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Last Updated: 10/12/21
Quantitative Imaging Network (QIN)

About the Quantitative Imaging Network (QIN)


Historically research teams have entered the Quantitative Imaging Network through the NIH peer review process in which a research application was submitted in response to a specific past retired program announcement such as PAR-18-248; PAR-18-249; or PAR-18-909. The current active QIN announcement is NOT-CA-21-032 and is reviewed in a special Academic Industrial Partnership study section (See PAR-20-155). Meritorious applications are considered for funding, and are given support if funds are available. Once in the network, each team is responsible for making steady progress on the research topic proposed in its application. In addition, team members are obligated to serve on network-wide activities such as the Executive (Steering) Committee and/or one or more of the current working groups. These activities are presented in the Network structure section.

Greater than forty research teams have participated in the QIN thus far since its inception in 2008. Because some teams have completed their research tenure with the network, the present number of active participants is now 16 supported by NCI. Each of these teams are listed below with links to their research abstracts and contact information.

QIN Members

Barrow Neurological Institute
Chad Quarles (
Multi-parametric Perfusion MRI for Therapy Response Assessment in Brain Cancer

Children’s Hospital of Philadelphia
Children’s National Hospital (Washington, D.C.)
Marius Linguraru (
MRI for Pediatric Optic Pathway Glioma Treatment Response

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

Johns Hopkins University and Washington University in Saint Louis
Michael A. Jacobs, Ph.D., (
Lilja Solnes (
Multi-Modality Quantitative Imaging for Evaluation of Response to Cancer Therapy

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

Memorial Sloan Kettering Cancer Center
Amita Dave (
Columbia University
Lawrence H. Schwartz (
Quantitative imaging tools to derive DW-MRI oncological biomarkers

Moffitt Cancer Center & Research Institute
Robert Gillies (
Matthew Schabath (
Radiomics of NSCLC

Weill Medical College of Cornell University
Gene Kim (
Diffusion MRI of Treatment Response for De-escalation of Radiation Therapy

New York University School of Medicine
Eric Sigmund (
Memorial Sloan Kettering Cancer Center
Sunitha Thakur (
Breast Cancer Intravoxel-incoherent-motion MRI Multisite (BRIMM) Study

Oregon Health & Science University
Wei Huang (
University of Iowa
James Holmes (
University of Washington
Savannah Partridge (
Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy

Thomas Jefferson University
Yevgeniy (Jenia) Vinogradskiy (
Emory University
Richard Castillo (
Beaumont Health System
Edward Castillo (
Quantitative Lung Function Imaging to Reduce Toxicity for patients treated with Radiation and Immunotherapy

University of Alabama at Birmingham
Harrison Kim (
Disposable Perfusion Phantom for Accurate DCE-MRI Measurement of Pancreatic Cancer Therapy Response

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

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

University of Texas at Austin
Thomas E. Yankeelov (
University of Chicago
Gregory Karczmar (
University of Chicago
Rita Nanda (
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy

University of Texas SW Medical Center
Ananth Madhuranthakam (
Joseph Maldjian (
Ivan Pedrosa (
Quantitative Non-Contrast Perfusion using Arterial Spin Labeling for Assessment of Cancer Therapy Response

QIN Emeritus Members

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

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

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

Stanford University
Sandy Napel (
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers

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

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

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

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

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

Brigham & Women’s Hospital
Fiona Fennessy, MB, PhD (
Quantitative MRI of prostate cancer as a biomarker and guide for treatment

Emory University
Hyunsuk Shim (
Quantitative Magnetic Resonance Spectroscopic Imaging to Predict Early Response to SAHA Therapy in GBM Management

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

Massachusetts General Hospital
Jayashree Kalpathy-Cramer (
Quantitative MRI of Glioblastoma Response

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

University of Arkansas Medical Sciences
Fred Prior (
Resources for Development and Validation of Radiomic Analysis & Adaptive Therapy

