Cargando…

The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma

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 unknow...

Descripción completa

Detalles Bibliográficos
Autores principales: van der Voort, Sebastian R., Incekara, Fatih, Wijnenga, Maarten M.J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J.P.E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Smits, Marion
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203723/
https://www.ncbi.nlm.nih.gov/pubmed/34159239
http://dx.doi.org/10.1016/j.dib.2021.107191
Descripción
Sumario: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.