Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

癌症治疗反应的影像生物标志物的鉴定和部署

• 批准号: 9927603
• 负责人: Daniel L Rubin
• 金额: $ 59.81万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9927603
• 关键词: Advanced Malignant Neoplasm; Algorithmic Software; Algorithms; American College of Radiology Imaging Network; Antineoplastic Agents; Architecture; Assessment tool; Biological Markers; Cancer Center; Carcinoid Tumor; Clinical; Clinical Data; Clinical Trials; Collaborations; Collection; Communities; Complex; Computer software; Cytotoxic agent; Data; Decision Making; Development; Devices; Dimensions; Eastern Cooperative Oncology Group; Effectiveness; Goals; Health; Human; Image; Image Analysis; Imaging Device; Individual; Intuition; Laboratories; Lesion; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Methods; Oncologist; Online Systems; Outcome; Output; Patients; Pharmaceutical Preparations; Physicians; Plug-in; Prediction of Response to Therapy; Primary carcinoma of the liver cells; Programming Languages; Qualifying; Radiology Specialty; Research; Research Personnel; Resources; Seeds; Site; Surrogate Endpoint; Surrogate Markers; System; Time; Translating; Translations; Treatment Effectiveness; Universities; Work; X-Ray Computed Tomography; anticancer research; base; cancer imaging; cancer therapy; clinical practice; cohort; flexibility; graphical user interface; imaging Segmentation; imaging biomarker; imaging scientist; improved; individual patient; investigator-initiated trial; magnetic resonance imaging biomarker; multidisciplinary; novel; patient response; prospective; public health relevance; quantitative imaging; radiologist; response; success; support tools; survival prediction; targeted agent; targeted treatment; tool; treatment response; tumor; tumor growth

 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.

 描述（由申请人提供）：随着临床试验中评估的癌症治疗从细胞毒性药物发展到靶向治疗，迫切需要将新的成像生物标志物（如定量成像网络（QIN）中的中心开发的生物标志物）纳入这些试验中，以检测治疗反应，其准确性优于当前基于简单线性测量的癌症评估。然而，三大挑战阻碍了进展：（1）当前的图像评估工具不能计算新的成像生物标志物，这是由于它们的封闭架构和缺乏开发生物标志物算法的不同编程语言的支持，（2）缺乏使用新的成像生物标志物评估患者的治疗反应或临床试验队列中的药物有效性的决策支持工具，以及（3）缺乏重新利用在临床试验中获得的大量图像数据集合以获得用于使新的成像生物标志物合格作为替代终点的证据的方法。在这项提案中，我们将开发一个软件平台，以便将QIN和其他机构开发的新型定量成像生物标志物转化为临床试验，并开发使其合格的方法。我们将通过在个别研究中心和ECOG-ACRIN合作组的两项临床试验中部署新的成像生物标志物来评估我们平台的成功。为实现这些目标：（1）我们将开发一个平台和工具，通过该平台和工具将新的成像生物标志物应用于临床试验，扩展我们先前开发的基于Web的图像查看工具，并开发四种独特的功能：一种插件机制，用于执行由我们或其他人以不同编程语言开发的新定量成像算法，用于评估患者反应和治疗有效性的决策支持工具，以及促进在临床试验中收集新的成像生物标志物的工作流程的工具，评估其相对于传统生物标志物的益处，以及收集在临床试验中有助于将其作为替代终点的数据;（二）我们将开发方法，通过开发自动图像分割方法，重新利用临床试验中的现有成像数据，研究新的成像生物标志物，以实现高效计算我们将在两个癌症中心和ECOG-ACRIN国家合作组部署和评估我们的平台和工具，并证明他们有效收集图像生物标志物数据和促进新成像生物标志物鉴定的能力。通过我们平台的公开可用性，其用于在临床试验中引入新的定量成像生物标志物的插件机制，用于在图像解释工作流程中收集这些生物标志物的直观图形用户界面，用于临床试验中图像解释的分散协调和监督的方法，以及用于决策支持的工具，我们的发展将满足QIN和更广泛的研究社区的需求，最终加速临床试验和将新的图像替代生物标志物转化为临床实践，这将改善对新癌症治疗的患者反应的评估。

项目成果

Volumetric Image Registration From Invariant Keypoints.

• DOI: 10.1109/tip.2017.2722689
• 发表时间: 2017-10
• 期刊: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
• 作者: Rister B;Horowitz MA;Rubin DL
• 通讯作者: Rubin DL

Predictive radiogenomics modeling of EGFR mutation status in lung cancer.

EGFR突变状态在肺癌中的预测放射基因组学模型。

• DOI: 10.1038/srep41674
• 发表时间: 2017-01-31
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Gevaert O;Echegaray S;Khuong A;Hoang CD;Shrager JB;Jensen KC;Berry GJ;Guo HH;Lau C;Plevritis SK;Rubin DL;Napel S;Leung AN
• 通讯作者: Leung AN

Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications.

• DOI: 10.1148/radiol.2017161845
• 发表时间: 2018-01
• 期刊: Radiology
• 影响因子: 19.7
• 作者: Zhou M;Leung A;Echegaray S;Gentles A;Shrager JB;Jensen KC;Berry GJ;Plevritis SK;Rubin DL;Napel S;Gevaert O
• 通讯作者: Gevaert O

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.

• DOI: 10.1016/j.ijrobp.2016.03.018
• 发表时间: 2016-08-01
• 期刊: International journal of radiation oncology, biology, physics
• 作者: Wu J;Gensheimer MF;Dong X;Rubin DL;Napel S;Diehn M;Loo BW Jr;Li R
• 通讯作者: Li R

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization.

用于推断概率 US-LI-RADS 分类的可扩展机器学习方法。

• 发表时间: 2018
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Banerjee,Imon;Choi,HailyeH;Desser,Terry;Rubin,DanielL
• 通讯作者: Rubin,DanielL