NRG Oncology Network Group Operations Center

NRG肿瘤学 面向AI/ML标准化的肿瘤学操作本体（O3） Charles马约，博士，补充主要研究者 项目摘要 母基金，NRG肿瘤学，是由前国家外科 辅助乳腺和肠道项目（NSABP），放射治疗肿瘤学组（RTOG），以及 妇科肿瘤组（GOG）的目标是通过实践改善癌症患者的生活- 改变多机构临床和转化研究，重点关注性别特异性恶性肿瘤 以及所有类型的局部或局部晚期癌症。该提案概述了一个补充项目，其目的是 开发方便用户的工具，以便在不同的放射治疗数据标准之间实现无缝转换 NCI临床试验期间收集的数据元素。该项目的目标是标准化和互操作性 放射治疗数据，这对于促进在该领域使用机器学习和人工智能至关重要。 该项目的具体目标包括开发一个全面的映射和转换框架， OOO/FHIR、CDISC、OMOP和NCI叙词表标准，设计和实施用户友好的工具， 数据聚合和无缝转换，以及与合作伙伴一起对开发的工具进行试点测试和验证 研究机构和NCI临床试验数据。该小组将与这些标准组的领导层协调， 确保绘图和概念定义的准确性。开发的工具将包括数据验证和 错误处理机制，以确保转换过程中的数据质量和完整性。最终 该项目旨在促进更广泛的放射肿瘤学社区采用这些工具，特别是 NCI临床试验，并有助于改善癌症患者的结果和推进癌症研究。 该项目的目标是： 目标1 -我们将扩展我们以前的工作与O3，提供易于访问，公开可用的映射 O3、OMOP、CDISC、NCI Thesaurus和HL 7-FHIR之间的相互关系，支持癌症治疗的临床试验。 目标2 -我们将扩展我们的数据提取工具，支持基于O3的映射和连接 人工智能（AI）准备好的数据集到机器学习算法。 目标3目标2中开发的工具将由一系列机构使用，以证明有能力 汇总代表性（5例患者/机构）患者集的RTOG 0617信息。 Aim 4映射和工具将在可公开访问的GitHub存储库上发布。

NRG Oncology Operational Ontology for Oncology (O3) towards AI/ML Ready Standardization Charles Mayo, PhD, Supplement Principal Investigator Project Summary The parent grant, NRG Oncology, was formed as a collaborative effort between the former National Surgical Adjuvant Breast and Bowel Project (NSABP), the Radiation Therapy Oncology Group (RTOG), and the Gynecologic Oncology Group (GOG) with the goal of improving the lives of cancer patients through practice- changing multi-institutional clinical and translational research, with emphases on gender-specific malignancies and localized or locally advanced cancers of all types. This proposal outlines a supplement project that aims to develop user-friendly tools to enable the seamless conversion between different data standards for radiotherapy data elements collected during NCI clinical trials. The project targets the standardization and interoperability of radiotherapy data, which is crucial for facilitating the use of machine learning and artificial intelligence in the field. The project's specific aims include developing a comprehensive mapping and conversion framework between OOO/FHIR, CDISC, OMOP, and NCI Thesaurus standards, designing and implementing user-friendly tools for data aggregation and seamless conversion, and pilot testing and validating the developed tools with collaborating institutions and NCI clinical trial data. The team will coordinate with leadership from these standards groups to ensure accuracy in mapping and concept definitions. The tools developed will incorporate data validation and error-handling mechanisms to ensure data quality and integrity during the conversion process. Ultimately, the project aims to promote the adoption of these tools by the broader radiation oncology community, specifically for NCI clinical trials, and contribute to improving cancer patient outcomes and advancing cancer research. The aims of the project are: Aim 1 - We will extend our prior work with O3, providing easily accessible, publicly available mappings between O3, OMOP, CDISC, NCI Thesaurus and HL7-FHIR supporting clinical trials for cancer care. Aim 2 – We will extend our tools for data extraction supporting mapping and connection of O3 based Artificial Intelligence (AI) ready data sets to machine learning algorithms. Aim 3 The tools developed in aim 2 will be used by a set of institutions to demonstrate the ability to aggregate RTOG 0617 information for a representative (5 patients/institution) set of patients. Aim 4 Mappings and tools will be published on a publicly accessible GitHub repository.