Quantitative Radiomics System Decoding the Tumor Phenotype

定量放射组学系统解码肿瘤表型

• 批准号: 9247166
• 负责人: Hugo Aerts
• 金额: $ 77.87万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9247166
• 关键词: Address; Algorithms; Bioinformatics; Biological Assay; Biological Markers; Biopsy; Clinic; Clinical; Clinical Data; Collaborations; Collection; Communities; Community Outreach; Complex; Computational Biology; Computer software; Data; Data Set; Descriptor; Development; Development Plans; Disease; Elements; Engineering; Ensure; Environment; Equipment and supply inventories; Event; Exhibits; Future; Gene Expression Profile; Genetic; Genetic Markers; Genomics; Goals; Head and Neck Cancer; Human; Image; Image Analysis; Imaging technology; Infiltration; Institutes; Interdisciplinary Study; Investments; Lung; Malignant Neoplasms; Measures; Medical Imaging; Methods; Modeling; Molecular; Molecular Biology; Monitor; Morphology; Mutation; Nature; Outcome; Patients; Performance; Pharmaceutical Preparations; Phenotype; Physicians; Positioning Attribute; Privatization; Prognostic Marker; Reporting; Research; Research Infrastructure; Research Personnel; Research Training; Resources; Sampling; Scientist; Site; Software Engineering; Software Tools; System; Technology; Testing; The Cancer Genome Atlas; Tissues; Training Support; United States National Institutes of Health; Validation; Work; base; cancer care; cancer imaging; cancer type; clinical decision-making; clinical practice; cohort; community building; computing resources; cost; data resource; experience; flexibility; genomic data; head and neck cancer patient; image archival system; imaging biomarker; imaging modality; neoplastic cell; non-invasive imaging; oncology; outreach; phenotypic biomarker; podcast; portability; prognostic performance; prognostic value; programs; public health relevance; quantitative imaging; radiomics; repository; research and development; response; tool; treatment response; tumor; tumor heterogeneity

 DESCRIPTION (provided by applicant): Advances in genomics have led to the recognition that tumors are characterized by distinct molecular events that drive their development and progression. However, the need for repeated sampling of heterogeneous tumors, together with the relatively high cost of assays, limits their use in monitoring the disease and its response to treatment. New medical imaging technologies and the emerging field of "radiomics" quantifies the tumor phenotype at a macroscopic level, allowing identification predictive phenotypic biomarkers using non- invasive imaging assays that is routinely collected throughout the course of treatment. We recently demonstrated that radiomic biomarkers have strong prognostic performance in large cohorts of lung and head and neck cancer patients, and are associated with underlying mutation and gene-expression patterns. A critical barrier hampering the widespread use of such quantitative features in clinical practice is the lack of robust software tools for the identification of imaging biomarkers and a collection of validated markers that have been shown to work across sites. Part of the reason for the relatively slow progress is that technical developments in quantitative imaging are often isolated; radiomics feature definitions are non-standardized; implementations occur in proprietary environments that make scientific exchange difficult; and analyses are focused on a single disease site or imaging modality. Here we propose to construct a publicly available computational radiomics system for the objective and automated extraction of quantitative imaging features that we believe will yield biomarkers of greater prognostic value compared with routinely extracted descriptors of tumor size. In this proposal, we will outlines research and development plans focused on creating a generalized, open, portable, and extensible radiomics platform that is widely applicable across cancer types and imaging modalities and describe how we will use lung and head and neck cancers as models to validate our developments. To achieve our goals we will identify and implement a large array of quantitative imaging features, develop a flexible radiomics platform usable by both image analysis experts (such as engineering scientists) and imaging non-experts (such as bioinformatics scientists or physicians) alike, and validate these developments by integrating radiomics, genomics, and clinical data to evaluate prognostic performance and examine associations. We will take advantage of The Cancer Imaging Archive (TCIA) with imaging data, and The Cancer Genome Atlas (TCGA), with corresponding genomic and clinical data. Throughout the project all software, tools, and other resources will be made freely available to ensure community building. We have assembled an interdisciplinary team including experts in imaging, computational biology, molecular biology, oncology, and bioinformatics that we believe uniquely positions us to substantially advance the field of radiomics and provide tools that will allow its translational use in the clinic.

 描述（由适用提供）：基因组学的进展导致认识到肿瘤的特征是驱动其发育和进展的不同分子事件。但是，需要重复对异质肿瘤进行采样，以及相对高的测定成本限制了它们在监测疾病中的使用及其对治疗的反应。新的医学成像技术和“放射线学”的新兴领域在宏观水平上量化了肿瘤表型，从而允许使用非侵入性成像测定法常规收集的鉴定预测性表型生物标志物。我们最近证明，放射线生物标志物在大量的肺和头颈癌患者中具有很强的预后性能，并且与潜在的突变和基因表达模式有关。在临床实践中阻碍了这种定量特征的宽度使用的关键障碍是缺乏可靠的软件工具来识别成像生物标志物和一系列已证明可以在跨站点起作用的经过验证的标记。相对缓慢进步的部分原因是定量成像中的技术发展通常是孤立的。放射学特征定义未得到标准化；实施发生在使科学交流困难的专有环境中发生；分析集中在单个疾病部位或成像方式上。在这里，我们建议构建一个公开可用的计算放射系统系统，以实现定量成像特征的客观和自动提取，我们认为，与常规提取的肿瘤大小描述符相比，我们认为这将产生更高的预后价值的生物标志物。在此提案中，我们将概述研究和开发计划的重点是创建一个广义，开放，便携式和可扩展的放射线平台，该平台广泛适用于癌症类型和成像方式，并描述我们如何将肺和头颈癌作为模型来验证我们的发展。为了实现我们的目标，我们将确定并实施一系列定量成像功能，开发一个可通过图像分析专家（例如工程科学家）和非专家（例如生物信息学科学家或医师）来使用的灵活的放射线平台，并通过综述了放射线，模型，模型和临床数据来验证这些发展，并评估这些发展，并评估这些发展。我们将利用具有成像数据的癌症成像存档（TCIA）以及具有相应的基因组和临床数据的癌症基因组图集（TCGA）。通过该项目，将免费提供所有软件，工具和其他资源，以确保社区建设。我们组建了一个跨学科团队，其中包括成像，计算生物学，分子生物学，肿瘤学和生物信息学专家，我们认为我们独特地定位了我们可以实质上推进放射线学领域并提供允许其在临床中翻译的工具。