Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI.

Journal: Radiology
Published Date: Feb 1, 2025

Abstract

Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI resources. Selecting women for MRI by assessing their mammogram may enable more resource-effective screening. Purpose To explore the feasibility of using a commercial artificial intelligence (AI) system at mammography to stratify women with intermediate risk for supplemental MRI or no MRI. Materials and Methods This retrospective study included consecutive women with intermediate risk screened with mammography and MRI between January 2003 and January 2020 at a Dutch university medical center. An AI system was used to independently evaluate all mammograms, providing a case-based score that ranked the likelihood of a malignancy on a scale of 1-10. Different AI thresholds for supplemental MRI screening were tested, balancing cancer detection against the number of women needing to undergo MRI. Univariate analyses were used to explore associations between personal factors (age, breast density, and duration of screening participation) and AI results. Results In 760 women (mean age, 48.9 years ± 10.5 [SD]), 2819 combined screening examinations were performed, and 37 breast cancers were detected. Use of AI at mammography achieved an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.63, 0.81) for the entire intermediate-risk population and 0.81 (95% CI: 0.69, 0.93) for women with prior breast cancer. Using a threshold score of 5, 31 of 37 (84%) breast cancers were detected, including 13 of 19 (68%) mammographically occult cancers, at a supplemental breast MRI rate of 50% (1409 of 2819 examinations). No significant association between breast density or age and the probability of a false-negative AI result was found. Conclusion Using AI at mammography to select women for supplemental MRI effectively identified women with higher breast cancer risk in an intermediate-risk population, including women with mammographically occult cancers. AI selection of women with intermediate risk for supplemental MRI screening has the potential to reduce screening burden and costs, while maintaining a high cancer detection rate. © RSNA, 2025.

Authors

  • Suzanne L van Winkel
    Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, The Netherlands. Suzanne.vanWinkel@radboudumc.nl.
  • Riccardo Samperna
    From the Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands (N.L., C.I.S., L.H.B., M.B., E.C., W.M.v.E., P.K.G., B.G., M.G., N.H., W.H., H.J.H., C.J., R.K., M.K., K.v.L., J.M., M.O., R.S., C. Schaefer-Prokop, S.S., E.T.S., C. Sital, J.T., K.V.V., C.d.V., W.X., B.d.W., M.P., B.v.G.); Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands (L.B.); Thirona, Nijmegen, the Netherlands (J.P.C., E.M.v.R.); Departments of Internal Medicine (T.D.) and Radiology (M.V.), Canisius-Wilhelmina Ziekenhuis, Nijmegen, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (H.A.G.); GROW School of Oncology and Developmental Biology, Maastricht, the Netherlands (H.A.G.); Departments of Biomedical Physics and Engineering and Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands (L.v.H., I.I.); Department of Radiology, Zuyderland Medical Center, Heerlen, the Netherlands (J.K.); Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany (B.L.); Department of Radiology and Nuclear Medicine, Haaglanden Medical Center, The Hague, the Netherlands (T.v.R.V.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C. Schaefer-Prokop, S.S.); and Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.L.S.).
  • Elizabeth A Loehrer
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.).
  • Jaap Kroes
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.).
  • Alejandro Rodríguez-Ruiz
    From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.).
  • Ritse M Mann
    Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.

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