A-STOR Cancer Clinical Trial Artificial Intelligence & Machine Learning Readiness

A-STOR癌症临床试验人工智能

• 批准号: 10593808
• 负责人: Suzanne George
• 金额: $ 20.76万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-17 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593808
• 关键词: Acceleration; Address; Adoption; Adverse event; Artificial Intelligence; Biological Markers; Cancer and Leukemia Group B; Clinical; Clinical Data; Clinical Informatics; Clinical Trials; Clinical Trials Network; Cloud Computing; Complement; Computational Biology; DNA sequencing; Data; Data Pooling; Data Set; Decentralization; Development; Disease; Doctor of Philosophy; Ensure; FAIR principles; Feedback; Foundations; Future; Genomics; Goals; Grant; Health Insurance Portability and Accountability Act; Image; Infrastructure; Machine Learning; Malignant Neoplasms; Manuals; Meta-Analysis; Metadata; Modeling; Modernization; Molecular; Molecular Profiling; National Cancer Institute; National Clinical Trials Network; Neoadjuvant Therapy; Ontology; Outcome; Output; Performance; Phase; Phase III Clinical Trials; Pilot Projects; Procedures; Process; Public Health; Publications; Randomized Clinical Trials; Readiness; Research; Research Design; Research Personnel; Resources; Secure; Site; Standardization; Structure; Testing; Translational Research; base; cBioPortal; cancer clinical trial; chemotherapy; cloud based; computer science; dashboard; data dictionary; data exploration; data harmonization; data repository; data reuse; data submission; data visualization; database of Genotypes and Phenotypes; digital pathology; encryption; flexibility; genomic data; high dimensionality; improved; interoperability; machine learning algorithm; machine learning model; multimodality; neoplasm resource; operation; pathology imaging; precision oncology; predicting response; prospective; repository; targeted biomarker; transcriptome sequencing; triple-negative invasive breast carcinoma

PROJECT SUMMARY High-dimensional molecular profiling, including genomic sequencing and digital pathology, has revolutionized our understanding of cancer and has led to the identification of multiple new targets and biomarkers. However, barriers impede unlocking the full potential of these data because most ‘omic data generated from National Cancer Institute’s National Clinical Trials Network (NCTN) clinical trials are decentralized, with data housed at a variety of sites and analyses taking place locally. To overcome these barriers for Alliance NCTN cooperative group trials, in 2020 we established A-STOR: Alliance Standardized Translational Omics Resource, a secure, HIPAA-compliant centralized repository of raw, and processed genomic and pathology image data from Alliance clinical trials, with standardized genomic data harmonization to facilitate cross-study analyses. In this proposal (a supplement to the Alliance U10 grant), we aim to realize the potential of A-STOR for artificial intelligence (AI)/machine learning (ML) modeling by rapidly expanding our existing infrastructure to: host data from over a dozen existing or ongoing Alliance/NCTN clinical trials (Aim 1), optimize infrastructure for cloud-based AI/ML analyses including engagement with and feedback from AI/ML applications (Aim 1-2), ensure FAIR principles in developing a unified clinical and adverse event (AE) data dictionary to facilitate clinical data harmonization (Aim 2), and complete an already-approved pooled multi-modal ML-based predictor as a pilot study (Aim 3). The impact of these aims will collectively capitalize on the expertise of Alliance A-STOR Co-Chairs Dr. Daniel Stover (clinical informatics, genomics) and Dr. James Blachly (computational biology, computer science), to optimize foundational infrastructure, clinical/AE harmonization to facilitate data reuse as part of AI/ML efforts, and cloud- based AI/ML compute optimization for data generated through Alliance NCTN cancer clinical trials.

项目摘要 包括基因组测序和数字病理学在内的高维分子分析已经彻底改变了 我们对癌症的理解，并导致了多个新的目标和生物标志物的识别。然而，在这方面， 障碍阻碍了这些数据的全部潜力的释放，因为大多数的“omic数据产生于国家 癌症研究所的国家临床试验网络（NCTN）临床试验是分散的，数据保存在一个 在当地进行的各种现场和分析。为了克服这些障碍，联盟NCTN合作 2020年，我们建立了A-STOR：联盟标准化翻译组学资源，一个安全， 符合HIPAA标准的Alliance原始和处理后的基因组和病理学图像数据集中存储库 临床试验，标准化的基因组数据协调，以促进交叉研究分析。本提案中 （对联盟U10赠款的补充），我们的目标是实现A-STOR在人工智能方面的潜力 (AI)/机器学习（ML）建模，通过快速扩展我们现有的基础设施， 数十项现有或正在进行的联盟/NCTN临床试验（目标1），优化基于云的AI/ML的基础设施 分析，包括与AI/ML应用程序的互动和反馈（目标1-2），确保公平原则， 开发统一的临床和不良事件（AE）数据词典，以促进临床数据协调（目的 2），并完成一个已经批准的汇总多模态ML为基础的预测作为试点研究（目标3）。的 这些目标的影响将共同利用联盟A-STOR联合主席丹尼尔秸秆博士的专业知识 （临床信息学，基因组学）和James Blachly博士（计算生物学，计算机科学），以优化 基础基础设施，临床/AE协调，以促进数据重用，作为AI/ML工作的一部分，以及云- 基于AI/ML的计算优化，用于通过Alliance NCTN癌症临床试验生成的数据。