دورية أكاديمية
The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma
| العنوان: | The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma |
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| المؤلفون: | Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W. Schouten, Hendrikus J. Dubbink, Arnaud J.P.E. Vincent, Martin J. van den Bent, Pim J. French, Stefan Klein, Marion Smits |
| المصدر: | Data in Brief, Vol 37, Iss , Pp 107191- (2021) |
| بيانات النشر: | Elsevier, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Science (General) |
| مصطلحات موضوعية: | Magnetic resonance imaging, Glioma, Radiomics, Segmentation, Genetics, WHO 2016, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390 |
| الوصف: | The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19–86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2352-3409 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2352340921004753Test; https://doaj.org/toc/2352-3409Test |
| DOI: | 10.1016/j.dib.2021.107191 |
| الوصول الحر: | https://doaj.org/article/ea99ca575e494146a9cfc51f01f33990Test |
| رقم الانضمام: | edsdoj.99ca575e494146a9cfc51f01f33990 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23523409 |
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| DOI: | 10.1016/j.dib.2021.107191 |