Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

点击翻译按钮获取中文摘要

 DESCRIPTION (provided by applicant): 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 these trials in order to detect treatment response with better accuracy than current, 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 proposal, we 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. We will evaluate the success of our 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) We will develop a platform and tools through which to deploy new imaging biomarkers into clinical trials, extending our previously developed Web-based image viewing tool and developing four unique capabilities: a plugin mechanism to execute new quantitative imaging algorithms developed by us 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 their benefit over conventional biomarkers, and that collect data which, across clinical trials, will help to qualify them as surrogate endpoints; (2) We 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) We will deploy and evaluate our platform and tools in two cancer centers and the ECOG-ACRIN national cooperative group, and demonstrate their ability to efficiently collect image biomarker data and to facilitate the qualification of new imaging biomarkers. Through the public availability of our 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, our developments will serve he needs of the QIN and the broader research community, ultimately accelerating clinical trials and the translation of novel image surrogate biomarkers into clinical practice, which will improve the assessment of patient response to new cancer treatments.