AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction.

Journal: Radiology
Published Date: Mar 1, 2024

Abstract

Background Mirai, a state-of-the-art deep learning-based algorithm for predicting short-term breast cancer risk, outperforms standard clinical risk models. However, Mirai is a black box, risking overreliance on the algorithm and incorrect diagnoses. Purpose To identify whether bilateral dissimilarity underpins Mirai's reasoning process; create a simplified, intelligible model, AsymMirai, using bilateral dissimilarity; and determine if AsymMirai may approximate Mirai's performance in 1-5-year breast cancer risk prediction. Materials and Methods This retrospective study involved mammograms obtained from patients in the EMory BrEast imaging Dataset, known as EMBED, from January 2013 to December 2020. To approximate 1-5-year breast cancer risk predictions from Mirai, another deep learning-based model, AsymMirai, was built with an interpretable module: local bilateral dissimilarity (localized differences between left and right breast tissue). Pearson correlation coefficients were computed between the risk scores of Mirai and those of AsymMirai. Subgroup analysis was performed in patients for whom AsymMirai's year-over-year reasoning was consistent. AsymMirai and Mirai risk scores were compared using the area under the receiver operating characteristic curve (AUC), and 95% CIs were calculated using the DeLong method. Results Screening mammograms ( = 210 067) from 81 824 patients (mean age, 59.4 years ± 11.4 [SD]) were included in the study. Deep learning-extracted bilateral dissimilarity produced similar risk scores to those of Mirai (1-year risk prediction, = 0.6832; 4-5-year prediction, = 0.6988) and achieved similar performance as Mirai. For AsymMirai, the 1-year breast cancer risk AUC was 0.79 (95% CI: 0.73, 0.85) (Mirai, 0.84; 95% CI: 0.79, 0.89; = .002), and the 5-year risk AUC was 0.66 (95% CI: 0.63, 0.69) (Mirai, 0.71; 95% CI: 0.68, 0.74; < .001). In a subgroup of 183 patients for whom AsymMirai repeatedly highlighted the same tissue over time, AsymMirai achieved a 3-year AUC of 0.92 (95% CI: 0.86, 0.97). Conclusion Localized bilateral dissimilarity, an imaging marker for breast cancer risk, approximated the predictive power of Mirai and was a key to Mirai's reasoning. © RSNA, 2024 See also the editorial by Freitas in this issue.

Authors

  • 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.).
  • 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.).
  • 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.).
  • Fides Schwartz
    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.).
  • 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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