using deep learning to offer this additional information to clinicians. MATERIALS AND METHODS Retrospective data from 894 studies from 783 patients were obtained (mean age 13.1 years, 47% female). Coronal and sagittal sequences that were T1/PD-weighted were included and resized to 224 × 224 pixels. Data were divided into train (n = 673), tune (n = 48), and test (n = 173) sets, and children were separated across sets. The chronologic age was predicted using deep learning approaches based on a long short-term memory (LSTM) model, which took as input DenseNet-121-extracted features from all T1/PD coronal and sagittal slices. Each test case was manually assigned a bone age by two radiology residents using a reference atlas provided by Pennock and Bomar. The patient's age served as ground truth. RESULTS The error of the model's predictions for chronological age was not significantly different from that of radiology residents (model M.S.E. 1.30 vs. resident 0.99, paired t-test = 1.47, p = 0.14). Pearson correlation between model and resident prediction of chronologic age was 0.96 (p < 0.001). CONCLUSION A deep learning-based approach demonstrated ability to infer skeletal maturity from knee MR sequences that was not significantly different from resident performance and did so in less than 2% of the time required by a human expert. This may offer a method for automatically evaluating lower extremity skeletal maturity automatically as part of every MR examination.
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88
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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Taylor-Phillips S, Seedat F, Kijauskaite G, Marshall J, Halligan S, Hyde C, Given-Wilson R, Wilkinson L, Denniston AK, Glocker B, Garrett P, Mackie A, Steele RJ. UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening. Lancet Digit Health 2022; 4:e558-e565. [PMID: 35750402 DOI: 10.1016/s2589-7500(22)00088-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 10/18/2022]
Abstract
Artificial intelligence (AI) could have the potential to accurately classify mammograms according to the presence or absence of radiological signs of breast cancer, replacing or supplementing human readers (radiologists). The UK National Screening Committee's assessments of the use of AI systems to examine screening mammograms continues to focus on maximising benefits and minimising harms to women screened, when deciding whether to recommend the implementation of AI into the Breast Screening Programme in the UK. Maintaining or improving programme specificity is important to minimise anxiety from false positive results. When considering cancer detection, AI test sensitivity alone is not sufficiently informative, and additional information on the spectrum of disease detected and interval cancers is crucial to better understand the benefits and harms of screening. Although large retrospective studies might provide useful evidence by directly comparing test accuracy and spectrum of disease detected between different AI systems and by population subgroup, most retrospective studies are biased due to differential verification (ie, the use of different reference standards to verify the target condition among study participants). Enriched, multiple-reader, multiple-case, test set laboratory studies are also biased due to the laboratory effect (ie, radiologists' performance in retrospective, laboratory, observer studies is substantially different to their performance in a clinical environment). Therefore, assessment of the effect of incorporating any AI system into the breast screening pathway in prospective studies is required as it will provide key evidence for the effect of the interaction of medical staff with AI, and the impact on women's outcomes.
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Affiliation(s)
| | - Farah Seedat
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Goda Kijauskaite
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - John Marshall
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Chris Hyde
- Exeter Test Group, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | | | - Alastair K Denniston
- Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Peter Garrett
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Robert J Steele
- Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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90
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Thomassin-Naggara I, Ceugnart L, Tardivon A, Verzaux L, Balleyguier C, Taourel P, Seradour B. Intelligence artificielle : Place dans le dépistage du cancer du sein en France. Bull Cancer 2022; 109:780-785. [DOI: 10.1016/j.bulcan.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/24/2022] [Accepted: 04/11/2022] [Indexed: 01/20/2023]
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91
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Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health 2022; 4:e507-e519. [PMID: 35750400 PMCID: PMC9839981 DOI: 10.1016/s2589-7500(22)00070-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 03/11/2022] [Accepted: 04/06/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND We propose a decision-referral approach for integrating artificial intelligence (AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on the basis of its quantification of uncertainty. Algorithmic assessments with high certainty are done automatically, whereas assessments with lower certainty are referred to the radiologist. This two-part AI system can triage normal mammography exams and provide post-hoc cancer detection to maintain a high degree of sensitivity. This study aimed to evaluate the performance of this AI system on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original radiologist decision. METHODS We used a retrospective dataset consisting of 1 193 197 full-field, digital mammography studies carried out between Jan 1, 2007, and Dec 31, 2020, from eight screening sites participating in the German national breast-cancer screening programme. We derived an internal-test dataset from six screening sites (1670 screen-detected cancers and 19 997 normal mammography exams), and an external-test dataset of breast cancer screening exams (2793 screen-detected cancers and 80 058 normal exams) from two additional screening sites to evaluate the performance of an AI algorithm on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original individual radiologist decision at the point-of-screen reading ahead of the consensus conference. Different configurations of the AI algorithm were evaluated. To account for the enrichment of the datasets caused by oversampling cancer cases, weights were applied to reflect the actual distribution of study types in the screening programme. Triaging performance was evaluated as the rate of exams correctly identified as normal. Sensitivity across clinically relevant subgroups, screening sites, and device manufacturers was compared between standalone AI, the radiologist, and decision referral. We present receiver operating characteristic (ROC) curves and area under the ROC (AUROC) to evaluate AI-system performance over its entire operating