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Updated: Jun 25, 2025

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
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Decoding the Prevalent High-Risk Breast Cancers: Demographics, Pathological, Imaging Insights, and Long-Term Outcome
Pedro Alvarenga1, Ji Yeon Park2, Renata Pinto3,4
1Temerty Faculty of Medicine, Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Summary
This study analyzed high-risk breast cancer subgroups, finding gene mutation carriers had more aggressive tumors. MRI proved effective for screening, and early detection is key for survival in these high-risk patients.
Area of Science:
- Oncology
- Radiology
- Genetics
Background:
- High-risk breast cancer subgroups require specialized investigation.
- Understanding tumor features and outcomes in these groups is crucial for effective management.
Purpose of the Study:
- To investigate the distinct features and clinical outcomes of breast cancer within various high-risk subgroups.
- To evaluate the efficacy of different imaging modalities in detecting breast cancer in these populations.
Main Methods:
- An observational study of 140 high-risk women diagnosed between 2010-2019.
- Radiological review of mammograms and MRIs using BI-RADS lexicon with inter-rater agreement analysis.
- Statistical analysis including survival rates (Kaplan-Meier) and comparative analysis (Cox model).
Main Results:
- Gene mutation carriers presented with smaller, higher-grade, triple-negative tumors (ER-, PR-, HER2-).
- Magnetic Resonance Imaging (MRI) demonstrated superior performance over mammography across all subgroups.
- No deaths were observed in the chest radiation group over 10 years; survival rates did not significantly differ between gene mutation and familial risk groups.
Conclusions:
- Age and specific tumor characteristics are vital for identifying high-risk breast cancer subgroups.
- MRI is a highly effective screening tool for high-risk individuals.
- Early detection significantly impacts survival outcomes, particularly for aggressive cancers in gene mutation carriers, supporting personalized care strategies.

