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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

Eun Kyung Park1, Hyeonsoo Lee2, Minjeong Kim2

  • 1Department of Radiology, We Comfortable Clinic, Seoul 07327, Republic of Korea.

Diagnostics (Basel, Switzerland)
|June 27, 2024
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A new artificial intelligence (AI) model accurately predicts breast cancer risk using mammograms. This AI model shows potential for improving early detection and outperforms traditional risk assessment tools.

Keywords:
artificial intelligencebreast cancermammographyrisk prediction

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate breast cancer risk prediction is crucial for early detection and personalized screening strategies.
  • Existing clinical risk models have limitations in predicting short-term breast cancer risk.
  • Deep learning algorithms show promise in analyzing medical images for risk assessment.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for breast cancer risk prediction using mammographic images.
  • To assess the feasibility and performance of the AI model in predicting 1-5 year breast cancer risk.
  • To compare the AI model's performance against established clinical risk models (Tyrer-Cuzick, Gail) and a state-of-the-art deep learning algorithm (Mirai).

Main Methods:

  • A deep learning model was trained on 36,995 mammographic examinations from 21,438 women.
  • The AI model's feasibility was determined using mammograms and clinical data.
  • Performance was evaluated using C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5 year risk prediction.
  • External validation was performed on 16,894 independent mammograms.

Main Results:

  • The AI model achieved a C-index of 0.76 and AUCs ranging from 0.78 to 0.90 for 1-5 year risk prediction.
  • The AI model demonstrated significantly higher AUCs compared to the Tyrer-Cuzick (AUC: 0.57) and Gail (AUC: 0.52) models (p < 0.001).
  • The AI model's performance was comparable to the Mirai deep learning algorithm.

Conclusions:

  • The developed deep learning AI model shows significant potential for accurate breast cancer risk prediction.
  • AI-powered analysis of mammograms and imaging biomarkers can enhance risk assessment capabilities.
  • This AI model offers a promising advancement over traditional methods for identifying women at high risk for breast cancer.