Summary
Excerpt |
---|
Put Collection Abstract hereThis collection comprise multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the Hospital of the University of Pennsylvania, coupled with patient demographics, clinical outcome (e.g., overall survival, progression free survival, molecular alterations of 50 clinically-relevant genes based on a novel next generation sequencing panel, computer-aided and manually-corrected segmentation labels of multiple histologically distinct tumor sub-regions, computer-aided and manually-corrected segmentations of the whole brain, a rich panel of radiomic features along with their corresponding co-registered mpMRI volumes in NIfTI format. Pre-operative scans defined via radiological assessment for prior surgical instrumentation. Scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated computational method. These segmentation labels were revised and any label misclassifications were manually corrected/approved by an expert board-certified neuroradiologists. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered, and hence allow associations with molecular markers (radiogenomic biomarker research), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one
NIH/NINDS: R01NS042645, NIH/NCI: U24CA189523, NIH/NCATS: UL1TR001878, and the ITMAT of the University of Pennsylvania.
Localtab Group |
---|
Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data AccessData Type | Download all or Query/Filter |
---|
Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | ![tcia_wiki_download_button.png](/plugins/servlet/confluence/placeholder/unknown-attachment?locale=en_US&version=2) ![tcia_wiki_search_button.png](/plugins/servlet/confluence/placeholder/unknown-attachment?locale=en_US&version=2)
(Download requires the NBIA Data Retriever) | Tissue Slide Images (SVS, XX.X GB) | | Clinical data (CSV) | | Genomics (web) | |
Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.net with any questions regarding usage. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed DescriptionImage Statistics |
|
---|
Modalities |
| Number of Patients |
| Number of Studies |
| Number of Series |
| Number of Images |
| Images Size (GB) |
|
<< Add any additional information as needed below. Likely would be something from site. >>
|
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage PolicyAdd any special restrictions in here. These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 4.0 International License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work: Info |
---|
| DOI goes here. Create using Datacite with information from Collection Approval form |
Info |
---|
title | Publication Citation |
---|
| We ask on the proposal form if they have ONE traditional publication they'd like users to cite. |
Info |
---|
| Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal. |
Info |
---|
| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
Localtab |
---|
| Version X (Current): Updated yyyy/mm/ddData Type | Download all or Query/Filter |
---|
Images (DICOM, xx.x GB) | | Clinical Data (CSV) | Link | Other (format) | |
<< One or two sentences about what you changed since last version. No note required for version 1. >> |
|
...