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)...
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| Published in | Scientific data Vol. 9; no. 1; pp. 453 - 12 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
29.07.2022
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| 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.
Measurement(s)
Magnetic Resonance Imaging
Technology Type(s)
Magnetic Resonance Imaging of the Brain with and without Contrast
Sample Characteristic - Organism
Homo sapiens
Sample Characteristic - Environment
brain
Sample Characteristic - Location
United States of America |
|---|---|
| AbstractList | 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. 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. 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.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. 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. Measurement(s) Magnetic Resonance Imaging Technology Type(s) Magnetic Resonance Imaging of the Brain with and without Contrast Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment brain Sample Characteristic - Location United States of America Measurement(s) Magnetic Resonance Imaging Technology Type(s) Magnetic Resonance Imaging of the Brain with and without Contrast Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment brain Sample Characteristic - Location United States of America 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.Measurement(s)Magnetic Resonance ImagingTechnology Type(s)Magnetic Resonance Imaging of the Brain with and without ContrastSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmentbrainSample Characteristic - LocationUnited States of America |
| ArticleNumber | 453 |
| Author | Parker, William Bergman, Mark Shukla, Gaurav Baid, Ujjwal Lustig, Robert A. Santamaría, Natali Flores Bagley, Stephen J. Rathore, Saima Akbari, Hamed Bilello, Michel Brem, Steven Watt, Christopher D. O’Rourke, Donald M. Binder, Zev A. Desai, Arati S. Verma, Ragini Kazerooni, Anahita Fathi Sotiras, Aristeidis Bakas, Spyridon Mamourian, Elizabeth Ha, Sung Min Doshi, Jimit Sako, Chiharu Morrissette, Jennifer Nasrallah, MacLean P. Davatzikos, Christos Melhem, Elias R. Mourelatos, Zissimos Pati, Sarthak Wolf, Ronald L. Mohan, Suyash Rudie, Jeffrey D. |
| Author_xml | – sequence: 1 givenname: Spyridon orcidid: 0000-0001-8734-6482 surname: Bakas fullname: Bakas, Spyridon organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 2 givenname: Chiharu orcidid: 0000-0003-3243-3954 surname: Sako fullname: Sako, Chiharu organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 3 givenname: Hamed orcidid: 0000-0001-9786-3707 surname: Akbari fullname: Akbari, Hamed organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 4 givenname: Michel orcidid: 0000-0001-6313-5437 surname: Bilello fullname: Bilello, Michel organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 5 givenname: Aristeidis orcidid: 0000-0003-0795-8820 surname: Sotiras fullname: Sotiras, Aristeidis organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Radiology and Institute for Informatics, Washington University, School of Medicine – sequence: 6 givenname: Gaurav orcidid: 0000-0001-7605-9365 surname: Shukla fullname: Shukla, Gaurav organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Radiation Oncology, Christiana Care Health System – sequence: 7 givenname: Jeffrey D. orcidid: 0000-0001-8609-8421 surname: Rudie fullname: Rudie, Jeffrey D. organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Radiology & Biomedical Imaging, University of California, San Francisco – sequence: 8 givenname: Natali Flores orcidid: 0000-0003-1024-2428 surname: Santamaría fullname: Santamaría, Natali Flores organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 9 givenname: Anahita Fathi orcidid: 0000-0001-7131-2261 surname: Kazerooni fullname: Kazerooni, Anahita Fathi organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 10 givenname: Sarthak orcidid: 0000-0003-2243-8487 surname: Pati fullname: Pati, Sarthak organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 11 givenname: Saima orcidid: 0000-0002-9818-2978 surname: Rathore fullname: Rathore, Saima organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 12 givenname: Elizabeth orcidid: 0000-0001-8581-4887 surname: Mamourian fullname: Mamourian, Elizabeth organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 13 givenname: Sung Min orcidid: 0000-0002-3526-9101 surname: Ha fullname: Ha, Sung Min organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Radiology and Institute for Informatics, Washington University, School of Medicine – sequence: 14 givenname: William orcidid: 0000-0001-8017-0525 surname: Parker fullname: Parker, William organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 15 givenname: Jimit orcidid: 0000-0002-2875-5814 surname: Doshi fullname: Doshi, Jimit organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 16 givenname: Ujjwal orcidid: 0000-0001-5246-2088 surname: Baid fullname: Baid, Ujjwal organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 17 givenname: Mark orcidid: 0000-0003-1220-6906 surname: Bergman fullname: Bergman, Mark organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania – sequence: 18 givenname: Zev A. orcidid: 0000-0003-1158-231X surname: Binder fullname: Binder, Zev A. organization: Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania – sequence: 19 givenname: Ragini orcidid: 0000-0002-7479-1007 surname: Verma fullname: Verma, Ragini organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 20 givenname: Robert A. orcidid: 0000-0003-0633-3802 surname: Lustig fullname: Lustig, Robert A. organization: Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania – sequence: 21 givenname: Arati S. orcidid: 0000-0002-4849-4703 surname: Desai fullname: Desai, Arati S. organization: Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania – sequence: 22 givenname: Stephen J. orcidid: 0000-0002-7117-0539 surname: Bagley fullname: Bagley, Stephen J. organization: Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania – sequence: 23 givenname: Zissimos orcidid: 0000-0002-9852-1845 surname: Mourelatos fullname: Mourelatos, Zissimos organization: Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 24 givenname: Jennifer orcidid: 0000-0001-9460-7177 surname: Morrissette fullname: Morrissette, Jennifer organization: Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 25 givenname: Christopher D. surname: Watt fullname: Watt, Christopher D. organization: Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 26 givenname: Steven orcidid: 0000-0002-5803-8920 surname: Brem fullname: Brem, Steven organization: Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania – sequence: 27 givenname: Ronald L. orcidid: 0000-0003-1887-3697 surname: Wolf fullname: Wolf, Ronald L. organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania – sequence: 28 givenname: Elias R. surname: Melhem fullname: Melhem, Elias R. organization: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine – sequence: 29 givenname: MacLean P. orcidid: 0000-0003-4861-0898 surname: Nasrallah fullname: Nasrallah, MacLean P. organization: Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania – sequence: 30 givenname: Suyash orcidid: 0000-0002-4025-115X surname: Mohan fullname: Mohan, Suyash organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 31 givenname: Donald M. orcidid: 0000-0002-8479-7314 surname: O’Rourke fullname: O’Rourke, Donald M. organization: Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania – sequence: 32 givenname: Christos orcidid: 0000-0002-1025-8561 surname: Davatzikos fullname: Davatzikos, Christos email: christos.davatzikos@pennmedicine.upenn.edu organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35906241$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s) 2022 2022. The Author(s). The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2022 – notice: 2022. The Author(s). – notice: The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Title | The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics |
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