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Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, Mikalsen KØ, Lund-Hanssen H, Solli HS, Silberhorn M, Sulheim ÅØ, Auensen S, Nygård JF, Hofvind S. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell 2024; 6:e230375. [PMID: 38597784 DOI: 10.1148/ryai.230375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Purpose To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661 695 digital mammographic examinations performed among 242 629 female individuals screened as a part of BreastScreen Norway, 2004-2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload. Keywords: Mammography, Breast, Screening, Convolutional Neural Network (CNN), Deep Learning Algorithms Supplemental material is available for this article. © RSNA, 2024 See also commentary by Bahl and Do in this issue.
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Affiliation(s)
- Marthe Larsen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Camilla F Olstad
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Christoph I Lee
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Tone Hovda
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Solveig R Hoff
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Marit A Martiniussen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Karl Øyvind Mikalsen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Håkon Lund-Hanssen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Helene S Solli
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Marko Silberhorn
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Åse Ø Sulheim
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Steinar Auensen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Jan F Nygård
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
| | - Solveig Hofvind
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.F.N.), Cancer Registry of Norway, Norwegian Institute of Public Health, PO 5313, Majorstuen, 0304 Oslo, Norway; Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway (T.H.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation, Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine, Faculty of Health Sciences (K.Ø.M.), Department of Physics and Technology, Faculty of Science and Technology (J.F.N.), and Department of Health and Care Sciences, Faculty of Health Sciences (S.H.), UiT-The Arctic University of Norway, Tromsø, Norway; Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); Department of Radiology, Innlandet Hospital Trust, Hamar, Norway (M.S.); and Department of Radiology, Innlandet Hospital Trust, Lillehammer, Norway (Å.Ø.S.)
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Elmore JG, Lee CI. Toward More Equitable Breast Cancer Outcomes. JAMA 2024:2818287. [PMID: 38687474 DOI: 10.1001/jama.2024.6052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Affiliation(s)
- Joann G Elmore
- Department of Medicine, UCLA National Clinician Scholar Program, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Fred Hutchinson Cancer Center, Seattle, Washington
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Liao JM, Anzai Y, Sadigh G, Fendrick AM, Lee CI. JACR Health Policy Expert Panel: Health Equity and Out-of-Pocket Payments for Imaging Studies. J Am Coll Radiol 2024; 21:688-690. [PMID: 37517773 DOI: 10.1016/j.jacr.2023.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023]
Affiliation(s)
- Joshua M Liao
- Director of the Value and Systems Science Lab and Associate Chair for Health Systems, Department of Medicine, University of Washington School of Medicine, Seattle, Washington.
| | - Yoshimi Anzai
- Director of Value and Safety for Enterprise Imaging, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah. https://twitter.com/yoshimianzai
| | - Gelareh Sadigh
- Director of Radiology Health Services and Comparative Outcomes Research, Department of Radiological Sciences, University of California at Irvine, Irvine, California. https://twitter.com/GelarehSadigh
| | - A Mark Fendrick
- Director, Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan. https://twitter.com/FendrickVBID
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Deputy Editor of JACR. https://twitter.com/christophleemd
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Bergan MB, Larsen M, Moshina N, Bartsch H, Koch HW, Aase HS, Satybaldinov Z, Haldorsen IHS, Lee CI, Hofvind S. AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway. Eur Radiol 2024:10.1007/s00330-024-10681-z. [PMID: 38528136 DOI: 10.1007/s00330-024-10681-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/19/2024] [Accepted: 02/10/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVE To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.
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Affiliation(s)
- Marie Burns Bergan
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | | | - Zhanbolat Satybaldinov
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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Lawson MB, Lee CI. Using Machine Learning to Predict Adherence to Recommended Imaging Follow-Up. J Am Coll Radiol 2024:S1546-1440(24)00275-8. [PMID: 38461916 DOI: 10.1016/j.jacr.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/01/2024] [Indexed: 03/12/2024]
Affiliation(s)
- Marissa B Lawson
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, Washington; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of JACR. https://twitter.com/christophleemd
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6
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Lee CI, Elmore JG. Beyond survival: a closer look at lead-time bias and disease-free intervals in mammography screening. J Natl Cancer Inst 2024; 116:343-344. [PMID: 38145456 DOI: 10.1093/jnci/djad254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023] Open
Affiliation(s)
- Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Kim JG, Haslam B, Diab AR, Sakhare A, Grisot G, Lee H, Holt J, Lee CI, Lotter W, Sorensen AG. Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. Radiol Artif Intell 2024; 6:e230137. [PMID: 38323914 PMCID: PMC10982824 DOI: 10.1148/ryai.230137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/26/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Jiye G. Kim
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Bryan Haslam
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Abdul Rahman Diab
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Ashwin Sakhare
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Giorgia Grisot
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Hyunkwang Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Jacqueline Holt
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Christoph I. Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
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Kocher MR, Lee CI. Radiologist Workforce Changes: Going Remote or Hybrid. J Am Coll Radiol 2024; 21:503-504. [PMID: 37813226 DOI: 10.1016/j.jacr.2023.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/06/2023] [Indexed: 10/11/2023]
Affiliation(s)
- Madison R Kocher
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, and the Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, Washington, and is Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington; and Deputy Editor of JACR
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9
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Moshina N, Gräwingholt A, Lång K, Mann R, Hovda T, Hoff SR, Skaane P, Lee CI, Aase HS, Aslaksen AB, Hofvind S. Digital breast tomosynthesis in mammographic screening: false negative cancer cases in the To-Be 1 trial. Insights Imaging 2024; 15:38. [PMID: 38332187 PMCID: PMC10853101 DOI: 10.1186/s13244-023-01604-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/21/2023] [Indexed: 02/10/2024] Open
Abstract
OBJECTIVES The randomized controlled trial comparing digital breast tomosynthesis and synthetic 2D mammograms (DBT + SM) versus digital mammography (DM) (the To-Be 1 trial), 2016-2017, did not result in higher cancer detection for DBT + SM. We aimed to determine if negative cases prior to interval and consecutive screen-detected cancers from DBT + SM were due to interpretive error. METHODS Five external breast radiologists performed the individual blinded review of 239 screening examinations (90 true negative, 39 false positive, 19 prior to interval cancer, and 91 prior to consecutive screen-detected cancer) and the informed consensus review of examinations prior to interval and screen-detected cancers (n = 110). The reviewers marked suspicious findings with a score of 1-5 (probability of malignancy). A case was false negative if ≥ 2 radiologists assigned the cancer site with a score of ≥ 2 in the blinded review and if the case was assigned as false negative by a consensus in the informed review. RESULTS In the informed review, 5.3% of examinations prior to interval cancer and 18.7% prior to consecutive round screen-detected cancer were considered false negative. In the blinded review, 10.6% of examinations prior to interval cancer and 42.9% prior to consecutive round screen-detected cancer were scored ≥ 2. A score of ≥ 2 was assigned to 47.8% of negative and 89.7% of false positive examinations. CONCLUSIONS The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT + SM versus DM in the To-Be 1 trial is complex and not due to interpretive error alone. CRITICAL RELEVANCE STATEMENT The randomized controlled trial on digital breast tomosynthesis and synthetic 2D mammograms (DBT) and digital mammography (DM), 2016-2017, showed no difference in cancer detection for the two techniques. The rates of false negative screening examinations prior to interval and consecutive screen-detected cancer for DBT were consistent with the rates in prior DM reviews, indicating that the non-superior DBT performance in the trial might not be due to interpretive error alone. KEY POINTS • Screening with digital breast tomosynthesis (DBT) did not result in a higher breast cancer detection rate compared to screening with digital mammography (DM) in the To-Be 1 trial. • The false negative rates for examinations prior to interval and consecutive screen-detected cancer for DBT were determined in the trial to test if the lack of differences was due to interpretive error. • The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT versus DM was complex and not due to interpretive error alone.
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Affiliation(s)
- Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Axel Gräwingholt
- Mammographiescreening-Zentrum Paderborn, Breast Cancer Screening, Paderborn, NRW, Germany
| | - Kristina Lång
- Department of Translational Medicine, Lund University, Lund, Sweden
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Solveig Roth Hoff
- Department of Radiology, Ålesund Hospital, Møre Og Romsdal Hospital Trust, Ålesund, Norway
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway
| | - Per Skaane
- Department of Radiology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Hildegunn S Aase
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Aslak B Aslaksen
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Global Public Health and Primary Care, Faculty of Medicine, University of Bergen, Bergen, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway.
