Grants

Quantitative Imaging Network (QIN)

QIN MISSION

The mission of the Quantitative Imaging Network (QIN) is to improve the role of quantitative imaging

for clinical decision making in oncology by the development and validation of data acquisition,

analysis methods, and tools to tailor treatment to individual patients and to predict or monitor the

response to drug or radiation therapy.

Membership to QIN• Submission of PAR-11-150 grant application• Review of grant application by NCI

• scoring

• If suitable score • presentation of grant• membership to QIN• Funding of proposed project

• QIN Members then become multidisciplinary teams in a network• to share retrospective and prospective images and meta-data, including

outcome data, from targeted therapy trials as a public resource• to use this resource to build consensus on validation and standardization

methods. • To fund teams to develop and validate innovative methods for data

collection and analysis as well as develop resources and consensus methods for quantitative imaging.

Specific Goals and Requirements for PAR-11-150• Projects must be designed to develop state-of-the-art quantitative

imaging methods for monitoring therapeutic responses to drugs, radiation therapy, or image guided interventions

• Projects may span the development in the research settings, translation, and validation as needed, and incorporation of these solutions as endpoints into imaging protocols for current and planned Phase 1 and 2 trials in the clinical setting. • The validation of imaging methods for the evaluation of therapeutic

responses should be included. • All of the proposed projects must include testing new or emerging imaging

protocols and quantitative imaging methods in early phase trials (funded by other sources). All these solution/tools must be suitable for use as clinical decision software tools.

• Research can incorporate quantitative imaging protocols and methods for several imaging modalities, including anatomical, functional and molecular imaging.

Research Areas for PAR-11-150 - 1• The identification of drug, radiation therapy,

and/or intervention guided intervention(IGI) trials that would benefit from quantitative imaging methods and improve prognostic outcome, including the development and optimization of clinical trial protocols, specifically to implement quantitative imaging methods

Research Areas for PAR-11-150 - 2• The development of quality assurance methods to

test and characterize time related changes in imaging systems and IGI platforms during the course of therapy

Research Areas for PAR-11-150 - 3• The development of algorithms, modeling and

image simulation methods, and related databases to validate clinical decision software tools with the goal of improving the ability to measure the response of targeted tumors to therapy quantitatively. • tools should be developed for a range of imaging methods

and validated against existing public web accessible databases. • the intent is to explore consensus methods for validating

clinical decision tools.

Research Areas for PAR-11-150 - 4• The development of software architecture,

designed to allow interoperability of software tools that may include open source approaches. The long-term goal should ideally include harmonizing data collection, analysis, and image display across different commercial imaging, therapy, and IGI platforms. Equivalent methods such as proprietary methods and solutions supported by industry are appropriate. All the methods proposed should ideally meet emerging NCI caBIG requirements and/or DICOM 23 requirements, if and when available.

Appropriate research goals to be accomplished by the end of year 5To meet the goals of this FOA, each applicant team is expected to engage oncologists in the evaluation process to accept quantitative imaging for clinical decision making in clinical trials for appropriately targeted therapies. Examples of appropriate research goals to be accomplished by the end of year 5 of the projects include (but are not limited to) the following:• Completion of quantitative imaging studies incorporated into two or more

Phase 1 and 2 clinical trials. • address the important aspects of patient accrual and data analyses• Validate with patient outcome if possible within 5 years or later

• Improved consensus and rationalization for employing and optimizing quantitative, multi-modal, and molecular imaging methods for therapies where they are clinically useful

• Public registries and image database resources to support clinical decision making for therapies by the broader oncology community

• Replacement of observer or qualitative estimates of therapy response, such as the use of the RECIST criteria.

• Compatibility with the NCI caBIG informatics initiative for validation methods and software tools

PAR-11-150 will not support• The development of any tumor imaging hardware

components or imaging systems; and • The use of prototype imaging

platforms/instruments for data collection. • Imaging Phase 1 clinical trials• Investigators seeking support for imaging clinical trials, as

such, are referred to the following initiatives: • NCI Imaging Program Phase 1 Trials (

http://imaging.cancer.gov/clinicaltrials/); and • NCI Radiation Research Program Trials (

http://www3.cancer.gov/rrp/ct_web.html.

Important for Writing Grants for QINGrants may be written to address one of the 4 QIN areas but are not limited to these 4 areas.However, the grant application must clearly address all of the following:• Specific Goals and Requirements for PAR-11-150• Appropriate research goals to be accomplished by

the end of year 5• Data sharing – how data will be shared• Reviewers will comment on whether the following Resource

Sharing Plans, or the rationale for not sharing the following types of resources, are reasonable: 1) Data Sharing Plan; 2) Sharing Model Organisms; and 3) Genome Wide Association Studies (GWAS).

The main goals of current QIN projects• The main goals of QIN projects comprise of elements

related to development, application and validation of new techniques for quantifying radiological images, correlating these new parameters with biomarkers, surrogate endpoints and outcome data to design new cancer clinical trials using quantitative imaging with clinical decision software tools.

• The QIN projects have focused on clinical trials for breast, brain, lung, head and neck, liver, prostate cancers as well as sarcomas but are not limited to these cancers.