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

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

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

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

Vanderbilt University
Richard Abramson (
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy

Associate Members

Along with the funded members, the QIN also has associate members from around the globe. Associate members are research groups or consortia interested in or working on exploring clinical utility of quantitative imaging tools and methods in predicting or measuring response to cancer therapy. Although associate membership does not involve NIH financial support, it provides collaborative opportunities with other QIN members, enabling participation in QIN tool challenges, working group interactive discussions and the opportunity to attend the annual face to face QIN meeting. Over the past few years, associate members have presented their work at the annual meetings and have ongoing collaborative projects with members of the QIN and with other associate members as well. The QIN continues to grow through the additions of new funded members as well as through new associate members. Associate membership to QIN is sought through written request to the QIN Executive Committee through the Program Director of the QIN. The Associate membership submission includes a summary of the funded quantitative imaging focused research project, and a biosketch of each participant from the petitioning group or investigator. Associate membership petitions are reviewed and voted on by the Executive Committee during the monthly Executive Committee teleconferences. Associate members are expected to participate by having representation in various working groups teleconference and if possible to attend QIN annual meetings.

Current Associate Members in QIN

Joel Saltz:
Stony Brook University, Stony Brook, NY, USA

Ron Summers:
NIH Clinical Center, Bethesda, MD, USA

Gordan Harris:
Massachusetts General Hospital, Boston, MA, USA

Anant Madabhushi:
Case Western Reserve University, Cleveland, Ohio, USA

Christos Davatzikos:
University of Pennsylvania, Philadelphia, PA, USA
Clement Adebayo Adebamowo:
African Collaborative Center for Microbiome and Genomics Research (ACCME)
Nigeria; Institute of Human Virology, University of Maryland School of Medicine (UMSOM)

Veselka Stoynova:
Cancer Center (Oncology Clinic), City Clinic, Sofia, Bulgaria

Kim Mouridsen: KIM@CFIN.AU.DK
Center of Functionally Integrative Neuroscience
Aarhus University, Aarhus University Hospital, Denmark

Winfried Brenner:
Charité — University Medicine Berlin, Berlin, Germany
Lachezar D. Penev:
City Clinic Sofia, Bulgaria

Omolara Fatiregun:
Lagos State University Teaching Hospital, Ikeja, Lagos State, Nigeria

Folakemi T. Odedina:
Prostate Cancer Transatlantic Consortium (CaPTC)
University of Florida, Gainsville, FL, USA

Rolf Heckemann:
Sahlgrenska University Hospital, Univ. of Gothenburg, Sweden

Abhishek Mahajan:
Tata Memorial Hospital, Mumbai, India

Meenakshi Thakur:
Tata Memorial Cancer Center, Mumbai, India

Galina Kirova Nedyalkova:
Tokuda Hospital, City Clinic, Sofia, Bulgaria

Finbarr O'Sullivan:
University College Cork, Cork, Ireland

Kyung Soo Lee:
University School of Medicine, South Korea

James Mountz:
University of Pittsburg, Pittsburg, PA

Wei Lu, Ph.D:
Memorial Sloan Kettering Cancer Center, New York, NY

Chi Liu, Ph.D.:
Yale University, New Haven, Connecticut

Vahagn Nazaryan, Ph.D.:
Hampton University Proton Therapy Institute, Hampton, VA, USA

Kathryn Keenan:
National Institute of Standards and Technology, Boulder, CO, USA

Stephen E Russek:
National Institute of Standards and Technology, Boulder, CO, USA

Professor Harish Poptani:
University of Liverpool, Liverpool, UK

Andrew Schache:
University of Liverpool, Liverpool, UK

Prof. Habib Zaidi, Ph.D, FIEEE:
Geneva University, Geneva, Switzerland

Wendy Diana Bautista Guzmán M.D. PhD.:
CCR-NCI, Intramural Program, Bethesda, MD, USA