Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Journal: Radiology
Published Date: Mar 29, 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.

Authors

  • Marthe Larsen
    Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
  • 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
    Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
  • 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
    Institute for Biomedical Engineering, ETH Zurich, Gloriastrasse 35, 8092, Zürich, Switzerland.
  • 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
    Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.

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