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Kerlikowske K, Chen S, Bissell MCS, Lee CI, Tice JA, Sprague BL, Miglioretti DL. Population Attributable Risk of Advanced-Stage Breast Cancer by Race and Ethnicity. JAMA Oncol 2024; 10:167-175. [PMID: 38060241 PMCID: PMC10704341 DOI: 10.1001/jamaoncol.2023.5242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/31/2023] [Indexed: 12/08/2023]
Abstract
Importance Advanced-stage breast cancer rates vary by race and ethnicity, with Black women having a 2-fold higher rate than White women among regular screeners. Clinical risk factors that explain a large proportion of advanced breast cancers by race and ethnicity are unknown. Objective To evaluate the population attributable risk proportions (PARPs) for advanced-stage breast cancer (prognostic pathologic stage IIA or higher) associated with clinical risk factors among routinely screened premenopausal and postmenopausal women by race and ethnicity. Design, Setting, and Participants This cohort study used data collected prospectively from Breast Cancer Surveillance Consortium community-based breast imaging facilities from January 2005 to June 2018. Participants were women aged 40 to 74 years undergoing 3 331 740 annual (prior screening within 11-18 months) or biennial (prior screening within 19-30 months) screening mammograms associated with 1815 advanced breast cancers diagnosed within 2 years of screening examinations. Data analysis was performed from September 2022 to August 2023. Exposures Heterogeneously or extremely dense breasts, first-degree family history of breast cancer, overweight/obesity (body mass index >25.0), history of benign breast biopsy, and screening interval (biennial vs annual) stratified by menopausal status and race and ethnicity (Asian or Pacific Islander, Black, Hispanic/Latinx, White, other/multiracial). Main Outcomes and Measures PARPs for advanced breast cancer. Results Among 904 615 women, median (IQR) age was 57 (50-64) years. Of the 3 331 740 annual or biennial screening mammograms, 10.8% were for Asian or Pacific Islander women; 9.5% were for Black women; 5.3% were for Hispanic/Latinx women; 72.0% were for White women; and 2.0% were for women of other races and ethnicities, including those who were Alaska Native, American Indian, 2 or more reported races, or other. Body mass index PARPs were larger for postmenopausal vs premenopausal women (30% vs 22%) and highest for postmenopausal Black (38.6%; 95% CI, 32.0%-44.8%) and Hispanic/Latinx women (31.8%; 95% CI, 25.3%-38.0%) and premenopausal Black women (30.3%; 95% CI, 17.7%-42.0%), with overall prevalence of having overweight/obesity highest in premenopausal Black (84.4%) and postmenopausal Black (85.1%) and Hispanic/Latinx women (72.4%). Breast density PARPs were larger for premenopausal vs postmenopausal women (37% vs 24%, respectively) and highest among premenopausal Asian or Pacific Islander (46.6%; 95% CI, 37.9%-54.4%) and White women (39.8%; 95% CI, 31.7%-47.3%) whose prevalence of dense breasts was high (62%-79%). For premenopausal and postmenopausal women, PARPs were small for family history of breast cancer (5%-8%), history of breast biopsy (7%-12%), and screening interval (2.1%-2.3%). Conclusions and Relevance In this cohort study among routinely screened women, the proportion of advanced breast cancers attributed to biennial vs annual screening was small. To reduce the number of advanced breast cancer diagnoses, primary prevention should focus on interventions that shift patients with overweight and obesity to normal weight.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
| | - Shuai Chen
- Department of Public Health Sciences, University of California, Davis
| | - Michael C. S. Bissell
- Department of Public Health Sciences, University of California, Davis
- PicnicHealth, San Francisco, California
| | - Christoph I. Lee
- Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle
| | - Jeffrey A. Tice
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco
| | - Brian L. Sprague
- Departments of Surgery and Radiology, University of Vermont, Burlington
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
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11
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Lee CI, Chen JH, Kohli MD, Smith AD, Liao JM. JACR Health Policy Expert Panel: Generative Artificial Intelligence. J Am Coll Radiol 2024:S1546-1440(24)00130-3. [PMID: 38295922 DOI: 10.1016/j.jacr.2024.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/26/2024] [Indexed: 02/29/2024]
Affiliation(s)
- Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; is the Director of Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington; and Deputy Editor, JACR.
| | - Jonathan H Chen
- Director of HealthRex Lab, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California; and is part of the Division of Hospital Medicine and Clinical Excellence Research Center, Stanford University, Stanford, California. https://twitter.com/jonc101x
| | - Marc D Kohli
- Associate Chair of Clinical Informatics, Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging and the Director of Imaging Informatics, University of California, San Francisco, School of Medicine, San Francisco, California
| | - Andrew D Smith
- Vice Chair of Innovation, Co-Director of Artificial Intelligence, and Director of the Human Imaging Core Lab, Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Joshua M Liao
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; and Director of Program on Policy Evaluation and Learning and Division Chief of General Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas. https://twitter.com/joshliaotweets
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12
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Schopf CM, Ramwala OA, Lowry KP, Hofvind S, Marinovich ML, Houssami N, Elmore JG, Dontchos BN, Lee JM, Lee CI. Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review. J Am Coll Radiol 2024; 21:319-328. [PMID: 37949155 PMCID: PMC10926179 DOI: 10.1016/j.jacr.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/05/2023] [Accepted: 10/05/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction. MATERIALS AND METHODS A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC). RESULTS Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement. CONCLUSIONS Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.
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Affiliation(s)
- Cody M Schopf
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Ojas A Ramwala
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Kathryn P Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Solveig Hofvind
- Section Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - M Luke Marinovich
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - Nehmat Houssami
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; National Breast Cancer Foundation Chair in Breast Cancer Prevention at the University of Sydney and Coeditor of The Breast
| | - Joann G Elmore
- David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California; Director of UCLA's National Clinician Scholars Program and Editor-in-Chief of Adult Primary Care at Up-To-Date. https://twitter.com/JoannElmoreMD
| | - Brian N Dontchos
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Clinical Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Janie M Lee
- Section Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of Journal of the American College of Radiology.
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13
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Gard CC, Lange J, Miglioretti DL, O’Meara ES, Lee CI, Etzioni R. Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity. J Med Screen 2023; 30:209-216. [PMID: 37306245 PMCID: PMC10713859 DOI: 10.1177/09691413231180028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVES Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity. METHODS We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women. RESULTS Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively. CONCLUSIONS Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.
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Affiliation(s)
- Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA
| | - Jane Lange
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
- Hutchinson Institute for Cancer Outcomes Research, Seattle, WA, USA
| | - Ruth Etzioni
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
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14
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Onega T, Abraham L, Miglioretti DL, Lee CI, Henderson LM, Kerlikowske K, Tosteson ANA, Weaver D, Sprague BL, Bowles EJA, di Florio-Alexander RM. Digital mammography and digital breast tomosynthesis for detecting invasive lobular and ductal carcinoma. Breast Cancer Res Treat 2023; 202:505-514. [PMID: 37697031 DOI: 10.1007/s10549-023-07051-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/13/2023] [Indexed: 09/13/2023]
Abstract
PURPOSE Invasive lobular carcinoma (ILC) is a distinct histological subtype of breast cancer that can make early detection with mammography challenging. We compared imaging performance of digital breast tomosynthesis (DBT) to digital mammography (DM) for diagnoses of ILC, invasive ductal carcinoma (IDC), and invasive mixed carcinoma (IMC) in a screening population. METHODS We included screening exams (DM; n = 1,715,249 or DBT; n = 414,793) from 2011 to 2018 among 839,801 women in the Breast Cancer Surveillance Consortium. Examinations were followed for one year to ascertain incident ILC, IDC, or IMC. We measured cancer detection rate (CDR) and interval invasive cancer rate/1000 screening examinations for each histological subtype and stratified by breast density and modality. We calculated relative risk (RR) for DM vs. DBT using log-binomial models to adjust for the propensity of receiving DBT vs. DM. RESULTS Unadjusted CDR per 1000 mammograms of ILC overall was 0.33 (95%CI: 0.30-0.36) for DM; 0.45 (95%CI: 0.39-0.52) for DBT, and for women with dense breasts- 0.33 (95%CI: 0.29-0.37) for DM and 0.54 (95%CI: 0.43-0.66) for DBT. Similar results were noted for IDC and IMC. Adjusted models showed a significantly increased RR for cancer detection with DBT compared to DM among women with dense breasts for all three histologies (RR; 95%CI: ILC 1.53; 1.09-2.14, IDC 1.21; 1.02-1.44, IMC 1.76; 1.30-2.38), but no significant increase among women with non-dense breasts. CONCLUSION DBT was associated with higher CDR for ILC, IDC, and IMC for women with dense breasts. Early detection of ILC with DBT may improve outcomes for this distinct clinical entity.