• The radiological imaging methods used for diagnosis and evaluation of response to treatment QIN proposals are PET, CT, MRI and SPECT.

Developmental• Develop optimal image analysis/registration methods

appropriate for quantitative imaging in clinical trials - analytical algorithm development, advances in quality control and software implementation to increase the effectiveness and reliability of quantitative imaging methods

• Characterize the accuracy, precision, reproducibility, variability and bias of the individual quantitative imaging methods and software algorithms developed to analyze images

• Establish robust quality assurance procedures and standards to enhance the quality and reliability of quantitative imaging for clinical decision-making.

• Use caBIG infrastructure for creating an open source software framework for quantitative image analysis tool development, testing and standardization for assessment of tumor burden

Application and Demonstration of Feasibility• Develop guidelines for incorporating quantitative imaging as a biomarker and

measure of response in cancer clinical trial design• Develop and implement methods for assessing and comparing pre- and post-

treatment data to optimize and clinically validate quantitative image analysis tools and increase the effectiveness and reliability of these new approaches for clinical decision making

• Develop methods to combine the metrics to predict or assess treatment response per patient, per tumor and intra-tumor combining data with other imaging studies, gene expression or biomarker studies in a clinically useful manner

• Explore the correlation between the new quantitative image analysis tools (markers) with biochemical/molecular/genetic biomarkers and the added value of the combination of markers in the prediction of response to therapy, disease free survival, survival and other clinically important endpoints of clinical trials

• Adapt, enhance and extend quantitative image-based response assessment for clinical trials decision-support

• The use of validation methods and software tools in response to this FOA must address the issue of compatibility with the NCI caBIG informatics initiative

Utilization in Clinical Trials• Optimize clinical trial design for cancer therapy

studies using quantitative PET/CT/SPECT/MRI imaging methods developed

• Conduct prospective clinical trials that customize chemotherapy based on the cellular, molecular and/or metabolic characteristics of tumors in individual research participants

• Conduct additional biomarkers explorations and optimizations of quantitative images and correlations with surrogate endpoints and outcomes

• Apply these tools to multiple clinical trials – prospectively and retrospectively.

MRI• EMORY• University of Michigan• UCSF• Oregon Health and Science University• Massachusetts General Hospital• Brigham & Women's Hospital, Harvard University

PET/CT/MRI• MOFFITT• The University of Iowa• Johns Hopkins University• University of Pittsburgh• Vanderbilt University

PET• Memorial Sloan Kettering Cancer Center• Columbia University• The University of Iowa• University of Washington

CANCERs addressed by current QIN projects• Lung

• Johns Hopkins University• Moffitt• Memorial Sloan Kettering Cancer Center

• Head and Neck• The University of Iowa• University of Pittsburgh

• Brain• Johns Hopkins University• University of Pittsburgh• Mayo Clinic• Mount Sinai• Emory• Massachusetts General Hospital

• Breast• Johns Hopkins University• Vanderbilt University• University of Michigan• UCSF• Oregon Health and Science University

• Prostate• Oregon Health and Science University

• Sarcoma• Columbia University

• Oregon Health and Science University

• HCC• Columbia University

NOT-CA-13-011• http://grants.nih.gov/grants/guide/notice-files/NOT-CA-13-011.html• The purpose of this Notice is to clarify specific requirements and

aspects of the scientific scope of Funding Opportunity Announcement (FOA) PAR-11-150, entitled “Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01)”.

• The specific purpose of this Notice is to add an opportunity for applications to be focused only on supporting QIN network-wide research resources

• Applicants who propose such applications should have prior research experience and capability in prediction and/or evaluation of responses to cancer therapy. They should show how the proposed research resources will support comparisons of the performance of related tools and methods for an array of imaging and/or molecular commercially supported imaging modalities, and/or their adoption in clinical trials.

NOT-CA-13-011: applications may be focused on one or more of the following research resources

• Development and maintenance of an informatics data sharing infrastructure that should include de-identification, quality control, user specific access control, helpdesk/support services, and hosting services. • Detailed standard operating procedures (SOPs), with appropriately assigned

resources to leverage the proposed infrastructure in the context of QIN goals should be provided.

• Utilization of the DICOM Standard on de-identification to ensure retention of all necessary metadata while adhering to all Health Insurance Portability and Accountability Act (HIPAA) and Health Information Technology for Economic and Clinical Health (HITECH) regulations.

• Implementation of QIN-adopted methods for data submission and archival, including Clinical Trials Processor (CTP) software for data collection and National Biomedical Imaging Archive (NBIA) software for hosting the image data. 

• Provision of web-based infrastructures to host all related software and statistical tools for clinical decision support and to permit these tools to be evaluated against selected data on the proposed imaging archive, including the use of cloud computing.

NOT-CA-13-011• All tools and methodology must be open source and

open access such that they can be implemented by other members of QIN and the scientific community.

• It is anticipated that if several such applications are funded, a federated data archive and tool sharing resource will implemented as a Trans-QIN resource

• All other aspects of the FOA (PAR-11-150) remain unchanged • Specific Goals and Requirements for PAR-11-150• Appropriate research goals to be accomplished by the end of

year 5• Data sharing – how data will be shared