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标题：Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
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作者：anousheh Sayah ; Camelia Bencheqroun ; Krithika Bhuvaneshwar 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-9
DOI：10.1038/s41597-022-01415-1
语种：English
出版社：Nature Publishing Group
摘要：Malignancy of the brain and CNS is unfortunately a common diagnosis . A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide . The complex nature of the brain tissue environment in which these lesions arise ofers a rich opportunity for translational research . Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease . Recent years has seen rapid growth in the feld of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions . In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain . Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format . This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository (https://www.nitrc.org/projects/ rembrandt_brain/) .