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Affiliation(s)
- Tracy Onega
- Department of Population Health Sciences, and the Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope Dr., RS 4725, Salt Lake City, UT, 84018, USA.
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Christoph I Lee
- Department of Radiology, University of Washington, and Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Donald Weaver
- Department of Pathology, University of Vermont, Burlington, VT, USA
| | - Brian L Sprague
- Departments of Surgery and Radiology, University of Vermont Cancer Center, University of Vermont, Burlington, VT, USA
| | - Erin J Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
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15
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Cheng M, Lee CI. Harnessing the Potential of Artificial Intelligence for Quality Assurance in Radiology Practice. J Am Coll Radiol 2023; 20:1231-1232. [PMID: 37423351 DOI: 10.1016/j.jacr.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 06/22/2023] [Indexed: 07/11/2023]
Affiliation(s)
- Monica Cheng
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Department of Health Systems & Population Health, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Washington; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of JACR
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16
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Hosseiny M, Lee CI. Improving Medical Imaging Order Entry With Artificial Intelligence Tools: Insights and Action Items. J Am Coll Radiol 2023; 20:1267-1268. [PMID: 37379889 PMCID: PMC11088912 DOI: 10.1016/j.jacr.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 04/26/2023] [Indexed: 06/30/2023]
Affiliation(s)
- Melina Hosseiny
- Department of Radiology, University of California, San Diego, San Diego, California.
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Deputy Editor of JACR. https://twitter.com/christophleemd
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17
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Nyante SJ, Abraham L, Bowles EJA, Lee CI, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM. Racial and Ethnic Variation in Diagnostic Mammography Performance among Women Reporting a Breast Lump. Cancer Epidemiol Biomarkers Prev 2023; 32:1542-1551. [PMID: 37440458 PMCID: PMC10790330 DOI: 10.1158/1055-9965.epi-23-0289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/12/2023] [Accepted: 07/11/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND We evaluated diagnostic mammography among women with a breast lump to determine whether performance varied across racial and ethnic groups. METHODS This study included 51,014 diagnostic mammograms performed between 2005 and 2018 in the Breast Cancer Surveillance Consortium among Asian/Pacific Islander (12%), Black (7%), Hispanic/Latina (6%), and White (75%) women reporting a lump. Breast cancers occurring within 1 year were ascertained from cancer registry linkages. Multivariable regression was used to adjust performance statistic comparisons for breast cancer risk factors, mammogram modality, demographics, additional imaging, and imaging facility. RESULTS Cancer detection rates were highest among Asian/Pacific Islander [per 1,000 exams, 84.2 (95% confidence interval (CI): 72.0-98.2)] and Black women [81.4 (95% CI: 69.4-95.2)] and lowest among Hispanic/Latina women [42.9 (95% CI: 34.2-53.6)]. Positive predictive values (PPV) were higher among Black [37.0% (95% CI: 31.2-43.3)] and White [37.0% (95% CI: 30.0-44.6)] women and lowest among Hispanic/Latina women [22.0% (95% CI: 17.2-27.7)]. False-positive results were most common among Asian/Pacific Islander women [per 1,000 exams, 183.9 (95% CI: 126.7-259.2)] and lowest among White women [112.4 (95% CI: 86.1-145.5)]. After adjustment, false-positive and cancer detection rates remained higher for Asian/Pacific Islander and Black women (vs. Hispanic/Latina and White). Adjusted PPV was highest among Asian/Pacific Islander women. CONCLUSIONS Among women with a lump, Asian/Pacific Islander and Black women were more likely to have cancer detected and more likely to receive a false-positive result compared with White and Hispanic/Latina women. IMPACT Strategies for optimizing diagnostic mammography among women with a lump may vary by racial/ethnic group, but additional factors that influence performance differences need to be identified. See related In the Spotlight, p. 1479.
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Affiliation(s)
- Sarah J. Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Erin J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health; Fred Hutchinson Cancer Center, Seattle, WA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
- Department of Public Health Sciences, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT
| | - Louise M. Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
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18
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Holen ÅS, Bergan MB, Lee CI, Zackrisson S, Moshina N, Aase HS, Haldorsen IS, Hofvind S. Early screening outcomes before, during, and after a randomized controlled trial with digital breast tomosynthesis. Eur J Radiol 2023; 167:111069. [PMID: 37708674 DOI: 10.1016/j.ejrad.2023.111069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/31/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE To describe and compare early screening outcomes before, during and after a randomized controlled trial with digital breast tomosynthesis (DBT) including synthetic 2D mammography versus standard digital mammography (DM) (To-Be 1) and a follow-up cohort study using DBT (To-Be 2). METHODS Retrospective results of 125,020 screening examinations from four consecutive screening rounds performed in 2014-2021 were described and compared for pre-To-Be 1 (DM), To-Be 1 (DM or DBT), To-Be 2 (DBT), and post-To-Be 2 (DM) cohorts. Descriptive analyses of rates of recall, biopsy, screen-detected and interval cancer, distribution of histopathologic tumor characteristics and time spent on image interpretation and consensus were presented for the four rounds including five cohorts, one cohort in each screening round except for the To-Be 1 trail, which included a DBT and a DM cohort. Odds ratios (OR) with 95% CIs was calculated for recall and cancer detection rates. RESULTS Rate of screen-detected cancer was 0.90% for women screened with DBT in To-Be 2 and 0.64% for DM in pre-To-Be 1. The rates did not differ for the To-Be 1 DM (0.61%), To-Be 1 DBT (0.66%) and post-To-Be 2 DM (0.67%) cohorts. The interval cancer rates ranged between 0.13% and 0.20%. The distribution of histopathologic tumor characteristics did not differ between the cohorts. CONCLUSIONS Screening all women with DBT following a randomized controlled trial in an organized, population-based screening program showed a temporary increase in the rate of screen-detected cancer.
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Affiliation(s)
- Åsne Sørlien Holen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
| | - Marie Burns Bergan
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA.
| | - Sophia Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden; Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden.
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
| | | | - Ingfrid Salvesen Haldorsen
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway.
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19
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Varney ET, Lee CI. The Potential for Using ChatGPT to Improve Imaging Appropriateness. J Am Coll Radiol 2023; 20:988-989. [PMID: 37400048 DOI: 10.1016/j.jacr.2023.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/03/2023] [Indexed: 07/05/2023]
Affiliation(s)
- Elliot T Varney
- Department of Radiology, University of Mississippi Medical Center School of Medicine, Jackson, Mississippi.
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; and Deputy Editor of JACR. https://twitter.com/christophleemd
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20
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Liao JM, Lee CI. Strategies for Mitigating Consequences of Federal Breast Density Notifications. JAMA Health Forum 2023; 4:e232801. [PMID: 37682552 DOI: 10.1001/jamahealthforum.2023.2801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023] Open
Abstract
This Viewpoint describes new federal updates to screening mammography rules and recommends strategies for mitigating potential consequences of the rules.
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Affiliation(s)
- Joshua M Liao
- Department of Medicine, University of Washington School of Medicine, Seattle
- Value and Systems Science Lab, University of Washington School of Medicine, Seattle
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington School of Medicine, Seattle
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21
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Lee CI, Davis MA, Lexa FJ, Liao JM. JACR Health Policy Expert Panel: Private Equity Investment in Radiology. J Am Coll Radiol 2023; 20:940-942. [PMID: 37011830 DOI: 10.1016/j.jacr.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 01/24/2023] [Indexed: 04/03/2023]
Affiliation(s)
- Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; and Deputy Editor, JACR.
