Shared Resource Core 2: Clinical Artificial Intelligence Core

共享资源核心2：临床人工智能核心

• 批准号: 10712296
• 负责人: Hugo Aerts
• 金额: $ 14.19万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712296
• 关键词: Academic Training; Acceleration; Address; Adoption; Algorithmic Analysis; Apache; Artificial Intelligence; Artificial Intelligence platform; Biological Assay; Cancer Biology; Cancer Research Project; Characteristics; Clinic; Clinical; Clinical Data; Communities; Community Clinical Oncology Program; Complex; Computer software; Computerized Medical Record; Data; Data Analyses; Data Commons; Data Science; Data Science Core; Data Set; Ensure; Goals; Heterogeneity; Image; Industry; Informatics; Infrastructure; Intelligence; Investigation; Investments; Knowledge; Late Effects; Malignant Childhood Neoplasm; Malignant Neoplasms; Measures; Medical; Medical Imaging; Medicine; Methods; Modeling; Molecular; Multiomic Data; Natural Language Processing; Neuroblastoma; Patients; Pattern; Phenotype; Prediction of Radiation Response; Preparation; Radiation; Radiation Oncology; Radiation therapy; Reproducibility; Research; Research Project Grants; Research Support; Resource Sharing; Resources; Scientific Advances and Accomplishments; Semantics; Standardization; Technology; Text; The Cancer Imaging Archive; Toxic effect; Training; anticancer research; artificial intelligence algorithm; artificial intelligence method; cancer therapy; clinical phenotype; clinical translation; data streams; electronic data; experience; imaging biomarker; imaging probe; improved; multiple omics; open source; programs; quantitative imaging; radiation during childhood; radiation resistance; radiation response; radiological imaging; radiomics; response; synergism; tool; treatment response; tumor; tumor heterogeneity; usability

PROJECT SUMMARY Artificial intelligence (AI) algorithms have the potential to fundamentally change medicine through their ability to recognize complex patterns in medical data. The Clinical Artificial Intelligence and Imaging Core (AI Core) is an essential shared resource that will support the Aims of the Harvard/UCSF ROBIN Research Projects to enable large-scale analysis of granular clinical data, allowing non-invasive characterization of tumoral and patient heterogeneity and a path towards clinical translation. This will be achieved through the following Specific Aims: i) retrieve, curate, and annotate digitized clinical data to support quantitative analyses and AI/informatics pipelines for the ROBIN Molecular Characterization Trial and Research Projects, which will produce one of the most comprehensive datasets for DMG and neuroblastoma patients in existence for AI- based data analysis, ii) develop and evaluate task-specific AI pipelines using our well-established data preprocessing, AI-derived imaging biomarkers, and natural language processing (NLP) platforms for tumor heterogeneity, radiation resistance/response, and toxicity characterization in accordance with the Research Projects and Data Science Core, and iii) standardize and release AI/informatics methods across data types and applications in ways that ensure transparency, reproducibility, and access to advance scientific knowledge within the wider research field, as well as accelerate clinical translation to the pediatric radiation oncology clinic. Achieving these aims will be possible through synergy with the molecular mechanistic analyses in the Data Science Core, as well as with the ROBIN-NEST Cross-Training Core and Administrative Core to disseminate our methods and provide training to the greater ROBIN Network and the scientific community. This Core is led by pioneers in the field of AI analysis of medical imaging (PI: Aerts) and clinical text (PI: Savova), with significant experience building open access platforms for medical AI applications. For imaging analysis, we developed and maintain PyRadiomics, one of the world’s most widely used and highly cited radiomics pipelines, developed with support of NCI’s investments in infrastructure and data, including the Informatics Technology for Cancer Research (ITCR), Imaging Data Commons (IDC), and Quantitative Imaging Network (QIN) programs. For clinical text, we have developed Apache cTakes(™), a leading open access natural language processing platform for extracting medical, grammatical, and semantic information from clinical texts, and DeepPhe, an open-source software for cancer clinical phenotyping, also supported by the NCI’s ITCR program (PI: Savova). We will use and build on our open access methods and state-of-the art AI- based phenotyping methods developed in these NCI projects to support the Harvard/UCSF ROBIN investigators to incorporate fundamental clinical -omics data into their investigation of intratumoral heterogeneity and predictors of radiation response and late effects.

项目总结 人工智能(AI)算法有可能通过它们的能力从根本上改变医学 识别医疗数据中的复杂模式。临床人工智能与成像核心(AI核心) 是一个重要的共享资源，将支持哈佛大学/加州大学旧金山分校罗宾研究项目的目标 实现对细粒度临床数据的大规模分析，允许对肿瘤和 患者的异质性和临床转化的路径。这将通过以下方式实现 具体目标：i)检索、整理和注释数字化临床数据，以支持量化分析和 人工智能/信息学管道，用于Robin分子表征试验和研究项目，这将 为人工智能现有的DMG和神经母细胞瘤患者提供最全面的数据集之一 基于数据分析，ii)使用我们完善的数据开发和评估特定于任务的人工智能管道 肿瘤的预处理、AI衍生的成像生物标记物和自然语言处理(NLP)平台 异质性、辐射抗性/反应和毒性表征与研究一致 项目和数据科学核心，以及III)跨数据类型标准化和发布人工智能/信息学方法 以确保透明度、再现性和获取先进科学知识的方式进行应用 在更广泛的研究领域内，以及加速向儿科放射肿瘤学的临床转化 诊所。通过与分子机制分析的协同，实现这些目标将是可能的 数据科学核心，以及与Robin-Nest交叉培训核心和管理核心 传播我们的方法，并向大罗宾网络和科学界提供培训。 这一核心由医学成像(PI：Aerts)和临床文本(PI： Svova)，拥有为医疗人工智能应用程序构建开放访问平台的丰富经验。用于成像 分析，我们开发并维护了世界上使用最广泛、被引用最多的 放射组学管道，在NCI在基础设施和数据方面的投资支持下开发，包括 癌症研究信息学技术(ITCR)、成像数据共享(IDC)和定量成像 网络(秦)节目。对于临床文本，我们开发了ApachecTake(™)，这是一种领先的开放获取 用于提取医疗、语法和语义信息的自然语言处理平台 临床文本，以及癌症临床表型分析的开源软件DeepPhe，也得到了 NCI的ITCR计划(PI：SAVOVA)。我们将使用并构建我们的开放访问方法和最先进的人工智能- 在这些NCI项目中开发的基于表型的方法，以支持哈佛/加州大学旧金山分校的罗宾 研究人员将基础临床组学数据纳入肿瘤内调查 辐射反应和迟发效应的异质性和预测因子。