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The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
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)...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9338035/ https://www.ncbi.nlm.nih.gov/pubmed/35906241 http://dx.doi.org/10.1038/s41597-022-01560-7 |
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author | 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 |
author_facet | 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 |
author_sort | Bakas, Spyridon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9338035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93380352022-07-31 The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics 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 Sci Data Data Descriptor 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. Nature Publishing Group UK 2022-07-29 /pmc/articles/PMC9338035/ /pubmed/35906241 http://dx.doi.org/10.1038/s41597-022-01560-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor 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 The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title_full | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title_fullStr | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title_full_unstemmed | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title_short | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
title_sort | university of pennsylvania glioblastoma (upenn-gbm) cohort: advanced mri, clinical, genomics, & radiomics |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9338035/ https://www.ncbi.nlm.nih.gov/pubmed/35906241 http://dx.doi.org/10.1038/s41597-022-01560-7 |
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