| | - Melissa A Davis
- Vice Chair of Informatics, Department of Radiology, Yale University School of Medicine, New Haven, Connecticut. https://twitter.com/MelissaDavis29
| | - Frank J Lexa
- Department of Radiology, University of Pittsburgh School of Medicine and UPMC International, Pittsburgh, Pennsylvania; and Chief Medical Officer and Vice Chair, ACR Radiology Leadership Institute. https://twitter.com/fjlexa
| | - Joshua M Liao
- Director of the Value and Systems Science Lab and Associate Chair for Health Systems, Department of Medicine, University of Washington School of Medicine, Seattle, Washington. https://twitter.com/JoshuaLiaoMD
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22
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Lawson MB, Partridge SC, Hippe DS, Rahbar H, Lam DL, Lee CI, Lowry KP, Scheel JR, Parsian S, Li I, Biswas D, Bryant ML, Lee JM. Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening. Radiology 2023; 308:e230576. [PMID: 37581498 PMCID: PMC10481328 DOI: 10.1148/radiol.230576] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 08/16/2023]
Abstract
Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI) are emerging alternatives to standard MRI for supplemental breast cancer screening. Purpose To compare the diagnostic performance of CEM, ABMRI, and standard MRI. Materials and Methods This single-institution, prospective, blinded reader study included female participants referred for breast MRI from January 2018 to June 2021. CEM was performed within 14 days of standard MRI; ABMRI was produced from standard MRI images. Two readers independently interpreted each CEM and ABMRI after a washout period. Examination-level performance metrics calculated were recall rate, cancer detection, and false-positive biopsy recommendation rates per 1000 examinations and sensitivity, specificity, and positive predictive value of biopsy recommendation. Bootstrap and permutation tests were used to calculate 95% CIs and compare modalities. Results Evaluated were 492 paired CEM and ABMRI interpretations from 246 participants (median age, 51 years; IQR, 43-61 years). On 49 MRI scans with lesions recommended for biopsy, nine lesions showed malignant pathology. No differences in ABMRI and standard MRI performance were identified. Compared with standard MRI, CEM demonstrated significantly lower recall rate (14.0% vs 22.8%; difference, -8.7%; 95% CI: -14.0, -3.5), lower false-positive biopsy recommendation rate per 1000 examinations (65.0 vs 162.6; difference, -97.6; 95% CI: -146.3, -50.8), and higher specificity (87.8% vs 80.2%; difference, 7.6%; 95% CI: 2.3, 13.1). Compared with standard MRI, CEM had significantly lower cancer detection rate (22.4 vs 36.6; difference, -14.2; 95% CI: -28.5, -2.0) and sensitivity (61.1% vs 100%; difference, -38.9%; 95% CI: -66.7, -12.5). The performance differences between CEM and ABMRI were similar to those observed between CEM and standard MRI. Conclusion ABMRI had comparable performance to standard MRI and may support more efficient MRI screening. CEM had lower recall and higher specificity compared with standard MRI or ABMRI, offset by lower cancer detection rate and sensitivity compared with standard MRI. These trade-offs warrant further consideration of patient population characteristics before widespread screening with CEM. Clinical trial registration no. NCT03517813 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chang in this issue.
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Affiliation(s)
- Marissa B. Lawson
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Savannah C. Partridge
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Daniel S. Hippe
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Habib Rahbar
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Diana L. Lam
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Christoph I. Lee
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Kathryn P. Lowry
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - John R. Scheel
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Sana Parsian
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Isabella Li
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Debosmita Biswas
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Mary Lynn Bryant
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Janie M. Lee
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
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23
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Paige JS, Lee CI, Wang PC, Hsu W, Brentnall AR, Hoyt AC, Naeim A, Elmore JG. Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman. J Gen Intern Med 2023; 38:2584-2592. [PMID: 36749434 PMCID: PMC10465429 DOI: 10.1007/s11606-023-08043-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/13/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.
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Affiliation(s)
- Jeremy S Paige
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Pin-Chieh Wang
- Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, and Office of Health Informatics and Analytics, University of California, Los Angeles, Los Angeles, USA
| | - William Hsu
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Adam R Brentnall
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Charterhouse Square, Queen Mary University of London, London, UK
| | - Anne C Hoyt
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Arash Naeim
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Joann G Elmore
- Department of Medicine, Division of General Internal Medicine and Health Services Research and the National Clinician Scholars Program, David Geffen School of Medicine, University of California, Los Angeles, 1100 Glendon Ave, Ste. 900, Los Angeles, CA, 90024, USA.
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24
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Sprague BL, Coley RY, Lowry KP, Kerlikowske K, Henderson LM, Su YR, Lee CI, Onega T, Bowles EJA, Herschorn SD, diFlorio-Alexander RM, Miglioretti DL. Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium. Radiology 2023; 307:e223142. [PMID: 37249433 DOI: 10.1148/radiol.223142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.
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Affiliation(s)
- Brian L Sprague
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Rebecca Yates Coley
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Kathryn P Lowry
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Karla Kerlikowske
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Louise M Henderson
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Yu-Ru Su
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Christoph I Lee
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Tracy Onega
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Erin J A Bowles
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Sally D Herschorn
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Roberta M diFlorio-Alexander
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
| | - Diana L Miglioretti
- From the Departments of Surgery (B.L.S.) and Radiology (B.L.S., S.D.H.), University of Vermont Cancer Center, University of Vermont Larner College of Medicine, UHC Bldg, Room 4425, 1 S Prospect St, Burlington, VT 05401; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (R.Y.C., Y.R.S., E.J.A.B., D.L.M.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, Seattle, Wash (K.P.L., C.I.L.); Departments of Medicine and Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (K.K.); Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, Utah (T.O.); Department of Radiology, Giesel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.F.A.); and Division of Biostatistics, Department of Public Health Sciences, University of California-Davis, Davis, Calif (D.L.M.)
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25
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Larsen M, Lynge E, Lee CI, Lång K, Hofvind S. Mammographic density and interval cancers in mammographic screening: Moving towards more personalized screening. Breast 2023; 69:306-311. [PMID: 36966656 PMCID: PMC10066543 DOI: 10.1016/j.breast.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/18/2023] [Indexed: 03/29/2023] Open
Abstract
PURPOSE The European Society on Breast Imaging has recommended supplemental magnetic resonance imaging (MRI) every two to four years for women with mammographically dense breasts. This may not be feasible in many screening programs. Also, the European Commission Initiative on Breast Cancer suggests not implementing screening with MRI. By analyzing interval cancers and time from screening to diagnosis by density, we present alternative screening strategies for women with dense breasts. METHODS Our BreastScreen Norway cohort included 508 536 screening examinations, including 3125 screen-detected and 945 interval breast cancers. Time from screening to interval cancer was stratified by density measured by an automated software and classified into Volpara Density Grades (VDGs) 1-4. Examinations with volumetric density ≤3.4% were categorized as VDG1, 3.5%-7.4% as VDG2, 7.5%-15.4% as VDG3, and ≥15.5% as VDG4. Interval cancer rates were also determined by continuous density measures. RESULTS Median time from screening to interval cancer was 496 (IQR: 391-587) days for VDG1, 500 (IQR: 350-616) for VDG2, 482 (IQR: 309-595) for VDG3 and 427 (IQR: 266-577) for VDG4. A total of 35.9% of the interval cancers among VDG4 were detected within the first year of the biennial screening interval. For VDG2, 26.3% were detected within the first year. The highest annual interval cancer rate (2.7 per 1000 examinations) was observed for VDG4 in the second year of the biennial interval. CONCLUSIONS Annual screening of women with extremely dense breasts may reduce the interval cancer rate and increase program-wide sensitivity, especially in settings where supplemental MRI screening is not feasible.
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Affiliation(s)
- Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Elsebeth Lynge
- Nykøbing Falster Hospital, University of Copenhagen, Nykøbing Falster, Denmark
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Kristina Lång
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway.
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26
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Milton AJ, Flores EJ, Charles EF, Elezaby MA, Ward EC, Lee CI, Woods RW, Martin Rother MD, Strigel RM, Narayan AK. Community-based Participatory Research: A Practical Guide for Radiologists. Radiographics 2023; 43:e220145. [PMID: 37104126 PMCID: PMC10190132 DOI: 10.1148/rg.220145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/21/2022] [Accepted: 09/27/2022] [Indexed: 04/28/2023]
Abstract
Community-based participatory research (CBPR) is defined by the Kellogg Community Health Scholars Program as a collaborative process that equitably involves all partners in the research process and recognizes the unique strengths that each community member brings. The CBPR process begins with a research topic of importance to the community, with the goal of combining knowledge and action with social change to improve community health and eliminate health disparities. CBPR engages and empowers affected communities to collaborate in defining the research question; sharing the study design process; collecting, analyzing, and disseminating the data; and implementing solutions. A CBPR approach in radiology has several potential applications, including removing limitations to high-quality imaging, improving secondary prevention, identifying barriers to technology access, and increasing diversity in the research participation for clinical trials. The authors provide an overview with the definitions of CBPR, explain how to conduct CBPR, and illustrate its applications in radiology. Finally, the challenges of CBPR and useful resources are discussed in detail. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Arissa J. Milton
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Efrén J. Flores
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Eden F. Charles
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Mai A. Elezaby
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Earlise C. Ward
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Christoph I. Lee
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Ryan W. Woods
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Maria D. Martin Rother
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Roberta M. Strigel
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
| | - Anand K. Narayan
- From the Department of Radiology (M.A.E., R.W.W., M.D.M.R., R.M.S.,
A.K.N.), School of Medicine and Public Health (A.J.M, E.F.C.), University of
Wisconsin–Madison, 600 Highland Ave, Madison, WI 53792-3252; Department
of Radiology, Massachusetts General Hospital, Boston, Mass (E.J.F.); Department
of Family Medicine and Nursing, School of Nursing, University of
Wisconsin–Madison, Madison, Wis (E.C.W.); Department of Radiology, School
of Medicine, University of Washington, Seattle, Wash (C.I.L.); and Carbone
Cancer Center, University of Wisconsin–Madison, Madison, Wis (E.C.W,
R.M.S, A.K.N.)
