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2014 QIN Face to Face Meeting

By Jayashree Kalpathy-Cramer, Laurence Clarke

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Workshops

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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.

Cite this work

Researchers should cite this work as follows:

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

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In This Workshop

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

    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...

  2. Objective Selection of GBM Imaging Features for Clinical Trials

    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.

  3. QUANTITATIVE MRI OF GLIOBLASTOMA RESPONSE

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

  4. RADIOMICS ON LUNG CANCER

    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

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

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

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

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

    Overall objective: Develop a quantitative multiparametric protocol combining measurements of MR diffusion, perfusion and hypoxia in hepatocellular carcinoma (HCC)

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

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

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

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

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

    30 Apr 2014 | Teaching Materials | Contributor(s): James M. Mountz

  10. Computerized Quantitative Imaging Assessment of Tumor Burden

    30 Apr 2014 | Teaching Materials | Contributor(s): Daniel L. Rubin, Sandy Napel, Edward Graves, Andrew Quon, George Fisher, Martin O\'Connor, Debra Willrett, Andrew Evans

    We are developing a software framework built on caBIG technologies to standardize quantitative imaging assessment of tumor burden and to enable researchers to integrate and analyze a spectrum of quantitative imaging biomarkers to leverage quantitative imaging to better enable assessment of cancer...

  11. Quantitative Imaging for Assessing Breast Cancer Response to Treatment

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

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

    30 Apr 2014 | Teaching Materials | Contributor(s): John Buatti, Thomas Casavant, Michael Graham, Milan Sonka, John Sunderland, Vincent Magnotta, Terry Braun, Bartley Brown, Reinhard Beichel, Carryn Anderson, Wenqing Sun, Yusuf Menda, Brian Smith, Christian Bauer, Markus Van Tol

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

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

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

    30 Apr 2014 | Teaching Materials | Contributor(s): Lanell M Peterson, Brenda F. Kurland, Andrew T Shields, Darrin Byrd, Alena Novakova, Rebecca Christopel, Mark Muzi, David A. Mankoff, Hannah M. Linden, Paul Kinahan

    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

  15. Development and Application of MRI & PET Methods for Predicting Therapeutic Response of Breast Cancers During Neoadjuvant Chemotherapy

    30 Apr 2014 | Teaching Materials | Contributor(s): Xia Li, Lori R. Arlinghaus, Richard G. Abramson, A. Bapsi Chakravarthy, Vandana G. Abramson, Jaime Farley, Hakmook Kang, Jason Williams, Melinda Sanders, Thomas E. Yankeelov

    Overall goal:Provide the breast cancer community with acquisition & analysis tools for: integration of PET and MRI data, and early predictive indices of NAC.