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（由申请人提供）：基因组学的进步已经导致人们认识到肿瘤的特征是驱动其发展和进展的不同分子事件，然而，需要对异质肿瘤进行重复采样，以及相对较高的检测成本。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，从而允许使用非侵入性成像测定来识别预测表型生物标志物。我们最近证明，放射组学生物标志物在大量肺癌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相关，这是阻碍其广泛使用的一个关键障碍。临床实践中此类定量特征的一个重要原因是缺乏用于识别成像生物标志物的强大软件工具以及已被证明可以跨站点工作的经过验证的标志物集合，进展相对缓慢的部分原因是定量技术的发展。影像组学特征定义通常是孤立的；非标准化；实施发生在专有环境中，这使得科学交流变得困难；并且分析集中于单一疾病部位或成像模式。在这里，我们建议构建一个公开可用的计算放射组学系统，用于客观和自动提取定量成像特征。我们相信，与常规提取的肿瘤大小描述符相比，将产生具有更大预后价值的生物标志物。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的通用、开放、便携式和可扩展的放射组学平台。类型和成像模式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发展，为了实现我们的目标，我们将识别和实施大量定量成像特征，开发可供图像分析专家使用的灵活放射组学平台。作为工程科学家）和成像非专家（例如生物信息学科学家或医生）等，并通过整合放射组学、基因组学和临床数据来验证这些进展，以评估预后性能并检查关联性。 (TCIA) 的成像数据，以及癌症基因组图谱 (TCGA) 的相应基因组和临床数据，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。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，我们相信这些专家使我们能够大幅推进放射组学领域，并提供允许其在临床中转化使用的工具。