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<p>This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge in cancer treatment. 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. The dataset’s uniqueness lies in its detailed focus on metastatic breast cancer to the brain–offering a rich resource for advanced image-based tumor phenotyping and the vast potential for radiogenomic-based predictions based on machine learning model development. The inclusion of clinician-reviewed tumor segmentations and radiomic features, encompassing shape and texture characteristics, enhances the dataset’s utility. This dataset aims to facilitate a deeper understanding of breast cancer metastasis to the brain, promote advancements in precision medicine, and improve patient care.</p>

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An Integrated Dataset of Metastatic Breast Cancer to the Brain with Imaging, Radiomics, and Tumor Genetics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Birra R. Taha,
  • David J. Wu,
  • Luke T. Sabal,
  • Megan Kollitz,
  • Lindsey Sloan,
  • B. Aika Shoo,
  • Jianling Yuan,
  • Matthew Hunt,
  • Michael C. Park,
  • David Darrow,
  • Andrew S. Venteicher,
  • Yoichi Watanabe

摘要

This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge in cancer treatment. 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. The dataset’s uniqueness lies in its detailed focus on metastatic breast cancer to the brain–offering a rich resource for advanced image-based tumor phenotyping and the vast potential for radiogenomic-based predictions based on machine learning model development. The inclusion of clinician-reviewed tumor segmentations and radiomic features, encompassing shape and texture characteristics, enhances the dataset’s utility. This dataset aims to facilitate a deeper understanding of breast cancer metastasis to the brain, promote advancements in precision medicine, and improve patient care.