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Lee CI, Abraham L, Miglioretti DL, Onega T, Kerlikowske K, Lee JM, Sprague BL, Tosteson ANA, Rauscher GH, Bowles EJA, diFlorio-Alexander RM, Henderson LM. National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium. Radiology 2023; 307:e222499. [PMID: 37039687 PMCID: PMC10323294 DOI: 10.1148/radiol.222499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/03/2023] [Accepted: 02/20/2023] [Indexed: 04/12/2023]
Abstract
Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.
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Affiliation(s)
- Christoph I. Lee
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Linn Abraham
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Diana L. Miglioretti
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Tracy Onega
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Janie M. Lee
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Brian L. Sprague
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Anna N. A. Tosteson
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Garth H. Rauscher
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Erin J. A. Bowles
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Roberta M. diFlorio-Alexander
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - Louise M. Henderson
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
| | - for the Breast Cancer Surveillance Consortium
- From the Department of Radiology, University of Washington School of
Medicine, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson
Cancer Center, 825 Eastlake Ave E, LG-200, Seattle, WA 98109 (C.I.L., J.M.L.);
Department of Health Systems & Population Health, University of
Washington School of Public Health, Seattle, Wash (C.I.L.); Kaiser Permanente
Washington Health Research Institute, Kaiser Permanente Washington, Seattle,
Wash (C.I.L., L.A., D.L.M., J.M.L., E.J.A.B.); Division of Biostatistics,
Department of Public Health Sciences, University of California Davis School of
Medicine, Davis, Calif (D.L.M.); Department of Population Health Sciences, and
the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (T.O.);
Department of Medicine, Department of Epidemiology and Biostatistics, and
General Internal Medicine Section, Department of Veterans Affairs, University of
California, San Francisco, San Francisco, Calif (K.K.); Department of Surgery,
Office of Health Promotion Research, Larner College of Medicine at the
University of Vermont and University of Vermont Cancer Center, Burlington, Vt
(B.L.S.); The Dartmouth Institute for Health Policy and Clinical Practice,
Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon,
NH (A.N.A.T.); Division of Epidemiology and Biostatistics, School of Public
Health, University of Illinois at Chicago, Chicago, Ill (G.H.R.); Department of
Radiology, Geisel School of Medicine at Dartmouth, Lebanon, NH (R.M.d.A.); and
Department of Radiology, University of North Carolina, Chapel Hill, NC
(L.M.H.)
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Lawson MB, Lee CI, Partridge SC, Rahbar H. Breast Imaging Research: Tips for Obtaining Funding and Sustaining a Successful Career. J Breast Imaging 2023; 5:351-359. [PMID: 37223454 PMCID: PMC10202023 DOI: 10.1093/jbi/wbac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Indexed: 02/29/2024]
Abstract
Many factors are involved in the successful development of early career breast imaging radiologists into independent investigators conducting impactful research. Key basic prerequisites for success include a motivated and resilient radiologist, institutional and departmental commitment to supporting early career physician-scientists, strong mentorship, and a flexible strategy for extramural funding that accounts for individualized professional goals. In this review, we describe these factors in greater detail, providing a practical overview for residents, fellows, and junior faculty who are interested in an academic career as a breast imaging radiologist engaged in original scientific research. We also describe the essential pieces of grant applications and summarize the professional milestones for early career physician-scientists as they look toward promotion to associate professor and sustained extramural funding.
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Affiliation(s)
- Marissa B Lawson
- University of Washington School of Medicine, Department of Radiology, Seattle, WA, USA
| | - Christoph I Lee
- University of Washington School of Medicine, Department of Radiology, Seattle, WA, USA
- University of Washington School of Public Health, Department of Health Services, Seattle, WA, USA
| | - Savannah C Partridge
- University of Washington School of Medicine, Department of Radiology, Seattle, WA, USA
| | - Habib Rahbar
- University of Washington School of Medicine, Department of Radiology, Seattle, WA, USA
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Ho TQH, Bissell MCS, Lee CI, Lee JM, Sprague BL, Tosteson ANA, Wernli KJ, Henderson LM, Kerlikowske K, Miglioretti DL. Prioritizing Screening Mammograms for Immediate Interpretation and Diagnostic Evaluation on the Basis of Risk for Recall. J Am Coll Radiol 2023; 20:299-310. [PMID: 36273501 PMCID: PMC10044471 DOI: 10.1016/j.jacr.2022.09.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 09/08/2022] [Accepted: 09/19/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation. METHODS A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms. RESULTS The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar. CONCLUSIONS Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit.
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Affiliation(s)
- Thao-Quyen H Ho
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Breast Imaging Unit, Diagnostic Imaging Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam; Department of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam
| | - Michael C S Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California
| | - Christoph I Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington, Seattle, Washington; Deputy Editor, JACR
| | - Janie M Lee
- Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington; Breast Imaging, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Brian L Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and Co-Leader, Cancer Control and Population Health Sciences Program, University of Vermont Cancer Center, Burlington, Vermont
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth and Associate Director for Population Sciences, Dartmouth Cancer Center, Lebanon, New Hampshire
| | - Karen J Wernli
- Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina; Cancer Epidemiology Program, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California; General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California; Women's Health Comprehensive Clinic, and Director, Advanced Postdoctoral Fellowship in Women's Health, San Francisco Veterans Affairs Health Care System, San Francisco, California
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, California; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington; Biostatistics and Population Sciences and Health Disparities Program, University of California, Davis, Comprehensive Cancer Center, Davis, California.
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Lee CI, Lawson MB. Addressing Racial Inequities in Access to State-of-the-Art Breast Imaging. Radiology 2023; 306:e222405. [PMID: 36219120 PMCID: PMC9885344 DOI: 10.1148/radiol.222405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 09/20/2022] [Accepted: 09/21/2022] [Indexed: 01/26/2023]
Affiliation(s)
- Christoph I. Lee
- From the Department of Radiology, Fred Hutchinson Cancer Center,
University of Washington School of Medicine, Seattle, Wash; and Department of
Health Systems & Population Health, University of Washington School of
Public Health, 1144 Eastlake Ave E, LG-200, Seattle, WA 98019
| | - Marissa B. Lawson
- From the Department of Radiology, Fred Hutchinson Cancer Center,
University of Washington School of Medicine, Seattle, Wash; and Department of
Health Systems & Population Health, University of Washington School of
Public Health, 1144 Eastlake Ave E, LG-200, Seattle, WA 98019
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31
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Kocher MR, Lee CI. Preventing Artificial Intelligence in Medical Imaging From Perpetuating Health Care Biases and Disparities. J Am Coll Radiol 2022; 19:1345-1346. [PMID: 36208841 DOI: 10.1016/j.jacr.2022.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/30/2022] [Indexed: 11/15/2022]
Affiliation(s)
- Madison R Kocher
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington; and Deputy Editor of JACR. https://twitter.com/christophleemd
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Liao JM, Hughes DR, Shetty S, Lee CI. JACR Health Policy Expert Panel: Bundled Payments. J Am Coll Radiol 2022; 19:1350-1352. [PMID: 36265812 DOI: 10.1016/j.jacr.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Joshua M Liao
- Medical Director of Payment Strategy and Associate Chair for Health Systems, Department of Medicine, University of Washington School of Medicine, Seattle, Washington.
| | - Danny R Hughes
- Director the Health Economics and Analytics Lab, School of Economics, Georgia Institute of Technology, Atlanta, Georgia; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia. https://twitter.com/economeer
| | - Sanjay Shetty
- President, Steward Healthcare, Dallas, Texas. https://twitter.com/SanjayRadMD
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Deputy Editor of JACR. https://twitter.com/christophleemd
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33
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Lopez EJ, Lee CI. Improving Health Equity in Adherence to Diagnostic Breast Imaging Workup Recommendations. J Am Coll Radiol 2022; 19:1310-1311. [PMID: 36208840 PMCID: PMC10150785 DOI: 10.1016/j.jacr.2022.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 07/30/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Eric J Lopez
- Department of Radiology, University of California, San Francisco, San Francisco, California.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and the Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington, and Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington. https://twitter.com/christophleemd
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Som A, Lee CI. Addressing usability of hospital price estimators for medical imaging procedures. J Am Coll Radiol 2022; 19:1260-1261. [PMID: 36155101 DOI: 10.1016/j.jacr.2022.09.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/16/2022] [Indexed: 10/14/2022]
Affiliation(s)
- Avik Som
- Department of Radiology, Massachusetts General Hospital.
