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The Ivy GAP is described in the resource paper (70 authors not all listed here) : Puchalski RB, R. B., Shah, N, …Foltz GD.  ., …, Foltz, G. D. (2018). An anatomic transcriptional atlas of human glioblastoma. Science360, 660-663 (2018). doi:. In Science (Vol. 360, Issue 6389, pp. 660–663).  

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


Localtab Group

titleData Access

Data Access

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Data TypeDownload all or Query/FilterLicense
Images (DICOM, 130.4GB)

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Additional Resources for this Dataset

IvyGap provides access to additional resources for this data.

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:

titleDetailed Description

Detailed Description

Collection Statistics



Number of Participants


Number of Studies


Number of Series


Number of Images


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). 

titleCitations & Data Usage Policy

Citations & Data Usage Policy 

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titleData Citation

    Shah, N., Feng, X., Lankerovich, M., Puchalski, R. B., & Keogh, B. (2016). Data from Ivy Glioblastoma Atlas Project (IvyGAP) [Data set]. The Cancer Imaging Archive.

titlePublication Citation
Puchalski 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.

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, 26(6), 1045–1057.

Other Publications Using This Data

TCIA maintains a list of publications that leverage our 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. doi:10.1093/noajnl/vdab167
  12. 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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Version 1 (Current): Updated 2016/12/30

Data TypeDownload all or Query/Filter
Images (DICOM, 130.4GB)

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Summary ISH, RNA, gene expression and clinical data (external)

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

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Pathology images (external)

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