TY - JOUR
T1 - The University of Pennsylvania glioblastoma (UPenn-GBM) cohort
T2 - advanced MRI, clinical, genomics, & radiomics
AU - Bakas, Spyridon
AU - Sako, Chiharu
AU - Akbari, Hamed
AU - Bilello, Michel
AU - Sotiras, Aristeidis
AU - Shukla, Gaurav
AU - Rudie, Jeffrey D.
AU - Santamaría, Natali Flores
AU - Kazerooni, Anahita Fathi
AU - Pati, Sarthak
AU - Rathore, Saima
AU - Mamourian, Elizabeth
AU - Ha, Sung Min
AU - Parker, William
AU - Doshi, Jimit
AU - Baid, Ujjwal
AU - Bergman, Mark
AU - Binder, Zev A.
AU - Verma, Ragini
AU - Lustig, Robert A.
AU - Desai, Arati S.
AU - Bagley, Stephen J.
AU - Mourelatos, Zissimos
AU - Morrissette, Jennifer
AU - Watt, Christopher D.
AU - Brem, Steven
AU - Wolf, Ronald L.
AU - Melhem, Elias R.
AU - Nasrallah, MacLean L.P.
AU - Mohan, Suyash
AU - O’Rourke, Donald M.
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85135180459&partnerID=8YFLogxK
U2 - 10.1038/s41597-022-01560-7
DO - 10.1038/s41597-022-01560-7
M3 - Article
C2 - 35906241
AN - SCOPUS:85135180459
SN - 2052-4463
VL - 9
JO - Scientific data
JF - Scientific data
IS - 1
M1 - 453
ER -