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Summary

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locationhttps://www.cancerimagingarchive.net/collection/ivygap/

Excerpt

This data collection consists of MRI/CT scan data for brain tumor patients that form the cohort for the resource Ivy Glioblastoma Atlast Atlas Project (Ivy GAP). There are 390 studies for 39 patients that include pre-surgery, post-surgery and follow up scans. The Ivy Glioblastoma Atlas Project (Ivy GAP ) is a collaborative partnership between the Ben and Catherine Ivy Foundation, which generously provided the financial support, the Allen Institute for Brain Science, and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment. The goal of the project is to provide online resources to scientists and physicians dedicated to the development of innovative treatments and diagnostics that will enhance the quality of life and survival of patients with brain cancer.

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  1. Ivy Glioblastoma Atlas Project - An open/public database providing in situ hybridization (ISH) and RNA sequencing (RNA-Seq) data, which map gene expression across the anatomic structures and putative cancer stem cell clusters in glioblastoma. The associated histological dataset is annotated and is suitable for neuropathological examination.
  2. Ivy GAP Clinical and Genomic Database -  A database offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access.

For scientific inquiries about this dataset, please contact: Nameeta Shah Ph.D. ( nameeta.shah@gmail.com )

The Ivy GAP is described in the resource paper (70 authors not all listed here) : Puchalski, R. B., Shah, N., …, Foltz, G. D. (2018). An anatomic transcriptional atlas of human glioblastoma. In Science (Vol. 360, Issue 6389, pp. 660–663).  https://doi.org/10.1126/science.aaf2666

For scientific or other inquiries about this dataset, please contact the TCIA Helpdesk. 


Localtab Group



Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

Click the Versions tab for more info about data releases.
Localtab
activetrue
titleData Access

Data Access

 Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search option.

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Data TypeDownload all or Query/FilterLicense
Images (DICOM, 130.4GB)
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urlhttps://wiki.cancerimagingarchive.net/download/attachments/22515597/doiJNLP-IvyGAP.tcia?version=1&modificationDate=1534787022177&api=v2



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labelSearch
urlhttps://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=IvyGAP



(Download requires the NBIA Data Retriever)

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Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

IvyGap provides access to additional resources for this data.

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Localtab
titleDetailed Description

Detailed Description


Collection Statistics

 


Modalities

MRI, CT

MR

Number of

Patients

Participants

39

Number of Studies

390

Number of Series

5223

Number of Images

846743

Image Size (GB)130.4


Supporting Documentation

In addition to the DICOM images in TCIA there are two additional databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities:

  1. The Ivy Glioblastoma Atlas Project web site includes the following data:
    1. ISH: Image data at cellular resolution of in situ hybridization (ISH) tissue sections and adjacent hematoxylin and eosin (H&E)-stained sections annotated for anatomic structures
      1. Anatomic Structures ISH Survey: Primary screen of 8 tumors with probes for 343 genes enriched in glioblastoma.
      2. Anatomic Structures ISH for Enriched Genes: Subsequent screen of 29 tumors with probes for 37 genes enriched in glioblastoma structures identified in Anatomic Structures RNA-Seq Study (see below).
      3. Cancer Stem Cells ISH Survey: Primary screen of 16 tumors with probes for 55 genes enriched in putative cancer stem cells, resulting in a 20 probe reference set, which was then used in an extensive screen of 42 tumors.
      4. Cancer Stem Cells ISH for Enriched Genes: Subsequent screen of 37 tumors with probes for 76 genes enriched in clusters of putative cancer stem cells identified in the Cancer Stem Cells RNA-Seq Study (see below).
    2. RNA-Seq: RNA sequencing data for anatomic structures identified in the Anatomic Structures ISH Survey and putative cancer stem cell clusters isolated by laser microdissection
      1. Anatomic Structures RNA-Seq: Screen of 5 structures (Leading Edge, Infiltrating Tumor, Cellular Tumor, Microvascular Proliferation, and Pseudopalisading Cells Around Necrosis) identified by H&E staining. A total of 122 RNA samples were generated from 10 tumors.
      2. Cancer Stem Cells RNA-Seq: Screen of 35 clusters of putative cancer stem cells identified by ISH with a 17 reference probe subset (validated in the Cancer Stem Cells ISH Survey). A total of 148 RNA samples were generated from 34 tumors.
    3. Specimen Metadata: De-identified clinical data for each patient and tumor.
  2. The Ivy GAP Clinical and Genomic Database contains detailed clinical information including pathology images, genomic data, and prospectively collected outcomes data. This site requires separate registration.
  3.  Additionally, the pathology images from this study are also available externally from here on Amazon Web Services (AWS). 




Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

 This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Tcia limited license policy

Info
titleIvy GAP Data Citation

  Nameeta Shah, PhN.D., Xu Feng, Ph.DX., Michael Lankerovich, Ph.DM., Ralph B. Puchalski, PhR. DB., Bart & Keogh, M.D., Ph.DB. (2016). Data from Ivy GAPGlioblastoma Atlas Project (IvyGAP) [Data set]. The Cancer Imaging Archive. httphttps://dx.doi.org/10.7937/K9/TCIA.2016.XLwaN6nL


Info
titlePublication Citation
Coming soonPuchalski RB, Shah N, Miller J, Dalley R, Nomura SR, Yoon J-G, Smith KA, Lankerovich M, Bertagnolli D, Bickley K, Boe AF, Brouner K, Butler S, Caldejon S, Chapin M, Datta S, Dee N, Desta T, Dolbeare T, Dotson N, Ebbert A, Feng D, Feng X, Fisher M, Gee G, Goldy J, Gourley L, Gregor BW, Gu G, Hejazinia N, Hohmann J, Hothi P, Howard R, Joines K, Kriedberg A, Kuan L, Lau C, Lee F, Lee H, Lemon T, Long F, Mastan N, Mott E, Murthy C, Ngo K, Olson E, Reding M, Riley Z, Rosen D, Sandman D, Shapovalova N, Slaughterbeck CR, Sodt A, Stockdale G, Szafer A, Wakeman W, Wohnoutka PE, White SJ, Marsh D, Rostomily RC, Ng L, Dang C, Jones A, Keogh B, Gittleman HR, Barnholtz-Sloan JS, Cimino PJ, Uppin MS, Keene CD, Farrokhi FR, Lathia JD, Berens ME, Iavarone A, Bernard A, Lein E, Phillips JW, Rostad SW, Cobbs C, Hawrylycz MJ, Foltz GD. (2018). An anatomic transcriptional atlas of human glioblastoma. Science, 360(6389), 660–663. https://doi.org/10.1126/science.aaf2666


Info
titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013).  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. (paper)), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7


Other Publications Using This Data

TCIA maintains a list of publications which that leverage our data. At this time we are not aware of any publications based on this data. If you have a publication you'd like to add, please contact the TCIA's Helpdesk.

  1. Beig, N., Bera, K., Prasanna, P., Antunes, J., Correa, R., Singh, S., . . . Tiwari, P. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clin Cancer Res. doi:10.1158/1078-0432.CCR-19-2556
  2. Gevaert, O., Nabian, M., Bakr, S., Everaert, C., Shinde, J., Manukyan, A., . . . Pochet, N. (2020). Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes. JCO Clin Cancer Inform, 4, 421-435. doi:10.1200/CCI.19.00125
  3. Le, N. Q. K., Hung, T. N. K., Do, D. T., Lam, L. H. T., Dang, L. H., & Huynh, T.-T. (2021). Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med, 132, 104320. doi:10.1016/j.compbiomed.2021.104320
  4. Mi, E., Mauricaite, R., Pakzad-Shahabi, L., Chen, J., Ho, A., & Williams, M. (2022). Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma. Br J Cancer, 126(2), 196-203. doi:10.1038/s41416-021-01590-9 
  5. Miller, T. E., Liau, B. B., Wallace, L. C., Morton, A. R., Xie, Q., Dixit, D., . . . Rich, J. N. (2017). Transcription elongation factors represent in vivo cancer dependencies in glioblastoma. Nature, 547(7663), 355. doi:10.1038/nature23000
  6. Puchalski, R. B., Shah, N., Miller, J., Dalley, R., Nomura, S. R., Yoon, J.-G., . . . Foltz, G. D. (2018). An anatomic transcriptional atlas of human glioblastoma. Science, 360(6389), 660-663. doi:10.1126/science.aaf2666
  7. Soike, M. H., McTyre, E. R., Shah, N., Puchalski, R. B., Holmes, J. A., Paulsson, A. K., . . . Strowd, R. E. (2018). Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns? Neuroradiology, 1-9. doi:10.1007/s00234-018-2060-y
  8. van der Voort, S. R., Incekara, F., Wijnenga, M. M. J., Kapsas, G., Gahrmann, R., Schouten, J. W., . . . Klein, S. (2022). Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro Oncol. doi:10.1093/neuonc/noac166
  9. Verma, R., Hill, V. B., Statsevych, V., Bera, K., Correa, R., Leo, P., . . . Tiwari, P. (2022). Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study. American Journal of Neuroradiology, 43(8), 1115-1123. doi:10.3174/ajnr.A7591
  10. Vo, V. T. A., Kim, S., Hua, T. N. M., Oh, J., & Jeong, Y. (2022). Iron commensalism of mesenchymal glioblastoma promotes ferroptosis susceptibility upon dopamine treatment. Communications Biology, 5(1). doi:10.1038/s42003-022-03538-y
  11. Zander, E., Ardeleanu, A., Singleton, R., Bede, B., Wu, Y., & Zheng, S. (2022). A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients. Neurooncol Adv, 4(1), vdab167.
  1. doi:10.1093/noajnl/vdab167
  2. Zheng, S., & Tao, W. (2021). Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme. Cancers (Basel), 13(5). doi:10.3390/cancers13051013

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 Localtab
titleVersions
Version 1 (Current): Updated 2016/12/30


   
Data TypeDownload all or Query/Filter
Images (DICOM, 130.4GB)
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 Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/22515597/doiJNLP-IvyGAP.tcia?version=1&modificationDate=1534787022177&api=v2


 Tcia button generator
labelSearch
urlhttps://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=IvyGAP

 

(Download requires the NBIA Data Retriever)
Summary ISH, RNA, gene expression and clinical data 
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(external)

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labelSearch
urlhttp://glioblastoma.alleninstitute.org/


Detailed clinical, genomic, and expression array data 
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(external)

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labelSearch
urlhttps://ivygap.org/home


Pathology images (external)

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labelSearch
urlhttps://registry.opendata.aws/allen-ivy-glioblastoma-atlas/