Objective decision support environment for clinical trials

临床试验的客观决策支持环境

• 批准号: 8926887
• 负责人: BRADLEY J. ERICKSON
• 金额: $ 52.29万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-09 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8926887
• 关键词: Algorithms; Biopsy; Brain; Brain Neoplasms; Brain imaging; Cancer Etiology; Cessation of life; Clinical; Clinical Data; Clinical Trials; Collection; Communities; Computers; Data; Databases; Decision Making; Detection; Differentiation Therapy; Early Diagnosis; Environment; Evaluation; Female; Goals; Image; Imaging Device; Imaging technology; Individual; Investigation; Lesion; Libraries; Machine Learning; Magnetic Resonance Imaging; Malignant neoplasm of brain; Malignant neoplasm of lung; Measurement; Methods; Outcome; Outcome Measure; Output; Pathologic; Patients; Primary Brain Neoplasms; Process; Property; Qualifying; Relative (related person); Research; Staging; Survival Rate; Techniques; Technology; Testing; Thoracic Radiography; Visual; Work; base; body system; cancer imaging; clinical decision-making; image processing; improved; innovation; male; quantitative imaging; radiologist; response; tool; tumor; virtual

DESCRIPTION: Brain tumors are the second- and fifth-most common cause of cancer death in males and females under 40, respectively. The 5-year relative survival rate is only 33%, even after decades of research into new treatments. Imaging based on "virtual biopsy" can provide information about the entire lesion in a minimally or non-invasive way. Prior work by our group has demonstrated that image processing methods applied to serial examinations together (as opposed to applying them to individual examinations) can make subtle changes in brain tumors more apparent, allowing earlier detection of progression. We see the union of virtual biopsy methods and change detection methods as an innovative and powerful combination that our team is uniquely qualified to develop and evaluate. In this application, we will implement several feature selection (FS) methods in a tool that is "image friendly". This tool will help select informative features from images; apply a wide range of existing ML algorithms to determine which features are important predictors of therapy response, and to evaluate the impact of a decision support application using these features and ML in a clinical trial. The final stage of th proposal will test the decision support tools in 3 clinical scenarios to see if they are able to significantly improve the decision making of clinicians in a clinical trial. The long-term goal of this proposal is to develop virtual biopsy technology that will enhance the clinical decision making process by providing tools for investigation of image-based therapy response assessment tools, that may also have some ability to predict outcome. We hope to apply the technology to other organ systems and other imaging technologies. We anticipate this project will impact clinical trials by enabling investigation of alternative outcome measures that are objectively assessed using algorithm evaluation methods. Such a toolset should be useful to the entire cancer imaging community to help evaluate features in old and new imaging technologies that correlate with patient survival. As such, this is ideal for helping the Quantitative Imaging Network (QIN) achieve its goals.

描述：脑肿瘤分别是男性和女性40岁以下的癌症死亡的第二和第五最常见原因。即使经过数十年的新疗法研究，相对生存率也只有33％。基于“虚拟活检”的成像可以以最小或无创的方式提供有关整个病变的信息。我们小组的先前工作表明，将图像处理方法一起应用于串行检查（而不是将其应用于单个检查）可以使脑肿瘤的微妙变化更加明显，从而使早期检测进展。我们将虚拟活检方法和更改检测方法的结合视为一种创新和强大的组合，我们的团队具有独特的发展和评估。在此应用程序中，我们将在“图像友好”的工具中实现多种功能选择（FS）方法。该工具将有助于从图像中选择信息性功能；应用广泛的现有ML算法来确定哪些特征是治疗反应的重要预测指标，并在临床试验中评估使用这些功能和ML的决策支持应用的影响。提案的最后阶段将在3种临床方案中测试决策支持工具，以查看他们是否能够在临床试验中显着改善临床医生的决策。 长期目标 该建议是开发虚拟活检技术，该技术将通过提供基于图像的治疗响应评估工具的工具来增强临床决策过程，这些工具也可能具有预测结果的能力。我们希望将技术应用于其他器官系统和其他成像技术。我们预计该项目将通过对使用算法评估方法进行客观评估的替代结果指标进行调查来影响临床试验。这样的工具集应对整个癌症成像社区有用，以帮助评估与患者生存相关的新旧成像技术的特征。因此，这是帮助定量成像网络（QIN）实现其目标的理想选择。