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Stanford University

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

Grant Number: U01-CA190214

As cancer treatments being evaluated in clinical trials evolve from cytotoxic agents to targeted therapies, there is a pressing need to incorporate new imaging biomarkers, such as those being developed by centers in the Quantitative Imaging Network (QIN), into trials in order to detect treatment response with better accuracy than current methods, simple linear measure-based assessments of cancer. Progress has been thwarted, however, by three major challenges: (1) inability of current image assessment tools to compute new imaging biomarkers, due to their closed architectures and lack of support of different programming languages in which biomarker algorithms are developed, (2) lack of decision support tools to assess treatment response in patients or drug effectiveness in clinical trial cohorts using new imaging biomarkers, and (3) lack of approaches to repurpose the vast collections of image data acquired in clinical trials to acquire evidence for qualifying new imaging biomarkers as surrogate endpoints.  In this research, the Stanford group will develop a software platform to enable translating novel quantitative imaging biomarkers being developed by the QIN and others into clinical trials, and methods to enable qualifying them. They will evaluate the success of their platform by deploying new imaging biomarkers in two clinical trials in individual sites and in the ECOG-ACRIN cooperative group. To accomplish these goals: (1) the group will develop a platform and tools through which to deploy new imaging biomarkers into clinical trials, extending their previously developed Web-based image viewing tool and developing four additional unique capabilities: a plugin mechanism to execute new quantitative imaging algorithms developed by them or by others in different programming languages, decision support tools for evaluating patient response and treatment effectiveness, and tools that facilitate the workflow of collecting novel imaging biomarkers in clinical trials, that evaluate benefits over conventional biomarkers, and that collect data which, across clinical trials, will help to qualify them as surrogate endpoints; (2) they will develop methods to repurpose existing imaging data from clinical trials for studying new imaging biomarkers by developing automated image segmentation methods to enable efficient calculation of novel quantitative imaging biomarkers; and (3) deploy and evaluate their platform and tools in two cancer centers and the ECOG-ACRIN national cooperative group, and demonstrate the ability to efficiently collect image biomarker data and to facilitate the qualification of new imaging biomarkers. Through the public availability of their platform, its plugin mechanism for introducing new quantitative imaging biomarkers in clinical trials, the intuitive graphical user interfaces for collecting these biomarkers in the image interpretation workflow, the methods for de-centralized coordination and oversight of image interpretation in clinical trials, and the tools for decision support, their developments will serve the needs of the QIN and the broader research community. If successful, their goal is to accelerate clinical trials and the translation of novel image surrogate biomarkers into clinical practice that will improve the assessment of patient response to new cancer treatments.