AI-POWERED INSIGHT
An Integrated Dataset of Metastatic Breast Cancer to the Brain with Imaging, Radiomics, and Tumor Genetics
This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge. Comprising 297 T1-weighted post-contrast images from 165 patients, this dataset from the University of Minnesota Medical Center is the first dedicated to breast cancer brain metastases. This collection includes expert-reviewed lesion segmentations with original image files, genetic markers, and an extensive array of tumor-derived radiomic features.
Executive Impact & Key Metrics
Leverage detailed radiogenomic insights to enhance precision medicine and improve patient outcomes.
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Patients Included
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MRI Images Collected
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Radiomic Features Extracted
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RF Classifier AUC for HER2 Prediction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Comprehensive Data Processing Pipeline
Our methodology ensures high-quality, comparable data by following a rigorous, multi-stage processing pipeline, crucial for robust radiogenomic analysis and machine learning model development.
Enterprise Process Flow
Image Acquisition (3D T1-w post-contrast MRI)
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Brain Extraction (HD-BET)
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N4 Bias Correction
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Clinician-Reviewed Tumor Segmentation
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Radiomic Feature Extraction (PyRadiomics)
This systematic workflow, from raw imaging data to extracted radiomic features, underpins the analytical rigor of the dataset, enabling advanced insights into breast cancer brain metastases. The inclusion of clinician-reviewed segmentations ensures high fidelity of the extracted features.
Calculate Your Enterprise AI ROI
Estimate the potential cost savings and efficiency gains your organization could achieve by implementing advanced AI solutions.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your Enterprise AI Implementation Roadmap
A structured approach to integrating AI into your enterprise, ensuring maximum impact and minimal disruption.
Phase 1: Discovery & Strategy
Comprehensive assessment of current workflows, identification of AI opportunities, and development of a tailored AI strategy aligned with business objectives.
Phase 2: Pilot & Validation
Development and deployment of a small-scale pilot project to test AI solutions, validate assumptions, and gather initial performance metrics.
Phase 3: Scaled Integration
Full-scale integration of validated AI solutions across relevant departments, including data migration, system interoperability, and user training.
Phase 4: Optimization & Future-Proofing
Continuous monitoring, performance optimization, and strategic planning for future AI advancements and expanded applications within the enterprise.
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