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Using Explainable AI to Characterize Features in the Mirai Mammographic Breast Cancer Risk Prediction Model.

Yao-Kuan Wang1, Zan Klanecek2, Tobias Wagner1

  • 1Department of Imaging and Pathology, KU Leuven, Herestraat 49, Box 7003, 3000 Leuven, Belgium.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) tool Mirai identifies mammographic calcifications for improved lesion detection and cancer risk prediction. Explainable AI confirms Mirai learns from specific calcification features, enhancing diagnostic capabilities.

Keywords:
BreastMammographyScreening

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mammography is crucial for breast cancer screening.
  • AI tools are being developed to enhance mammographic interpretation.
  • Understanding AI feature relevance is key for clinical integration.

Purpose of the Study:

  • To evaluate if Mirai's extracted features align with mammographic observations.
  • To determine if these features meaningfully contribute to cancer risk prediction.
  • To assess the clinical relevance of AI-identified features.

Main Methods:

  • Retrospective analysis of 29,374 mammograms from the EMBED Dataset.
  • Used a feature-centric explainable AI pipeline to evaluate 512 Mirai features.
  • Assessed performance using area under the receiver operating characteristic curve (AUC) for lesion detection and risk prediction.

Main Results:

  • Mirai demonstrated superior performance in lesion detection compared to calcification-only (CalcMirai) or mass-only (MassMirai) models.
  • No significant difference in 5-year cancer risk prediction was found between Mirai and CalcMirai.
  • MassMirai showed lower performance in risk prediction compared to Mirai.

Conclusions:

  • Explainable AI confirms Mirai implicitly identifies mammographic lesion features, especially calcifications.
  • Mirai's ability to leverage calcification features is valuable for both lesion detection and risk prediction.
  • The study validates the clinical relevance of AI-extracted features in mammography.