2014 QIN Face to Face Meeting

By Jayashree Kalpathy-Cramer, Laurence Clarke

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Abstract

The Quantitative Imaging Network (QIN) grows from the NCI program announcement "Quantitative Imaging for Evaluation of Responses to Cancer Therapies". The network is designed to promote research and development of quantitative imaging methods for the measurement of tumor response to therapies in clinical trial settings, with the overall goal of facilitating clinical decision making. Projects include the appropriate development and adaptation/implementation of quantitative imaging methods, imaging protocols, and software solutions/tools (using existing commercial imaging platforms and instrumentation) and application of these methods in current and planned clinical therapy trials. The projects are focusing on imaging-derived quantitative measurements of responses to drugs and/or radiation therapy, and/or image-guided interventions (IGI). To achieve the goals of the QIN, multidisciplinary teams that include oncologists as well as clinical and basic imaging scientists are required. The involvement of industrial partners in the development and adaptation/implementation of quantitative imaging methods to aid cancer therapies is encouraged.

The 2014 Meeting of the QIN took place at the Natcher Center on the NIH Main Campus, in Bethesda, Maryland, on March 27-28th.

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Researchers should cite this work as follows:

  • Jayashree Kalpathy-Cramer; Laurence Clarke (2014), "2014 QIN Face to Face Meeting," https://nciphub.org/resources/218.

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Tags

  1. Imaging
  2. NCI
  3. Network
  4. QIN
  5. Quantitative

In This Workshop

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  5. 2014 QIN Abstract Book

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    18 Apr 2014 | Publications | Contributor(s): Larry Clark

    The QIN Face-to-Face Meeting Presentations Posters and Abstracts.

  6. The Quantitative Imaging Network Meeting Introduction

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    18 Apr 2014 | Presentations | Contributor(s): Robert J.Nordstrom, Larry Clark, Pushpa Tandon, Yantian Zhang, Huiming Zhang, Lori Henderson, Lalitha Shankar, Keyvan Farahani, George redmond, James Deye, Jacek Capala, John Freyman, Justin Kirby

    Introductory remarks from the meeting sponsors.

  7. Translational Cancer Imaging

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    18 Apr 2014 | Presentations | Contributor(s): Tony Ng, David Hawkes, Shonit Punwani

  8. Data Acquisition Working Group

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    21 Apr 2014 | Presentations | Contributor(s): Tom Chenevert, Paul Kinahan

    DA-WG Update on PET-CT and DWI MRI

  9. Image Analysis and Performance Metrics WG - PET/CT Update

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    21 Apr 2014 | Presentations | Contributor(s): Jayashree Kalpathy-Cramer, Vyshak Chandra

    IAPM-WG PET/CT Update

  10. Image Analysis and Performance Metrics WG - DW and DCE MRI Update

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    21 Apr 2014 | Presentations | Contributor(s): Wei Huang, Tom Chenevert, Vyshak Chandra

    IAPM-WG - DW and DCE MRI Update

  11. Clinical Trials Design, Development, and Outreach Working Group

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    21 Apr 2014 | Presentations | Contributor(s): Richard Wahl, John Buatti, Vyshak Chandra

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  12. Bioinformatics/IT and Data Sharing Working Group Update

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    21 Apr 2014 | Presentations | Contributor(s): Brad Erickson, Tom Casavant, John Freymann, Vyshak Chandra

    Bioinformatics/IT and Data Sharing Working Group Update

  13. Hub Zero Status Update

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    21 Apr 2014 | Presentations | Contributor(s): Jayashree Kalpathy-Cramer, Vyshak Chandra

    NCIP Hub, an environment to facilitate collaboration within the Quantitative Imaging Network

  14. Slicer Update

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    21 Apr 2014 | Presentations | Contributor(s): Andrey Fedorov, Ron Kikinis

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  15. Quantitative MRI of Prostate Cancer as a Biomarker and Guide for Treatment

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  16. Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model

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    A critical barrier to the development of new cancer therapies is the lack of evidence validating the imaging methods used for patient response assessment. Our project evaluates the ability of improved image analysis techniques, e.g., tumor volume and necrosis fraction, to assess response in...

