ArticleScientific data2022
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
113 citing papers in PubMed.
- Contralateral DTI-ALPS index as a prognostic biomarker in IDH-wildtype glioblastoma: integrating extent of resection and sex-stratified analyses.Neuroradiology · 2026Article
- SynPAR for Empirical Membership Inference Auditing of Synthetic Radiomic and Embedding Data.Journal of imaging informatics in medicine · 2026Article
- Validating Radiomic Feature Comparisons with Patient-Specific Image Surrogates.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging.Japanese journal of radiology · 2026Review
- Spatial organization of AQP4 water channel expression in the human brain.Science advances · 2026Article
- Effective tumor kinetics inferred from single routine H&E biopsies enable counterfactual virtual radiotherapy trials.medRxiv : the preprint server for health sciences · 2026Article
- NeuroRAD-FM: A Distributionally Robust Foundation Model for Precision Neuro-Oncology.Research square · 2026Article
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Glioblastoma MRI Dataset with Standardized Preprocessing, Expert-Validated Segmentation, and MGMT Profiling.Scientific data · 2026Article
- Radiomics Outperforms Conventional Tumor Volume in Prognostic Stratification Across Independent Glioma Cohorts.Cureus · 2026Article
- Technical architecture for integrating vision-language models with DICOM viewers.European radiology experimental · 2026Article
- Pan-cancer atlas of cellular architecture reveals a nearest-neighbour distance-associated biomechanical-immune axis involving CD4+ memory T cells.Clinical and translational medicine · 2026Article
- Validation of automated DTI-ALPS and free water index as potential survival stratification markers in IDH-wildtype glioblastoma.Neuroradiology · 2026Article
- Impact of Free Water Correction on the Mean Diffusivity of Glioblastoma is Regional and b-value Dependent: A Retrospective Study on Open-source Datasets.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Article
- Towards generalisable foundation models for brain MRI.Npj imaging · 2026Article
- Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival.Annals of clinical and translational neurology · 2026Article
- A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.Brain informatics · 2026Article
- The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.Scientific data · 2026Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
32 authors.
Funding
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.
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Registered trials
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