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Programs & Resources

Stanford University

Computerized Quantitative Imaging Assessment of Tumor Burden.
Daniel L. Rubin, Ph.D., Principal Investigator
dlrubin@stanford.edu
Stanford University

Grant Number: U01-CA142555

Stanford University has recruited a multidisciplinary team of scientist to address promising quantitative imaging methods to improve the ability of cancer researchers to evaluate tumor burden and treatment response through the support of the Quantitative Imaging Network. To date, progress is thwarted by the lack of software infrastructure to record quantitative imaging information efficiently and reproducibly in the routine clinical workflow, and by the inability to store and share image metadata in standard formats. Many different quantitative imaging features that could more completely describe tumor burden are not being captured because collecting this information is laborious without tool support. The objective of this program is to develop software infrastructure that meets these needs of cancer researchers through three aims: (1) creating tools leveraging caBIG technologies to standardize quantitative imaging assessment of tumor burden. These tools will enable comprehensive and reproducible assessment of the quantitative imaging features of tumor burden as part of the routine clinical workflow and will improve the coordination of radiologists and oncologists in collecting quantitative image data. Through a commercial partnership, the investigators incorporate features of their tools in a commercial image interpretation workstation to introduce our methods into clinical practice; (2) developing methods to analyze quantitative image metadata and to help oncologists evaluate quantitative criteria on images collected as part of clinical trials; and (3) evaluating the utility of their infrastructure by applying their tools in two clinical trials and demonstrating the ability of their software infrastructure to quantitatively and more reproducibly measure tumor burden, helping researchers to assess the response to treatment in individual patients and patient cohorts. Their infrastructure will provide new ways of looking at quantitative imaging information related to treatment response along multiple dimensions so that researchers can recognize the effectiveness of treatments in clinical trials better and potentially sooner than using current unassisted approaches. Their work will accelerate quantitative imaging in cancer research, and will provide an essential complement to other centers in the Quantitative Imaging Network that focus on individual quantitative imaging methods.

The methods and tools to be developed will improve the ability of cancer researchers to collect and use quantitative imaging data to accurately assess tumor burden and to develop improved methods for evaluating whether treatment is effective. Improving the accuracy of quantitative imaging in assessing treatment response in individual patients will enable better treatment choices and improve human health.

To accomplish the goals this project, the investigators have proposed the following aims:
Specific Aim 1 {Framework): To create an open source software framework built on caBIG technologies to standardize quantitative imaging assessment of tumor burden.
Aim 2 {Analysis): To develop image annotation analysis methods that process image metadata to summarize changes in tumor burden over time and across cohorts.
Aim 3 {Deployment and Evaluation): To apply their tools in two on-going clinical trials that evaluate new treatments for follicular lymphoma and metastatic colon cancer, respectively.