The National Cancer Institute recently established the Quantitative Imaging Network, a consortium of leading academic research centers in response to a new national program: Quantitative Imaging for Evaluation of Responses to Cancer Therapies. Most of these centers are developing foundational methods for imaging cancer or developing novel imaging biomarkers to image cancer. Our center complements all the other centers in QIN by developing and evaluating methods and tools to improve assessment of tumor burden and cancer treatment response. Our mission is to enable image-based assessment and comparison of a variety of quantitative imaging biomarkers using standars-based methods (in particular, those for image annotation and markup emerging from the caBIG Imaging Workspace). We are initially using images and data from clinical trials in lymphoma and colon cancer; however the results of our work are not limited to these cancers and are intended to be applicable to clinical trials for other cancers.
A statement of the effort or objective: Quantitative imaging methods have tremendous potential to improve the accuracy and efficiency of cancer research, but the lack of software infrastructure that would enable evaluating and adopting these methods in cancer clinical trials hampers progress in developing new treatments. Our objectives are fourfold: (1) to develop software infrastructure that will facilitate the use of quantitative imaging methods in cancer research, (2) to create tools leveraging caBIG technologies that will permit comprehensive and reproducible assessment of the quantitative imaging features of tumor burden, and that will improve the coordination of radiologists and oncologists in collecting quantitative image data, (3) to develop methods to analyze quantitative image metadata that will help oncologists evaluate images collected as part of clinical trials, and (4) to evaluate the utility of our infrastructure and our tools to measure tumor burden quantitatively and reproducibly in two clinical trials, thereby demonstrating the potential of helping researchers to assess response to treatment in patient cohorts and in individual patients.
The clinical problem that our research will address: New promising quantitative imaging methods are being developed and could improve assessment of tumor burden and treatment response; however, validating and using these techniques is hindered by several challenges: (1) lack of tools to record quantitative imaging information about tumor burden reproducibly and efficiently in the clinical workflow; (2) lack of coordination and effective communication between oncologists and radiologists in making quantitative imaging assessments; (3) lack of integration of multiple different quantitative measures of tumor burden that, when taken together, would be more informative than individual indicators, and (4) lack of infrastructure to enable sharing of data derived from quantitative imaging methods among centers and data mining. By tackling these challenges, we will enable cancer researchers to reproducibly measure tumor burden, to better assess the effectiveness of therapies in patient cohorts, potentially leading to shorter clinical trials, and to make the best treatment choices for their cancer patients--a potential benefit to over 1.4 million patients with new cancers annually.
Planned deliverable: Our work will deliver
(1) an infrastructure to harmonize data collection and analysis across different open source and commercial image viewing platforms, and, compatible with this infrastructure
(2) tools to measure lesions making up tumor burden comprehensively and reproducibly
(3) a resource for recording and storing quantitative image data compliant with caBIG standards to enable data sharing
(4) tools for mining quantitative image data, aiding the oncologist in leveraging quantitative imaging metrics to recognize whether a treatment is effective.
As a result, the clinical oncology community will be able to share approaches to validate and standardize imaging biomarkers and quantitative imaging measurements of tumor responses to cancer therapies, which in turn will lead to robust comparison of quantitative methods and to the identification of more accurate approaches for assessing tumor response. Our work will thus accelerate the incorporation of quantitative imaging methods in cancer research, and will provide an essential complement to other centers in the Quantitative Imaging Network that focus on individual quantitative imaging methods.
Motivation: Issues thwarting quantitative imaging in assessing disease and response
The effectiveness of quantitative imaging is limited by variability in radiology imaging equipment hardware, post-processing software, calibration, and lack of standard imaging protocols. These issues will require standardization and validation of hardware and protocols by the centers participating in QIN. But there remain other key challenges arising from the lack of standards-based software tools that hamper the use of quantitative imaging data in cancer research--challenges which our Center will tackle:
(1) reproducibility of measurements on images
(2) coordination and effective communication between oncologists and radiologists in making quantitative imaging assessments
(3) integration of multiple different quantitative measures of tumor burden that, when taken together, will be more informative than individual indicators
(4) lack of tools for recording quantitative image metadata to enable data sharing and data mining
Preliminary work:
(1) Capturing and recording quantitative image data compliant with caBIG standards for collaborative and unambiguous identification and tracking of the tumor burden for oncologists and radiologists (iPad image annotation tool).
(2) Methods for comprehensive and reproducible assessment of tumor burden (RT Image automated segmentation of lesions on PET/CT studies to reduce variation in the measurement of tumor burden).
(3) Warehousing structured, objective image-based assessments acquired in cancer research by a variety of tools (iPad and commercial PACS), and using this resource to enable applications such as decision support tools (Biomedical Image Metadata Manager).
(4) Data visualization and analysis methods to provide decision support based on analysis of multi-dimensional quantitative image data collected in Biomedical Image Metadata Manager (BIMM).
