Scholarlink

<p>Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marker profile. Traditionally, molecular markers are determined through pathological testing on a tissue specimen acquired through biopsy. Several Magnetic Resonance Imaging (MRI) based Deep Learning (DL) methods offer a promising, non-invasive approach to predict these markers. However, they often require high-quality, well-annotated datasets. To support this need, we present a well-curated brain tumor dataset developed at The University of Texas Southwestern (UTSW) Medical Center. This dataset includes multi-contrast-MRI, demographics, molecular-markers, and multi-label tumor segmentations for 625 patients treated at UTSW between 2006 and 2023. Each patient record contains four MRI contrasts: pre-contrast-T1w, post-contrast-T1w, T2w, and T2-weighted fluid-attenuated inversion recovery (T2w-FLAIR) images. The dataset also provides comprehensive genetic information, including IDH mutation-status, 1p19q co-deletion, MGMT promoter methylation, tumor-type, and tumor-grade. This dataset offers a valuable resource for exploring the relationship between MRI characteristics and tumor genetics. It also serves as a robust benchmark for developing and validating DL models for various downstream tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

查看全文
  • Divya D. Reddy,
  • Niloufar Saadat,
  • James M. Holcomb,
  • Benjamin C. Wagner,
  • Nghi C. Truong,
  • Jason Bowerman,
  • Kimmo J. Hatanpaa,
  • Toral R. Patel,
  • Marco C. Pinho,
  • Fang Yu,
  • Kuan Zhang,
  • Sadeem Lodhi,
  • Ananth J. Madhuranthakam,
  • Chandan Ganesh Bangalore Yogananda,
  • Joseph A. Maldjian

摘要

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marker profile. Traditionally, molecular markers are determined through pathological testing on a tissue specimen acquired through biopsy. Several Magnetic Resonance Imaging (MRI) based Deep Learning (DL) methods offer a promising, non-invasive approach to predict these markers. However, they often require high-quality, well-annotated datasets. To support this need, we present a well-curated brain tumor dataset developed at The University of Texas Southwestern (UTSW) Medical Center. This dataset includes multi-contrast-MRI, demographics, molecular-markers, and multi-label tumor segmentations for 625 patients treated at UTSW between 2006 and 2023. Each patient record contains four MRI contrasts: pre-contrast-T1w, post-contrast-T1w, T2w, and T2-weighted fluid-attenuated inversion recovery (T2w-FLAIR) images. The dataset also provides comprehensive genetic information, including IDH mutation-status, 1p19q co-deletion, MGMT promoter methylation, tumor-type, and tumor-grade. This dataset offers a valuable resource for exploring the relationship between MRI characteristics and tumor genetics. It also serves as a robust benchmark for developing and validating DL models for various downstream tasks.