range. Comparison with radiologists and subgroup analysis was based on sensitivity and specificity at clinically relevant configurations. FINDINGS The exemplary configuration of the AI system in standalone mode achieved a sensitivity of 84·2% (95% CI 82·4-85·8) and a specificity of 89·5% (89·0-89·9) on internal-test data, and a sensitivity of 84·6% (83·3-85·9) and a specificity of 91·3% (91·1-91·5) on external-test data, but was less accurate than the average unaided radiologist. By contrast, the simulated decision-referral approach significantly improved upon radiologist sensitivity by 2·6 percentage points and specificity by 1·0 percentage points, corresponding to a triaging performance at 63·0% on the external dataset; the AUROC was 0·982 (95% CI 0·978-0·986) on the subset of studies assessed by AI, surpassing radiologist performance. The decision-referral approach also yielded significant increases in sensitivity for a number of clinically relevant subgroups, including subgroups of small lesion sizes and invasive carcinomas. Sensitivity of the decision-referral approach was consistent across the eight included screening sites and three device manufacturers. INTERPRETATION The decision-referral approach leverages the strengths of both the radiologist and AI, demonstrating improvements in sensitivity and specificity surpassing that of the individual radiologist and of the standalone AI system. This approach has the potential to improve the screening accuracy of radiologists, is adaptive to the requirements of screening, and could allow for the reduction of workload ahead of the consensus conference, without discarding the generalised knowledge of radiologists. FUNDING Vara.
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Impact of artificial intelligence in breast cancer screening with mammography. Breast Cancer 2022; 29:967-977. [PMID: 35763243 PMCID: PMC9587927 DOI: 10.1007/s12282-022-01375-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 05/29/2022] [Indexed: 11/21/2022]
Abstract
Objectives To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time. Methods A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or “continuous BI-RADS 100”.
Cohen’s kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed. Results On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528–0.571) without AI and κ = 0.626, 95% CI (0.607–0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754). Conclusions When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.
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Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, Ong HY, Jagmohan P, Tan WC, Khoo VM, Wong YM, Thian YL, Baskar S, Teo EC, Algazwi DAR, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST, Hallinan JTPD. Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI. Radiology 2022; 305:160-166. [PMID: 35699577 DOI: 10.1148/radiol.220076] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.
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Affiliation(s)
- Desmond Shi Wei Lim
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Andrew Makmur
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Lei Zhu
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wenqiao Zhang
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Amanda J L Cheng
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - David Soon Yiew Sia
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sterling Ellis Eide
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Han Yang Ong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Pooja Jagmohan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wei Chuan Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Vanessa Meihui Khoo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ying Mei Wong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yee Liang Thian
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sangeetha Baskar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ee Chin Teo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Diyaa Abdul Rauf Algazwi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Qai Ven Yap
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yiong Huak Chan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Jiong Hao Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Naresh Kumar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Beng Chin Ooi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Hiroshi Yoshioka
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Swee Tian Quek
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - James Thomas Patrick Decourcy Hallinan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
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Smetherman D, Golding L, Moy L, Rubin E. The Economic Impact of AI on Breast Imaging. JOURNAL OF BREAST IMAGING 2022; 4:302-308. [PMID: 38416968 DOI: 10.1093/jbi/wbac012] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Indexed: 03/01/2024]
Abstract
This article explores the development of computer-aided detection (CAD) and artificial or augmented intelligence (AI) for breast radiology examinations and describes the current applications of AI in breast imaging. Although radiologists in other subspecialties may be less familiar with the use of these technologies in their practices, CAD has been used in breast imaging for more than two decades, as mammography CAD programs have been commercially available in the United States since the late 1990s. Likewise, breast radiologists have seen payment for CAD in mammography and breast MRI evolve over time. With the rapid expansion of AI products in radiology in recent years, many new applications for these technologies in breast imaging have emerged. This article outlines the current state of reimbursement for breast radiology AI algorithms within the traditional fee-for-service model used by Medicare and commercial insurers as well as potential future payment pathways. In addition, the inherent challenges of employing the existing payment framework in the United States to AI programs in radiology are detailed for the reader. This article aims to give breast radiologists a better understanding of how AI will be reimbursed as they seek to further incorporate these emerging technologies into their practices to advance patient care and improve workflow efficiency.
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Affiliation(s)
- Dana Smetherman
- Ochsner Health, Department of Radiology, New Orleans, LA, USA
| | - Lauren Golding
- Triad Radiology Associates, PLLC, Winston-Salem, NC, USA
| | - Linda Moy
- NYU Langone Health, New York, NY, USA
| | - Eric Rubin
- Southeast Radiology Limited, Philadelphia, PA,USA
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95
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Baccouche A, Garcia-Zapirain B, Zheng Y, Elmaghraby AS. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106884. [PMID: 35594582 DOI: 10.1016/j.cmpb.2022.106884] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy. METHODS We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix. RESULTS Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams. CONCLUSIONS Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.
