Evaluating the Impact of Changes in Artificial Intelligence-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.

Journal: Radiology. Artificial intelligence
Published Date: Feb 1, 2025

Abstract

Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and Methods This retrospective review included 21 108 female patients (mean age ± SD, 58.1 years ± 11.5) with 31 741 DBT screening examinations performed at a single site from February 3, 2020, to September 12, 2022. Among 7000 patients with two or more DBT-AI screenings, 1799 had a 1-year follow-up and were included in the analysis. DBT-AI case scores and differences in case score over time were determined. Case scores ranged from 0 to 100. For each screening outcome (true positive [TP], false positive [FP], true negative [TN], false negative [FN]), mean and median case score change was calculated. Results The highest average case score was seen in TP examinations (average, 75; range, 7-100; = 41), and the lowest average case score was seen in TN examinations (average, 34; range, 0-100; = 1640). The largest positive case score change was seen in TP examinations (mean case score change, 21.1; median case score change, 17). FN examinations included mammographically occult cancers diagnosed following supplemental screening and those found at symptomatic diagnostic imaging. Differences between TP and TN mean case score change ( < .001) and between TP and FP mean case score change ( = .02) were statistically significant. Conclusion Using the combination of DBT AI case score with change in case score over time may help radiologists make recall decisions in DBT screening. All studies with high case score and/or case score changes should be carefully scrutinized to maximize screening performance. Mammography, Breast, Computer Aided Diagnosis (CAD) © RSNA, 2025.

Authors

  • Samantha P Zuckerman
    Hospital of the University of Pennsylvania, 3400 Spruce Street, 1 Silverstein Place, Philadelphia, PA 19104.
  • Senthil Periaswamy
    Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).
  • Julie L Shisler
    Hospital of the University of Pennsylvania, 3400 Spruce Street, 1 Silverstein Place, Philadelphia, PA 19104.
  • Ameena Elahi
    From RAD-AID International, 8004 Ellingson Dr, Chevy Chase, MD 20815 (D.J.M., M.P.C., E.P., G.B., J.R.S., V.L.M., A.E., A.S., F.D.); Department of Radiology and Medical Imaging, Denver Health and Hospital Authority, Denver, Colo (E.P.); Departments of Radiology and Global Health, University of Washington, Seattle, Wash (J.R.S.); Fred Hutchinson Cancer Research Center, Seattle, Wash (J.R.S.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (V.L.M.); Department of Radiology, University of Pennsylvania Health System, Philadelphia, Pa (A.E.); and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (F.D.).
  • Christine E Edmonds
  • Jeffrey Hoffmeister
    iCAD, Inc, Nashua, NH, USA.
  • Emily F Conant
    Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.).

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