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

Publication ,  Journal Article
Donnelly, J; Moffett, L; Barnett, AJ; Trivedi, H; Schwartz, F; Lo, J; Rudin, C
Published in: Radiology
March 2024

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 (n = 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, r = 0.6832; 4-5-year prediction, r = 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; P = .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; P < .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 Supplemental material is available for this article See also the editorial by Freitas in this issue.

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Published In

Radiology

DOI

EISSN

1527-1315

Publication Date

March 2024

Volume

310

Issue

3

Start / End Page

e232780

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Mammography
  • Humans
  • Female
  • Deep Learning
  • Breast Neoplasms
  • Breast
  • 3202 Clinical sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Donnelly, J., Moffett, L., Barnett, A. J., Trivedi, H., Schwartz, F., Lo, J., & Rudin, C. (2024). AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction. Radiology, 310(3), e232780. https://doi.org/10.1148/radiol.232780
Donnelly, Jon, Luke Moffett, Alina Jade Barnett, Hari Trivedi, Fides Schwartz, Joseph Lo, and Cynthia Rudin. “AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction.Radiology 310, no. 3 (March 2024): e232780. https://doi.org/10.1148/radiol.232780.
Donnelly J, Moffett L, Barnett AJ, Trivedi H, Schwartz F, Lo J, et al. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction. Radiology. 2024 Mar;310(3):e232780.
Donnelly, Jon, et al. “AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction.Radiology, vol. 310, no. 3, Mar. 2024, p. e232780. Pubmed, doi:10.1148/radiol.232780.
Donnelly J, Moffett L, Barnett AJ, Trivedi H, Schwartz F, Lo J, Rudin C. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction. Radiology. 2024 Mar;310(3):e232780.

Published In

Radiology

DOI

EISSN

1527-1315

Publication Date

March 2024

Volume

310

Issue

3

Start / End Page

e232780

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Mammography
  • Humans
  • Female
  • Deep Learning
  • Breast Neoplasms
  • Breast
  • 3202 Clinical sciences