The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.
Spyridon BakasChiharu SakoHamed AkbariMichel BilelloAristeidis SotirasGaurav ShuklaJeffrey D RudieNatali Flores SantamaríaAnahita Fathi KazerooniSarthak PatiSaima RathoreElizabeth MamourianSung Min HaWilliam ParkerJimit DoshiUjjwal BaidMark BergmanZev A BinderRagini VermaRobert A LustigArati S DesaiStephen J BagleyZissimos MourelatosJennifer J D MorrissetteChristopher D WattSteven BremRonald L WolfElias R MelhemMac Lean P NasrallahSuyash MohanDonald M O'RourkeChristos DavatzikosPublished in: Scientific data (2022)
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.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- clinical practice
- high resolution
- computed tomography
- healthcare
- single cell
- electronic health record
- randomized controlled trial
- end stage renal disease
- ejection fraction
- newly diagnosed
- squamous cell carcinoma
- rna seq
- lymph node metastasis
- emergency department
- prognostic factors
- young adults
- fluorescence imaging
- case control
- health information
- data analysis
- artificial intelligence