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Programs & Resources

Quantitative Imaging for Evaluation of Responses to Cancer Therapies

Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) (PAR-11-150) Request for Applications

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.

This network is one of several being conducted within the Cancer Imaging Program. The main emphasis is on the support of the development and adaptation/implementation of quantitative imaging endpoints (including imaging methods and related software tools research, and/or informatics infrastructure, as needed).  Any related clinical trials are not supported under this program.

To date, seventeen centers of imaging excellence have been selected through the NIH peer review process and more will be added as they pass through peer review.  Four working groups, addressing common issues to the various programs, including data collection, image analysis, informatics, and clinical trial design have been established.  These working groups are composed of members of the different QIN teams, though not necessarily the principal investigators. Various program staff members from the NCI join with the principal investigators of the teams to form the Executive Committee.  This committee has oversight of the network through monthly phone calls. The Network Executive Committee meets monthly via teleconference and organizes network-wide activities such as consensus publications, cross-network activities, associate membership in the network, and semi-annual face-to-face meetings.  Two annual reports have been created by the QIN teams, and are available upon request.

Agendas from the following QIN Steering Committee Face-to-Face Annual Meeting are available here:

The organization of the QIN is more than just an assembly of individual research programs.  Click here to read about the QIN Organization.  In addition to the Steering Committee, the currently funded centers are linked by five working groups.  These are functions identified by the centers as being common to each center.  By pooling resources in these areas, the centers can leverage their efforts and prevent “siloing”, a common problem in many multi-site initiatives. Click here to read about the five Working Groups.

QIN Newsletter No 1

QIN Newsletter No 2

QIN Newsletter No 3

QIN Newsletter No 4

QIN Newsletter No 5

QIN Newsletter No 6

QIN Newsletter No 7

QIN Funded Centers

The University of lowa
John M. Buatti (
Quantitative Imaging to Assess Response in Cancer Therapy Trials

University of Pittsburgh
James M. Mountz (
Quantitative Biomarker Imaging for Early Therapy Response Assessment in Cancer

Stanford University
Daniel Rubin (
Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

Stanford University (2)
Sandy Napel (
Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

Vanderbilt University
Thomas E. Yankeelov (
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy

The H. Lee Moffitt Cancer Center & Research Institute
Robert Gillies (
Radiomics of NSCLC 

University of Washington
Paul Kinahan  (
Advanced PET/CT Imaging for Improving Clinical Trials

Brigham and Women's Hospital, Harvard University
Fennessy, Fiona   (
Quantitative MRI of Prostate Cancer as a Biomarker and Guide for Treatment

Massachusetts General Hospital
Bruce Rosen (
Quantitative MRI of Glioblastoma Response

Columbia University
Lawrence H. Schwartz ( )
Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model

Oregon Health and Science University
Wei Huang (
Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy

Johns Hopkins University
Richard Wahl (
Multi-Modality Quantitative Imaging for Evaluation of Response to Cancer Therapy

University of California at San Francisco
Nola Hylton (
Quantitative Imaging for Assessing Breast Cancer Response to Treatment

Memorial Sloan Kettering Cancer Center
Sadek Nehmeh (
Prognostic Value of Tumor Hypoxia as Measured by 18F-MISO Breath-Hold PET/CT

University of Michigan
Brian Ross (
Advancing Quantification of Diffusion MRI for Oncologic Imaging

University of Michigan 2
Lubomir Hadjiyski
Biomarkers for Staging and Treatment Response Monitoring of Bladder Cancer

University of Michigan 3
Yue Cao
Quantitative MRI Models of Head & Neck Cancers for Physiological Adaption of RT

Mayo Clinic
Bradley Erickson (
Objective Decision Support Environment for Clinical Trials

Mount Sinai
Bachir Taouli (
Evaluation of HCC Response to Systemic Therapy with Quantitative MRI

Emory University
Hyunsuk Shim (
Quantitative MRSI to Predict Early Response to SAHA Therapy in GBM Management

Medical College of Wisconsin
Kathleen Schmainda
Quantitative (Perfusion & Diffusion) MRI Biomarkers to Measure Glioma Response

University of California - Los Angeles
Michael McNitt-Gray
Quantitative CT Imaging for Response Assessment When Using Dose Reduction Methods

University of Chicago
Maryellen Giger (
Quantitative Image Analysis for Assessing Response to Breast Cancer Therapy

Mitchell D. Schnall (
ECOG-ACRIN-Based QIN Resource for Advancing Quantitative Cancer Imaging in Clinical Trials

University Health Network
David Jaffray (
Image-based Quantitative Assessment of tumor Hypoxia 

University of British Columbia
Francois Benard (
Integrating Quantitative Imaging Methods and Genomic Biomarkers to Assess the Therapeutic Response to Cancers

Dana-Farber Cancer Institute
Hugo Aerts (
Genotype and Imaging Phenotype Biomarkers in Lung Cancer

Washington University
Fred Prior (
Resources for Development and Validation of Radiomic Analysis & Adaptive Therapy