Development of a tool to extract quantitative image features and predict outcome

开发提取定量图像特征并预测结果的工具

• 批准号: 8568919
• 负责人: Laurence E Court
• 金额: $ 8万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8568919
• 关键词: Algorithms; Area Under Curve; Cancer Patient; Caregivers; Clinical; Clinical Trials; Data; Data Set; Decision Making; Dependence; Descriptor; Development; Dose; Family; Funding; Genetic; Genetic Polymorphism; Goals; HSPB1 gene; Human; Image; Individual; Investigation; Malignant Neoplasms; Malignant neoplasm of esophagus; Medicine; Modeling; Non-Small-Cell Lung Carcinoma; Outcome; Patients; Protocols documentation; Publishing; Radiation therapy; Radiotherapy Research; Receiver Operating Characteristics; Reporting; Research; Research Personnel; Risk; Shipping; Ships; Software Tools; Source; Staging; Techniques; Therapeutic Studies; Tomography, Computed, Scanners; Treatment outcome; Uncertainty; Work; X-Ray Computed Tomography; base; clinically significant; image processing; mortality; open source; public health relevance; tool; treatment trial; tumor

DESCRIPTION (provided by applicant): Project Summary It is well known that clinical parameters such as clinical stage are correlated with survival outcomes among cancer patients. However, there is much variability among patients, and we are unable to accurately or reliably predict survival for individual patients - a necessary step for personalized cancer medicine. There is a strong need for accurate, reliable outcome predictions for patients, caregivers, and clinical staff. Even the addition of genetic data has yet to make a clinically significant increasein the reliability of our outcome prediction for individual patients. Recent research, including our own, has shown that image features extracted from pre-treatment CT images can be used to predict treatment outcomes for non-small cell lung cancer patients, esophageal cancer patients, and others. A limitation to current studies is the lack of a common platform that would enable research to share results and quickly and easily apply techniques to their own patient datasets. Our proposed project will create open-source software tools that will integrate with current open-source tools that are available for radiation therapy research. We will also carry out an in-depth investigation into the various sources of uncertainty involved in calculating image features, allowing researchers to avoid using features that have high dependence on imaging parameters (such as pixel size). Nearly 100% of NCI- funded clinical trials include pre-treatment CT imaging. Our preliminary work will provide the tools to allow researchers involved in these studies to investigate the use of quantitative image features for predicting treatment outcome.

项目简介：项目摘要众所周知，临床分期等临床参数与癌症患者的生存结果相关。然而，患者之间存在很大的变异性，我们无法准确或可靠地预测单个患者的生存--这是个性化癌症药物的必要步骤。对于患者、护理人员和临床工作人员来说，迫切需要准确、可靠的结果预测。即使是遗传数据的添加也没有在临床上显着增加我们对个别患者结果预测的可靠性。最近的研究，包括我们自己的研究表明，从治疗前的CT图像中提取的图像特征可以用于预测非小细胞肺癌患者、食道癌患者和其他患者的治疗结果。目前研究的一个限制是缺乏一个通用平台，使研究能够共享结果，并快速轻松地将技术应用于自己的患者数据集。我们提议的项目将创建开源软件工具，这些工具将与目前可用于放射治疗研究的开源工具相结合。我们还将对计算图像特征时涉及的各种不确定性来源进行深入调查，使研究人员能够避免使用对成像参数(如像素大小)有高度依赖的特征。NCI资助的几乎100%的临床试验包括治疗前的CT成像。我们的初步工作将提供工具，使参与这些研究的研究人员能够研究使用定量图像特征来预测治疗结果。