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine. https://twitter.com/christophleemd
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37
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Liao JM, Lee RK, McGinty G, Nicola LP, Lee CI. JACR Health Policy Expert Panel: Pay for Performance. J Am Coll Radiol 2022; 19:1074-1076. [PMID: 35926692 DOI: 10.1016/j.jacr.2022.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022]
Affiliation(s)
- Joshua M Liao
- Director of the Value and Systems Science Lab and Associate Chair for Health Systems, Department of Medicine, University of Washington School of Medicine, Seattle, Washington.
| | - Ryan K Lee
- Chair of Radiology, Department of Radiology, Einstein Healthcare Network, Philadelphia, Pennsylvania
| | - Geraldine McGinty
- Senior Associate Dean for Clinical Affairs, Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Lauren P Nicola
- Chief Executive Officer of Triad Radiology Associates and Chief Medical Officer of the Strategic Radiology-Patient Safety Organization, Triad Radiology Associates, Winston-Salem, North Carolina
| | - Christoph I Lee
- Director of the Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and is Deputy Editor of JACR
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Lawson MB, Bissell MCS, Miglioretti DL, Eavey J, Chapman CH, Mandelblatt JS, Onega T, Henderson LM, Rauscher GH, Kerlikowske K, Sprague BL, Bowles EJA, Gard CC, Parsian S, Lee CI. Multilevel Factors Associated With Time to Biopsy After Abnormal Screening Mammography Results by Race and Ethnicity. JAMA Oncol 2022; 8:1115-1126. [PMID: 35737381 PMCID: PMC9227677 DOI: 10.1001/jamaoncol.2022.1990] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Importance Diagnostic delays in breast cancer detection may be associated with later-stage disease and higher anxiety, but data on multilevel factors associated with diagnostic delay are limited. Objective To evaluate individual-, neighborhood-, and health care-level factors associated with differences in time from abnormal screening to biopsy among racial and ethnic groups. Design, Setting, and Participants This prospective cohort study used data from women aged 40 to 79 years who had abnormal results in screening mammograms conducted in 109 imaging facilities across 6 US states between 2009 and 2019. Data were analyzed from February 21 to November 4, 2021. Exposures Individual-level factors included self-reported race and ethnicity, age, family history of breast cancer, breast density, previous breast biopsy, and time since last mammogram; neighborhood-level factors included geocoded education and income based on residential zip codes and rurality; and health care-level factors included mammogram modality, screening facility academic affiliation, and facility onsite biopsy service availability. Data were also assessed by examination year. Main Outcome and Measures The main outcome was unadjusted and adjusted relative risk (RR) of no biopsy within 30, 60, and 90 days using sequential log-binomial regression models. A secondary outcome was unadjusted and adjusted median time to biopsy using accelerated failure time models. Results A total of 45 186 women (median [IQR] age at screening, 56 [48-65] years) with 46 185 screening mammograms with abnormal results were included. Of screening mammograms with abnormal results recommended for biopsy, 15 969 (34.6%) were not resolved within 30 days, 7493 (16.2%) were not resolved within 60 days, and 5634 (12.2%) were not resolved within 90 days. Compared with White women, there was increased risk of no biopsy within 30 and 60 days for Asian (30 days: RR, 1.66; 95% CI, 1.31-2.10; 60 days: RR, 1.58; 95% CI, 1.15-2.18), Black (30 days: RR, 1.52; 95% CI, 1.30-1.78; 60 days: 1.39; 95% CI, 1.22-1.60), and Hispanic (30 days: RR, 1.50; 95% CI, 1.24-1.81; 60 days: 1.38; 95% CI, 1.11-1.71) women; however, the unadjusted risk of no biopsy within 90 days only persisted significantly for Black women (RR, 1.28; 95% CI, 1.11-1.47). Sequential adjustment for selected individual-, neighborhood-, and health care-level factors, exclusive of screening facility, did not substantially change the risk of no biopsy within 90 days for Black women (RR, 1.27; 95% CI, 1.12-1.44). After additionally adjusting for screening facility, the increased risk for Black women persisted but showed a modest decrease (RR, 1.20; 95% CI, 1.08-1.34). Conclusions and Relevance In this cohort study involving a diverse cohort of US women recommended for biopsy after abnormal results on screening mammography, Black women were the most likely to experience delays to diagnostic resolution after adjusting for multilevel factors. These results suggest that adjustment for multilevel factors did not entirely account for differences in time to breast biopsy, but unmeasured factors, such as systemic racism and other health care system factors, may impact timely diagnosis.
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Affiliation(s)
- Marissa B. Lawson
- Department of Radiology, University of Washington School of Medicine, Seattle
| | - Michael C. S. Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis,Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Joanna Eavey
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Christina H. Chapman
- Department of Radiation Oncology, Michigan Medicine, Ann Arbor,University of Wisconsin–Madison School of Medicine and Public Health, Madison
| | - Jeanne S. Mandelblatt
- Department of Oncology, Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Tracy Onega
- Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City
| | - Louise M. Henderson
- Departments of Radiology and Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Garth H. Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco,General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
| | - Brian L. Sprague
- Departments of Surgery and Radiology, University of Vermont Cancer Center, University of Vermont, Burlington
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Charlotte C. Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces
| | | | - 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
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Lee CI, Elmore JG. Cancer Risk Prediction Paradigm Shift: Using Artificial Intelligence to Improve Performance and Health Equity. J Natl Cancer Inst 2022; 114:1317-1319. [PMID: 35876797 PMCID: PMC9552274 DOI: 10.1093/jnci/djac143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.,Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA, USA.,Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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Nyante SJ, Abraham L, Bowles EJA, Lee CI, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM. Diagnostic Mammography Performance across Racial and Ethnic Groups in a National Network of Community-Based Breast Imaging Facilities. Cancer Epidemiol Biomarkers Prev 2022; 31:1324-1333. [PMID: 35712862 PMCID: PMC9272467 DOI: 10.1158/1055-9965.epi-21-1379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/16/2022] [Accepted: 04/26/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND We evaluated differences in diagnostic mammography performance based on women's race/ethnicity. METHODS This cohort study included 267,868 diagnostic mammograms performed to evaluate screening mammogram findings at 98 facilities in the Breast Cancer Surveillance Consortium between 2005 and 2017. Mammogram assessments were recorded prospectively and breast cancers occurring within one year were ascertained. Performance statistics were calculated with 95% confidence intervals (CI) for each racial/ethnic group. Multivariable regression was used to control for personal characteristics and imaging facility. RESULTS Among non-Hispanic White (70%), non-Hispanic Black (13%), Asian/Pacific Islander (10%), and Hispanic (7%) women, the invasive cancer detection rate (iCDR, per 1,000 mammograms) and positive predictive value (PPV2) were highest among non-Hispanic White women (iCDR, 35.8; 95% CI, 35.0-36.7; PPV2, 27.8; 95% CI, 27.3-28.3) and lowest among Hispanic women (iCDR, 22.3; 95% CI, 20.2-24.6; PPV2, 19.4; 95% CI, 18.0-20.9). Short interval follow-up recommendations were most common among non-Hispanic Black women [(31.0%; 95% CI, 30.6%-31.5%) vs. other groups, range, 16.6%-23.6%]. False-positive biopsy recommendations were most common among Asian/Pacific Islander women [per 1,000 mammograms: 169.2; 95% CI, 164.8-173.7) vs. other groups, range, 126.5-136.1]. Some differences were explained by adjusting for receipt of diagnostic ultrasound or MRI for iCDR and imaging facility for short-interval follow-up. Other differences changed little after adjustment. CONCLUSIONS Diagnostic mammography performance varied across racial/ethnic groups. Addressing characteristics related to imaging facility and access, rather than personal characteristics, may help reduce some of these disparities. IMPACT Diagnostic mammography performance studies should include racially and ethnically diverse populations to provide an accurate view of the population-level effects.
