Quantitative Radiomics System Decoding the Tumor Phenotype

定量放射组学系统解码肿瘤表型

• 批准号: 8875289
• 负责人: Hugo Aerts
• 金额: $ 71.51万
• 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/8875289
• 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 Markers; Genetic Programming; Genomics; Goals; Head and Neck Cancer; Heterogeneity; Human; Image; Image Analysis; Imaging technology; Infiltration; Institutes; Interdisciplinary Study; Investments; Lung; Malignant Neoplasms; Measures; Medical Imaging; Methods; Modeling; Molecular; Molecular Biology; Monitor; Mutation; Nature; Outcome; Patients; Performance; Pharmaceutical Preparations; Phenotype; Physicians; Positioning Attribute; Prognostic Marker; Reporting; Research; Research Infrastructure; Research Personnel; Research Training; Resources; Sampling; Scientist; Site; Slice; Software Engineering; Software Tools; System; Technology; Testing; The Cancer Genome Atlas; Tissues; Training Support; Validation; Work; base; cancer care; cancer imaging; cancer type; clinical decision-making; clinical practice; cohort; computing resources; cost; experience; flexibility; head and neck cancer patient; image archival system; imaging biomarker; imaging modality; neoplastic cell; non-invasive imaging; oncology; outreach; prognostic; prognostic value; public health relevance; quantitative imaging; repository; research and development; response; tool; tumor

 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（由申请人提供）：基因组学的进展已使人们认识到，肿瘤的特征在于驱动其发展和进展的不同分子事件。然而，需要对异质性肿瘤进行重复取样，以及相对较高的测定成本，限制了它们在监测疾病及其对治疗的反应中的应用。新的医学成像技术和新兴的“放射组学”领域在宏观水平上量化肿瘤表型，允许使用在整个治疗过程中常规收集的非侵入性成像测定来鉴定预测性表型生物标志物。我们最近证明，放射组学生物标志物在肺癌和头颈癌患者的大队列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相关。阻碍这种定量特征在临床实践中广泛使用的关键障碍是缺乏用于识别成像生物标志物的强大软件工具和已显示跨站点工作的经验证的标志物的集合。进展相对缓慢的部分原因是，定量成像的技术发展往往是孤立的;放射组学特征定义是非标准化的;实施发生在专有环境中，使科学交流变得困难;分析集中在单一的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组学系统的客观和自动化提取的定量成像功能，我们相信将产生更大的预后价值的生物标志物相比，常规提取的肿瘤大小的描述符。在本提案中，我们将概述研究和开发计划，重点是创建一个广泛适用于癌症类型和成像方式的通用、开放、便携和可扩展的放射组学平台，并描述我们将如何使用肺癌和头颈癌作为模型来验证我们的开发。为了实现我们的目标，我们将确定和实施大量的定量成像功能，开发一个灵活的放射组学平台，可供图像分析专家（如工程科学家）和成像非专家（如生物信息学科学家或医生）使用，并通过整合放射组学，基因组学和临床数据来验证这些发展，以评估预后性能并检查相关性。我们将利用癌症成像档案（TCIA）的成像数据，以及癌症基因组图谱（TCGA），以及相应的基因组和临床数据。在整个项目中，所有软件、工具和其他资源都将免费提供，以确保社区建设。我们已经组建了一个跨学科团队，包括成像，计算生物学，分子生物学，肿瘤学和生物信息学方面的专家，我们相信这将使我们能够大大推进放射组学领域，并提供将其转化为临床应用的工具。