The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

• Published in: Scientific data Vol. 9; no. 1; pp. 453 - 12
• Main Authors: Bakas, Spyridon, Sako, Chiharu, Akbari, Hamed, Bilello, Michel, Sotiras, Aristeidis, Shukla, Gaurav, Rudie, Jeffrey D., Santamaría, Natali Flores, Kazerooni, Anahita Fathi, Pati, Sarthak, Rathore, Saima, Mamourian, Elizabeth, Ha, Sung Min, Parker, William, Doshi, Jimit, Baid, Ujjwal, Bergman, Mark, Binder, Zev A., Verma, Ragini, Lustig, Robert A., Desai, Arati S., Bagley, Stephen J., Mourelatos, Zissimos, Morrissette, Jennifer, Watt, Christopher D., Brem, Steven, Wolf, Ronald L., Melhem, Elias R., Nasrallah, MacLean P., Mohan, Suyash, O’Rourke, Donald M., Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.07.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/245/1628; 631/67/1922; 692/308/53; 692/308/575; 706/648/697; Adult; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain Neoplasms - physiopathology; Brain tumors; Data Descriptor; Datasets; Demography; Genomics; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - genetics; Glioblastoma - physiopathology; Humanities and Social Sciences; Humans; Magnetic Resonance Imaging; multidisciplinary; Neuroimaging; Prognosis; Radiomics; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-022-01560-7

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the “University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics” (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments. Measurement(s) Magnetic Resonance Imaging Technology Type(s) Magnetic Resonance Imaging of the Brain with and without Contrast Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment brain Sample Characteristic - Location United States of America