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IVYGAP

The Cancer Imaging Archive

IvyGAP | Ivy Glioblastoma Atlas Project

DOI: 10.7937/K9/TCIA.2016.XLwaN6nL | Data Citation Required | 442 Views | 27 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Brain Human 39 MR Glioblastoma 139.98GB Clinical, Genomics Limited, Complete 2016/12/30

Summary

This data collection consists of MRI/CT scan data for brain tumor patients that form the cohort for the resource Ivy Glioblastoma Atlas Project (Ivy GAP). There are 390 studies for 39 patients that include pre-surgery, post-surgery and follow up scans. The 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.

These resources represent an unprecedented platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels. In addition to the DICOM images in TCIA there are two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities:

  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.

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

Data Access

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.

Version 1: Updated 2016/12/30

Title Data Type Format Access Points Subjects Studies Series Images License
Images MR DICOM
Download requires NBIA Data Retriever
 39 390 5,223 846,743 TCIA Restricted
Related Datasets
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Additional Resources for this Dataset

IvyGap provides access to additional external resources for this data.

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data 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. https://doi.org/10.7937/K9/TCIA.2016.XLwaN6nL

Detailed Description

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).

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication 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. https://doi.org/10.1126/science.aaf2666

No other publications were recommended by dataset authors.

Publication 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. https://doi.org/10.1126/science.aaf2666

Research Community Publications

TCIA maintains a list of publications that leverage our data. If you have a publication you’d like to add, please contact 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

 

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

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 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