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Affiliation(s)
- Sarah J. Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Erin J. Aiello Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA,Department of Public Health Sciences, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT
| | - Louise M. Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Kerlikowske K, Su YR, Sprague BL, Tosteson ANA, Buist DSM, Onega T, Henderson LM, Alsheik N, Bissell MCS, O’Meara ES, Lee CI, Miglioretti DL. Association of Screening With Digital Breast Tomosynthesis vs Digital Mammography With Risk of Interval Invasive and Advanced Breast Cancer. JAMA 2022; 327:2220-2230. [PMID: 35699706 PMCID: PMC9198754 DOI: 10.1001/jama.2022.7672] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/21/2022] [Indexed: 12/15/2022]
Abstract
Importance Digital breast tomosynthesis (DBT) was developed with the expectation of improving cancer detection in women with dense breasts. Studies are needed to evaluate interval invasive and advanced breast cancer rates, intermediary outcomes related to breast cancer mortality, by breast density and breast cancer risk. Objective To evaluate whether DBT screening is associated with a lower likelihood of interval invasive cancer and advanced breast cancer compared with digital mammography by extent of breast density and breast cancer risk. Design, Setting, and Participants Cohort study of 504 427 women aged 40 to 79 years who underwent 1 003 900 screening digital mammography and 375 189 screening DBT examinations from 2011 through 2018 at 44 US Breast Cancer Surveillance Consortium (BCSC) facilities with follow-up for cancer diagnoses through 2019 by linkage to state or regional cancer registries. Exposures Breast Imaging Reporting and Data System (BI-RADS) breast density; BCSC 5-year breast cancer risk. Main Outcomes and Measures Rates per 1000 examinations of interval invasive cancer within 12 months of screening mammography and advanced breast cancer (prognostic pathologic stage II or higher) within 12 months of screening mammography, both estimated with inverse probability weighting. Results Among 504 427 women in the study population, the median age at time of mammography was 58 years (IQR, 50-65 years). Interval invasive cancer rates per 1000 examinations were not significantly different for DBT vs digital mammography (overall, 0.57 vs 0.61, respectively; difference, -0.04; 95% CI, -0.14 to 0.06; P = .43) or among all the 836 250 examinations with BCSC 5-year risk less than 1.67% (low to average-risk) or all the 413 061 examinations with BCSC 5-year risk of 1.67% or higher (high risk) across breast density categories. Advanced cancer rates were not significantly different for DBT vs digital mammography among women at low to average risk or at high risk with almost entirely fatty, scattered fibroglandular densities, or heterogeneously dense breasts. Advanced cancer rates per 1000 examinations were significantly lower for DBT vs digital mammography for the 3.6% of women with extremely dense breasts and at high risk of breast cancer (13 291 examinations in the DBT group and 31 300 in the digital mammography group; 0.27 vs 0.80 per 1000 examinations; difference, -0.53; 95% CI, -0.97 to -0.10) but not for women at low to average risk (10 611 examinations in the DBT group and 37 796 in the digital mammography group; 0.54 vs 0.42 per 1000 examinations; difference, 0.12; 95% CI, -0.09 to 0.32). Conclusions and Relevance Screening with DBT vs digital mammography was not associated with a significant difference in risk of interval invasive cancer and was associated with a significantly lower risk of advanced breast cancer among the 3.6% of women with extremely dense breasts and at high risk of breast cancer. No significant difference was observed in the 96.4% of women with nondense breasts, heterogeneously dense breasts, or with extremely dense breasts not at high risk.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Brian L. Sprague
- Departments of Surgery and Radiology, University of Vermont, Burlington
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Diana S. M. Buist
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | - Tracy Onega
- Department of Population Health Sciences, University of Utah, Salt Lake City
- Huntsman Cancer Institute, Salt Lake City, Utah
| | | | - Nila Alsheik
- School of Public Health, Division of Epidemiology and Biostatistics, University of Illinois at Chicago
| | | | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
| | | | - Diana L. Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
- Department of Public Health Sciences, University of California, Davis
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Larsen M, Aglen CF, Lee CI, Hoff SR, Lund-Hanssen H, Lång K, Nygård JF, Ursin G, Hofvind S. Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program. Radiology 2022. [PMID: 35348377 DOI: 10.1148/radiol.212381:212381] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.
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Affiliation(s)
- Marthe Larsen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Camilla F Aglen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Christoph I Lee
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Solveig R Hoff
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Håkon Lund-Hanssen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Kristina Lång
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Jan F Nygård
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Giske Ursin
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Solveig Hofvind
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
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Carlos RC, Obeng-Gyasi S, Cole SW, Zebrack BJ, Pisano ED, Troester MA, Timsina L, Wagner LI, Steingrimsson JA, Gareen I, Lee CI, Adams AS, Wilkins CH. Linking Structural Racism and Discrimination and Breast Cancer Outcomes: A Social Genomics Approach. J Clin Oncol 2022; 40:1407-1413. [PMID: 35108027 PMCID: PMC9851699 DOI: 10.1200/jco.21.02004] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 01/23/2023] Open
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Liao JM, Bai G, Forman HP, White AA, Lee CI. JACR Health Policy Expert Panel: Hospital Price Transparency. J Am Coll Radiol 2022; 19:792-794. [PMID: 35460605 DOI: 10.1016/j.jacr.2022.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Joshua M Liao
- Director of the Value and Systems Science Lab and Associate Chair for Health Systems, Department of Medicine at the University of Washington, and the Department of Medicine, University of Washington School of Medicine, Seattle, Washington.
| | - Gei Bai
- Johns Hopkins Carey Business School, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Howard P Forman
- Department of Diagnostic Radiology, Yale University School of Medicine; Yale School of Management; Department of Economics, Yale College; and Yale School of Public Health, New Haven, Connecticut, and is Director of Clinical Leadership Development for Yale New Haven Health System and Faculty Director for Finance, Department of Radiology
| | - Andrew A White
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and is Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of JACR
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Larsen M, Aglen CF, Lee CI, Hoff SR, Lund-Hanssen H, Lång K, Nygård JF, Ursin G, Hofvind S. Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program. Radiology 2022; 303:502-511. [PMID: 35348377 DOI: 10.1148/radiol.212381] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program. Furthermore, the histopathologic characteristics of tumors with different AI scores were explored. Materials and Methods In this retrospective study, 122 969 screening examinations from 47 877 women performed at four screening units in BreastScreen Norway from October 2009 to December 2018 were included. The data set included 752 screen-detected cancers (6.1 per 1000 examinations) and 205 interval cancers (1.7 per 1000 examinations). Each examination had an AI score between 1 and 10, where 1 indicated low risk of breast cancer and 10 indicated high risk. Threshold 1, threshold 2, and threshold 3 were used to assess the performance of the AI system as a binary decision tool (selected vs not selected). Threshold 1 was set at an AI score of 10, threshold 2 was set to yield a selection rate similar to the consensus rate (8.8%), and threshold 3 was set to yield a selection rate similar to an average individual radiologist (5.8%). Descriptive statistics were used to summarize screening outcomes. Results A total of 653 of 752 screen-detected cancers (86.8%) and 92 of 205 interval cancers (44.9%) were given a score of 10 by the AI system (threshold 1). Using threshold 3, 80.1% of the screen-detected cancers (602 of 752) and 30.7% of the interval cancers (63 of 205) were selected. Screen-detected cancer with AI scores not selected using the thresholds had favorable histopathologic characteristics compared to those selected; opposite results were observed for interval cancer. Conclusion The proportion of screen-detected cancers not selected by the artificial intelligence (AI) system at the three evaluated thresholds was less than 20%. The overall performance of the AI system was promising according to cancer detection. © RSNA, 2022.
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Affiliation(s)
- Marthe Larsen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Camilla F Aglen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Christoph I Lee
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Solveig R Hoff
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Håkon Lund-Hanssen
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Kristina Lång
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Jan F Nygård
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Giske Ursin
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
| | - Solveig Hofvind
- From the Section for Breast Cancer Screening (M.L., C.F.A., S.H.) and Department of Register Informatics (J.F.N.), Cancer Registry of Norway (G.U.), P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Translational Medicine, Lund University, Lund, Sweden (K.L.); and Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden (K.L.)
