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

Spyridon Bakas, Chiharu Sako, Hamed Akbari, Michel Bilello, Aristeidis Sotiras, Gaurav Shukla, Jeffrey D. Rudie, Natali Flores Santamaría, Anahita Fathi Kazerooni, Sarthak Pati, Saima Rathore, Elizabeth Mamourian, Sung Min Ha, William Parker, Jimit Doshi, Ujjwal Baid, Mark Bergman, Zev A. Binder, Ragini Verma, Robert A. LustigArati S. Desai, Stephen J. Bagley, Zissimos Mourelatos, Jennifer Morrissette, Christopher D. Watt, Steven Brem, Ronald L. Wolf, Elias R. Melhem, MacLean L.P. Nasrallah, Suyash Mohan, Donald M. O’Rourke, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish
Article number453
JournalScientific data
Volume9
Issue number1
DOIs
StatePublished - Dec 2022

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