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.

项目摘要 人工智能（AI）算法有可能通过其能力从根本上改变医学 公认的医学数据中的复杂模式。临床人工智能和成像核心（AI核心） 是一种基本共享资源，它将支持哈佛/UCSF Robin研究项目的目标 对颗粒临床数据进行大规模分析，从而允许对肿瘤和 患者异质性和通往临床翻译的途径。这将通过以下 具体目的：i）检索，策划和注释数字化的临床数据，以支持定量分析和 Robin分子表征试验和研究项目的AI/信息学管道，该项目将 生产最全面的DMG和神经母细胞瘤患者的数据集之一 基于数据分析，ii）使用我们良好的数据开发和评估特定于任务的AI管道 肿瘤的预处理，AI衍生的成像生物标志物和自然语言处理（NLP）平台 根据研究 项目和数据科学核心，以及iii）标准化和释放数据类型的AI/信息学方法 以及以确保透明度，可重复性并获得进步科学知识的方式的应用 在广泛的研究领域，以及加速临床翻译为小儿辐射肿瘤学 诊所。通过与分子机械分析的协同作用实现这些目标是可能的 数据科学核心，以及罗宾 - 纽约交叉训练的核心和行政核心 传播我们的方法，并为更大的罗宾网络和科学界提供培训。 该核心由医学成像分析领域（PI：AERTS）和临床文本（PI：：PI： Savova），具有丰富的经验，为医疗AI应用程序构建开放访问平台。用于成像 分析，我们开发和维护了pyradiomys，这是世界上最广泛使用和高度引用的pyradiomys之一 在支持NCI在基础架构和数据上的投资的支持下开发的放射线管道，包括 癌症研究的信息技术（ITCR），成像数据共享（IDC）和定量成像 网络（QIN）程序。对于临床文本，我们开发了Apache Ctakes（™），这是一个领先的开放访问 自然语言处理平台，用于从中提取医学，语法和语义信息 临床文本和Deepphe是一种用于癌症临床表型的开源软件，也支持 NCI的ITCR计划（PI：Savova）。我们将使用和建立我们的开放访问方法和最先进的技术 在这些NCI项目中开发的基于基于的表型方法，以支持哈佛/UCSF Robin 研究人员将基本临床 - 摩学数据纳入其肿瘤内研究 异质性和辐射反应的预测因素和迟到效应。