  17. Clinical Trials Design and Development Working Group

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    30 Apr 2014 | Teaching Materials | Contributor(s): Richard L. Wahl, John Buatti, Brenda Kurland, James M. Mountz, Daniel Rubin, Christopher Ryan, Edward Eikman, Elizabeth Gerstner, Frank S. Lieberman

    This poster includes the Goals, Timeline and Specific Project information for the Working Group

  18. Data Acquisition Work Group

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    30 Apr 2014 | Teaching Materials | Contributor(s): Paul E. Kinahan, Thomas L. Chenevert

    Information and Results regarding the following projects: Diffusion MRI Demonstration Project: Gradient Non LinearityPET/CT Protocol variations and scanner calibration drift

  19. Image Analysis and Performance Metrics Working Group

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    30 Apr 2014 | Teaching Materials | Contributor(s): Michael Jacobs, David Newitt

    Information and results regarding the following projects:DCE-MRI Data Analysis ChallengePlatform-Dependent Image Scaling Challenge

  20. Quantitative MRSI to predict early response to SAHA therapy in GBM management

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    30 Apr 2014 | Teaching Materials | Contributor(s): Hyunsuk Shim, Hui-Kuo Shu, Jeffrey J. Olson, Xiaoping P. Hu, Tim Fox, Eduard Schreibmann, Chad A. Holder, Ying Guo, Andrew H. Miller, Daniel Brat, Alfredo Voloschin, Peter Barker, Matthias Holdhoff, Stuart Grossman, Doris Lin, Lawrence Kleinberg, Fausto Rodriguez, Andrew Maudsley

  21. Multiparametric and Multimodality Quantitative Imaging for Evaluation of Response to Cancer Therapy

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    30 Apr 2014 | Teaching Materials | Contributor(s): Eric C. Frey, Michael A. Jacobs, Martin A. Lodge, Hao Wang, Richard L. Wahl

    The overall goal of this U01 is to combine quantitative parameters from multiple modalities, in particular PET/CT, SPECT/CT and MRI, to provide a better prediction or assessment of therapeutic response. We will use different tracers in PET and SPECT and advanced MRI methods. In the project we...

  22. Objective Selection of GBM Imaging Features for Clinical Trials

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    30 Apr 2014 | Teaching Materials | Contributor(s): Bradley Erickson, Daniel Blezek, Panos Korfiatis, Jian Su, Ross Mitchell

    We focus on identifying MRI features that predict patient survival and response in both low grade and high grade brain tumors.

  23. QUANTITATIVE MRI OF GLIOBLASTOMA RESPONSE

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    30 Apr 2014 | Teaching Materials | Contributor(s): Bruce Rosen

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    30 Apr 2014 | Teaching Materials | Contributor(s): Robert Gillies, Robert Gatenby, Dmitry Goldgof, Philippe Lambin, Yoganand Balagurunathan, Yuhua Gu, Olya Grove, Hua Wang, Lawrence Hall, Matthew Schabath, Jhanelle Gray, Eduardo Moros, Thomas Dilling, Jongphil Kim, Anders Berglund, Steven Eschrich, Gregory Bloom, John Heine, Andre Dekker, Hugo Aerts, Emmanuel Rios

  25. Prognostic Value of Tumor Hypoxia, as measured by 18F-FMISO Breath-Hold PET/CT, in NSCLC

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    30 Apr 2014 | Teaching Materials | Contributor(s): S.A. Nehmeh, J. Schwartz, R. Schmidtlein, H. Schoder, A. Rimner, J. Chaft, M. Grkowski, J. Humm

  26. Evaluation of HCC Response to Systemic Therapy with Quantitative MRI

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    30 Apr 2014 | Teaching Materials | Contributor(s): Guido Jajmovich, Octavia Bane, Hadrien Dyvorne, Bachir Taouli

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  27. Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy

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    30 Apr 2014 | Teaching Materials | Contributor(s): Wei Huang, Christopher Ryan

  28. The PET/CT Working Group: 2013-2014

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    30 Apr 2014 | Teaching Materials | Contributor(s): The PET/CT Working Group

  29. Quantitative Biomarker Imaging for Early Therapy Response Assessment in Cancer

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  30. Computerized Quantitative Imaging Assessment of Tumor Burden

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  31. Quantitative Imaging for Assessing Breast Cancer Response to Treatment

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    30 Apr 2014 | Teaching Materials | Contributor(s): Ella Jones, Lisa Wilmes, David Newitt, Sheye Aliu, Nola Hylton

  32. The University of Iowa: Quantitative Imaging to Assess Response in Cancer Therapy Trials

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  33. Quantitative DW-MRI for Early Breast Cancer Treatment Response Assessment

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    30 Apr 2014 | Teaching Materials | Contributor(s): Brian D. Ross, Craig J. Galbán, Anne Schott, Dariya I. Malyarenko, Ryan Chamberlain, Thomas L. Chenevert

  34. Reproducibility of FDG SUVmax for metastatic breast cancer lesions in the same or different PET/CT scanners in a local network

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    Accurate reproducibility of PET SUVs is important for patient management and for clinical trial design. Measuring and reducing SUV variability in PET scanners throughout a local area network can aid in monitoring patient response to therapy and may increase patient accrual to clinical trials

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