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Affiliation(s)
- Asma Baccouche
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
| | | | - Yufeng Zheng
- University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Adel S Elmaghraby
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA
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96
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Bhowmik A, Eskreis-Winkler S. Deep learning in breast imaging. BJR Open 2022; 4:20210060. [PMID: 36105427 PMCID: PMC9459862 DOI: 10.1259/bjro.20210060] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022] Open
Abstract
Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
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Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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97
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Wan Y, Tong Y, Liu Y, Huang Y, Yao G, Chen DQ, Liu B. Evaluation of the Combination of Artificial Intelligence and Radiologist Assessments to Interpret Malignant Architectural Distortion on Mammography. Front Oncol 2022; 12:880150. [PMID: 35515107 PMCID: PMC9067265 DOI: 10.3389/fonc.2022.880150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose To compare the mammographic malignant architectural distortion (AD) detection performance of radiologists who read mammographic examinations unaided versus those who read these examinations with the support of artificial intelligence (AI) systems. Material and Methods This retrospective case-control study was based on a double-reading of clinical mammograms between January 2011 and December 2016 at a large tertiary academic medical center. The study included 177 malignant and 90 benign architectural distortion (AD) patients. The model was built based on the ResNeXt-50 network. Algorithms used deep learning convolutional neural networks, feature classifiers, image analysis algorithms to depict AD and output a score that translated to malignant. The accuracy for malignant AD detection was evaluated using area under the curve (AUC). Results The overall AUC was 0.733 (95% CI, 0.673-0.792) for Reader First-1, 0.652 (95% CI, 0.586-0.717) for Reader First-2, and 0.655 (95% CI, 0.590-0.719) for Reader First-3. and the overall AUCs for Reader Second-1, 2, 3 were 0.875 (95% CI, 0.830-0.919), 0.882 (95% CI, 0.839-0.926), 0.884 (95% CI, 0.841-0.927),respectively. The AUCs for all the reader-second radiologists were significantly higher than those for all the reader-first radiologists (Reader First-1 vs. Reader Second-1, P= 0.004). The overall AUC was 0.792 (95% CI, 0.660-0.925) for AI algorithms. The combination assessment of AI algorithms and Reader First-1 achieved an AUC of 0.880 (95% CI, 0.793-0.968), increased than the Reader First-1 alone and AI algorithms alone. AI algorithms alone achieved a specificity of 61.1% and a sensitivity of 80.6%. The specificity for Reader First-1 was 55.5%, and the sensitivity was 86.1%. The results of the combined assessment of AI and Reader First-1 showed a specificity of 72.7% and sensitivity of 91.7%. The performance showed significant improvements compared with AI alone (p<0.001) as well as the reader first-1 alone (p=0.006). Conclusion While the single AI algorithm did not outperform radiologists, an ensemble of AI algorithms combined with junior radiologist assessments were found to improve the overall accuracy. This study underscores the potential of using machine learning methods to enhance mammography interpretation, especially in remote areas and primary hospitals.
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Affiliation(s)
- Yun Wan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yunfei Tong
- AI Research Lab, Boston Meditech Group, Burlington, MA, United States.,AI Research Lab, Shanghai Yanghe Huajian Artificial Intelligence Technology Co., Ltd, Shanghai, China
| | - Yuanyuan Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Huang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guoyan Yao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Daniel Q Chen
- AI Research Lab, Boston Meditech Group, Burlington, MA, United States
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski MA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE Trans Biomed Eng 2022; 69:1639-1650. [PMID: 34788216 DOI: 10.1109/tbme.2021.3126281] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.
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99
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Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
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Fromherz MR, Makary MS. Artificial intelligence: Advances and new frontiers in medical imaging. Artif Intell Med Imaging 2022; 3:33-41. [DOI: 10.35711/aimi.v3.i2.33] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/20/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago. These algorithms, ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks, have continuously sought to revolutionize medical imaging. With the number of imaging studies outpacing the amount of trained of readers, AI has been implemented to streamline workflow efficiency and provide quantitative, standardized interpretation. AI relies on massive amounts of data for its algorithms to function, and with the wide-spread adoption of Picture Archiving and Communication Systems (PACS), imaging data is accumulating rapidly. Current AI algorithms using machine-learning technology, or computer aided-detection, have been able to successfully pool this data for clinical use, although the scope of these algorithms remains narrow. Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage, however interpretation has generally remained a human responsibility for now. In this review article, we will summarize the current successes and limitations of AI in radiology, and explore the exciting prospects that deep-learning technology offers for the future.
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Affiliation(s)
- Marc R Fromherz
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
| | - Mina S Makary
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
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