Multi-site validation of an interpretable model to analyze breast masses.

Journal: PloS one
Published Date: Jun 26, 2025

Abstract

An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) from Emory University. This is the first external validation of an inherently interpretable, deep learning-based lesion classification model. IAIA-BL and black-box baseline models had lower mass margin classification performance on the external datasets than the internal dataset as measured by AUC. These losses correlated with a smaller reduction in malignancy classification performance, though AUC 95% confidence intervals overlapped for all sites. However, interpretability, as measured by model activation on relevant portions of the lesion, was maintained across all populations. Together, these results show that model interpretability can generalize even when performance does not.

Authors

  • Luke Moffett
    From the Departments of Computer Science (J.D., L.M., A.J.B., C.R.) and Electrical and Computer Engineering (C.R.), Duke University, 308 Research Dr, LSRC Building D101, Duke Box 90129, Durham, NC 27708; Department of Radiology and Imaging Services, Emory University, Atlanta, Ga (H.T.); Department of Radiology, Harvard University, Cambridge, Mass (F.S.); and Department of Radiology, Duke University School of Medicine, Durham, NC (J.L.).
  • Alina Jade Barnett
    From the Departments of Computer Science (J.D., L.M., A.J.B., C.R.) and Electrical and Computer Engineering (C.R.), Duke University, 308 Research Dr, LSRC Building D101, Duke Box 90129, Durham, NC 27708; Department of Radiology and Imaging Services, Emory University, Atlanta, Ga (H.T.); Department of Radiology, Harvard University, Cambridge, Mass (F.S.); and Department of Radiology, Duke University School of Medicine, Durham, NC (J.L.).
  • Jon Donnelly
    From the Departments of Computer Science (J.D., L.M., A.J.B., C.R.) and Electrical and Computer Engineering (C.R.), Duke University, 308 Research Dr, LSRC Building D101, Duke Box 90129, Durham, NC 27708; Department of Radiology and Imaging Services, Emory University, Atlanta, Ga (H.T.); Department of Radiology, Harvard University, Cambridge, Mass (F.S.); and Department of Radiology, Duke University School of Medicine, Durham, NC (J.L.).
  • Fides Regina Schwartz
    University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland.
  • Hari Trivedi
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Joseph Lo
    From the Departments of Computer Science (J.D., L.M., A.J.B., C.R.) and Electrical and Computer Engineering (C.R.), Duke University, 308 Research Dr, LSRC Building D101, Duke Box 90129, Durham, NC 27708; Department of Radiology and Imaging Services, Emory University, Atlanta, Ga (H.T.); Department of Radiology, Harvard University, Cambridge, Mass (F.S.); and Department of Radiology, Duke University School of Medicine, Durham, NC (J.L.).
  • Cynthia Rudin
    Duke University.

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