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AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.

Mio Adachi1, Tomoyuki Fujioka2, Toshiyuki Ishiba1

  • 1Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.

Journal of Imaging
|September 27, 2024
PubMed
Summary

Artificial intelligence (AI) in mammography (MG) shows promise for diagnosing metachronous bilateral breast cancer (BC). The AI system identified more cancers earlier than radiologists, improving early detection for challenging BC cases.

Keywords:
artificial intelligencebreast cancermammographymetachronous contralateral breast cancer

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Mammography (MG) is crucial for breast cancer (BC) detection.
  • Diagnosing metachronous bilateral breast cancer (BC) presents unique challenges.
  • Limited research exists on artificial intelligence (AI) for metachronous contralateral BC detection.

Purpose of the Study:

  • To evaluate AI's effectiveness in diagnosing metachronous contralateral BC.
  • To compare AI-assisted mammography (MG) diagnoses against radiologist assessments.
  • To determine if AI can achieve earlier or more accurate BC diagnoses.

Main Methods:

  • Retrospective analysis of patients with prior unilateral BC surgery who developed contralateral BC.
  • Evaluation of the FxMammo™ AI diagnostic system on mammograms (MG).
  • Comparison of AI diagnoses with readings from experienced radiologists.

Main Results:

  • AI identified malignancies in 60% of cases, compared to 50% by radiologists.
  • AI solely diagnosed 20% of metachronous contralateral BC cases.
  • AI detected malignancies up to a year earlier than conventional diagnoses in some instances.

Conclusions:

  • The AI-supported mammography (MG) system demonstrates significant effectiveness in diagnosing metachronous contralateral breast cancer (BC).
  • AI has the potential to improve early detection rates and diagnostic accuracy for challenging BC cases.
  • AI systems can aid radiologists by identifying malignancies earlier than traditional assessments.