Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

 描述(申请人提供)：随着临床试验中被评估的癌症治疗从细胞毒性药物演变为靶向治疗，迫切需要将新的成像生物标记物，如由定量成像网络(QIN)的中心正在开发的那些，纳入这些试验，以便比目前基于简单线性测量的癌症评估更准确地检测治疗反应。然而，进展受到三个主要挑战的阻碍：(1)由于目前的图像评估工具无法计算新的成像生物标记物，由于其封闭的体系结构以及缺乏对开发生物标记物算法的不同编程语言的支持，(2)缺乏决策支持工具来评估患者的治疗反应或使用新的成像生物标记物的临床试验队列中的药物有效性，以及(3)缺乏方法来重新利用在临床试验中获得的大量图像数据来获取证据以将新的成像生物标记物作为替代终点。在这项提案中，我们将开发一个软件平台，使秦等人正在开发的新的定量成像生物标记物能够转化为临床试验，以及能够对其进行资格鉴定的方法。我们将通过在个别地点和ECOG-ACRIN合作小组的两个临床试验中部署新的成像生物标记物来评估我们平台的成功。为了实现这些目标：(1)我们将开发一个平台和工具，通过它将新的成像生物标记物部署到临床试验中，扩展我们以前开发的基于Web的图像查看工具，并开发四个独特的功能：一个用于执行我们或其他人用不同编程语言开发的新的定量成像算法的插件机制，用于评估患者反应和治疗有效性的决策支持工具，以及促进临床试验中收集新的成像生物标记物的工作流程的工具，这些工具评估它们相对于传统生物标记物的益处，并收集数据，这些数据将有助于在临床试验中将它们作为替代终点；(2)我们将开发用于研究新的成像生物标记物的临床试验中的现有成像数据的用途，方法是开发自动图像分割方法，以高效计算新的定量成像生物标记物；以及(3)我们将在两个癌症中心和ECOG-ACRIN国家合作小组部署和评估我们的平台和工具，并展示它们高效收集图像生物标记物数据的能力，并促进新的成像生物标记物的资格鉴定。通过我们的平台的公开可用性，其在临床试验中引入新的定量成像生物标记物的插件机制，在图像解释工作流程中收集这些生物标记物的直观图形用户界面，临床试验中图像解释的分散协调和监督方法，以及决策支持工具，我们的开发将服务于秦氏研究院和更广泛的研究社区的需求，最终加快临床试验和将新型图像替代生物标记物转化为临床实践，这将改善对患者对新癌症治疗的反应的评估。

 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.