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Moshina N, Aase HS, Danielsen AS, Haldorsen IS, Lee CI, Zackrisson S, Hofvind S. Comparing Screening Outcomes for Digital Breast Tomosynthesis and Digital Mammography by Automated Breast Density in a Randomized Controlled Trial: Results from the To-Be Trial. Radiology 2022; 303:E23. [PMID: 35312347 DOI: 10.1148/radiol.229003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Ho TQH, Bissell MCS, Kerlikowske K, Hubbard RA, Sprague BL, Lee CI, Tice JA, Tosteson ANA, Miglioretti DL. Cumulative Probability of False-Positive Results After 10 Years of Screening With Digital Breast Tomosynthesis vs Digital Mammography. JAMA Netw Open 2022; 5:e222440. [PMID: 35333365 PMCID: PMC8956976 DOI: 10.1001/jamanetworkopen.2022.2440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/23/2021] [Indexed: 11/25/2022] Open
Abstract
Importance Breast cancer screening with digital breast tomosynthesis may decrease false-positive results compared with digital mammography. Objective To estimate the probability of receiving at least 1 false-positive result after 10 years of screening with digital breast tomosynthesis vs digital mammography in the US. Design, Setting, and Participants An observational comparative effectiveness study with data collected prospectively for screening examinations was performed between January 1, 2005, and December 31, 2018, at 126 radiology facilities in the Breast Cancer Surveillance Consortium. Analysis included 903 495 individuals aged 40 to 79 years. Data analysis was conducted from February 9 to September 7, 2021. Exposures Screening modality, screening interval, age, and Breast Imaging Reporting and Data System breast density. Main Outcomes and Measures Cumulative risk of at least 1 false-positive recall for further imaging, short-interval follow-up recommendation, and biopsy recommendation after 10 years of annual or biennial screening with digital breast tomosynthesis vs digital mammography, accounting for competing risks of breast cancer diagnosis and death. Results In this study of 903 495 women, 2 969 055 nonbaseline screening examinations were performed with interpretation by 699 radiologists. Mean (SD) age of the women at the time of the screening examinations was 57.6 (9.9) years, and 58% of the examinations were in individuals younger than 60 years and 46% were performed in women with dense breasts. A total of 15% of examinations used tomosynthesis. For annual screening, the 10-year cumulative probability of at least 1 false-positive result was significantly lower with tomosynthesis vs digital mammography for all outcomes: 49.6% vs 56.3% (difference, -6.7; 95% CI, -7.4 to -6.1) for recall, 16.6% vs 17.8% (difference, -1.1; 95% CI, -1.7 to -0.6) for short-interval follow-up recommendation, and 11.2% vs 11.7% (difference, -0.5; 95% CI, -1.0 to -0.1) for biopsy recommendation. For biennial screening, the cumulative probability of a false-positive recall was significantly lower for tomosynthesis vs digital mammography (35.7% vs 38.1%; difference, -2.4; 95% CI, -3.4 to -1.5), but cumulative probabilities did not differ significantly by modality for short-interval follow-up recommendation (10.3% vs 10.5%; difference, -0.1; 95% CI, -0.7 to 0.5) or biopsy recommendation (6.6% vs 6.7%; difference, -0.1; 95% CI, -0.5 to 0.4). Decreases in cumulative probabilities of false-positive results with tomosynthesis vs digital mammography were largest for annual screening in women with nondense breasts (differences for recall, -6.5 to -12.8; short-interval follow-up, 0.1 to -5.2; and biopsy recommendation, -0.5 to -3.1). Regardless of modality, cumulative probabilities of false-positive results were substantially lower for biennial vs annual screening (overall recall, 35.7 to 38.1 vs 49.6 to 56.3; short-interval follow-up, 10.3 to 10.5 vs 16.6 to 17.8; and biopsy recommendation, 6.6 to 6.7 vs 11.2 to 11.7); older vs younger age groups (eg, among annual screening in women ages 70-79 vs 40-49, recall, 39.8 to 47.0 vs 60.8 to 68.0; short-interval follow-up, 13.3 to 14.2 vs 20.7 to 20.9; and biopsy recommendation, 9.1 to 9.3 vs 13.2 to 13.4); and women with entirely fatty vs extremely dense breasts (eg, among annual screening in women aged 50-59 years, recall, 29.1 to 36.3 vs 58.8 to 60.4; short-interval follow-up, 8.9 to 11.6 vs 19.5 to 19.8; and biopsy recommendation, 4.9 to 8.0 vs 15.1 to 15.3). Conclusions and Relevance In this comparative effectiveness study, 10-year cumulative probabilities of false-positive results were lower on digital breast tomosynthesis vs digital mammography. Biennial screening interval, older age, and nondense breasts were associated with larger reductions in false-positive probabilities than screening modality.
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Affiliation(s)
- Thao-Quyen H. Ho
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Department of Training and Scientific Research, University Medical Center, Ho Chi Minh City, Vietnam
| | - Michael C. S. Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Rebecca A. Hubbard
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Brian L. Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, Vermont
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle
- Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, New Hampshire
- Department of Medicine, Geisel School of Medicine at Dartmouth, Hanover, Lebanon, New Hampshire
- Department of Oncology, Norris Cotton Cancer Center, Lebanon, New Hampshire
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
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Advani S, Abraham L, Buist DS, Kerlikowske K, Miglioretti DL, Sprague BL, Henderson LM, Onega T, Schousboe JT, Demb J, Zhang D, Walter LC, Lee CI, Braithwaite D, O’Meara ES. Breast biopsy patterns and findings among older women undergoing screening mammography: The role of age and comorbidity. J Geriatr Oncol 2022; 13:161-169. [PMID: 34896059 PMCID: PMC9450010 DOI: 10.1016/j.jgo.2021.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Limited evidence exists on the impact of age and comorbidity on biopsy rates and findings among older women. MATERIALS AND METHODS We used data from 170,657 women ages 66-94 enrolled in the United States Breast Cancer Surveillance Consortium (BCSC). We estimated one-year rates of biopsy by type (any, fine-needle aspiration (FNA), core or surgical) and yield of the most invasive biopsy finding (benign, ductal carcinoma in situ (DCIS) and invasive breast cancer) by age and comorbidity. Statistical significance was assessed using Wald statistics comparing coefficients estimated from logistic regression models adjusted for age, comorbidity, BCSC registry, and interaction between age and comorbidity. RESULTS Of 524,860 screening mammograms, 9830 biopsies were performed following 7930 exams (1.5%) within one year, specifically 5589 core biopsies (1.1%), 3422 (0.7%) surgical biopsies and 819 FNAs (0.2%). Biopsy rates per 1000 screens decreased with age (66-74:15.7, 95%CI:14.8-16.8), 75-84:14.5(13.5-15.6), 85-94:13.2(11.3,15.4), ptrend < 0.001) and increased with Charlson Comorbidity Score (CCS = 0:14.4 (13.5-15.3), CCS = 1:16.6 (15.2-18.1), CCS ≥2:19.0 (16.9-21.5), ptrend < 0.001).Biopsy rates increased with CCS at ages 66-74 and 75-84 but not 85-94. Core and surgical biopsy rates increased with CCS at ages 66-74 only. For each biopsy type, the yield of invasive breast cancer increased with age irrespective of comorbidity. DISCUSSION Women aged 66-84 with significant comorbidity in a breast cancer screening population had higher breast biopsy rates and similar rates of invasive breast cancer diagnosis than their counterparts with lower comorbidity. A considerable proportion of these diagnoses may represent overdiagnoses, given the high competing risk of death from non-breast-cancer causes among older women.
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Affiliation(s)
- Shailesh Advani
- Department of Oncology, Georgetown University, Washington, DC
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Karla Kerlikowske
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA,Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Diana L. Miglioretti
- Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT
| | | | - Tracy Onega
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | | | - Joshua Demb
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, University of California, San Diego, La Jolla, CA
| | - Dongyu Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL
| | - Louise C. Walter
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health, Seattle, WA
| | - Dejana Braithwaite
- Department of Epidemiology, University of Florida, Gainesville, FL, United States of America; University of Florida Health Cancer Center, Gainesville, FL, United States of America; Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States of America.
| | - Ellen S. O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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Affiliation(s)
- Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
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Anderson AW, Marinovich ML, Houssami N, Lowry KP, Elmore JG, Buist DS, Hofvind S, Lee CI. Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review. J Am Coll Radiol 2022; 19:259-273. [PMID: 35065909 PMCID: PMC8857031 DOI: 10.1016/j.jacr.2021.11.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. METHODS A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage. CONCLUSIONS To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.
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Affiliation(s)
- Anna W. Anderson
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - M. Luke Marinovich
- Curtin School of Population Health, Curtin University, Bentley, Western Australia, Australia
| | - Nehmat Houssami
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - Kathryn P. Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Joann G. Elmore
- David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | | | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
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