Federated Learning for Optimal Decision Making in Radiotherapy Using Panomics Analytics

使用全景组学分析进行放射治疗最佳决策的联邦学习

• 批准号: 10417829
• 负责人: Issam M. El Naqa
• 金额: $ 15.87万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-06 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10417829
• 关键词: Address; Algorithms; Awareness; Bayesian Network; Benchmarking; Benefits and Risks; Big Data; Big Data Methods; Biological Markers; Case Study; Characteristics; Clinical; Clinical Decision Support Systems; Complex; Computer software; Computers; Data; Data Aggregation; Data Set; Decision Making; Decision Support Systems; Disease; Dose; Environment; Ethics; Genomics; Goals; Graph; Hostage; Human; Image; Improve Access; Infrastructure; Institution; Intelligence; Learning; Legal; Liver; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of lung; Methods; Modeling; Modernization; Nature; Normal tissue morphology; Outcome; Patient Preferences; Patients; Performance; Physicians; Play; Predictive Factor; Privacy; Procedures; Process; Proteomics; Psychological reinforcement; Quality of life; Radiation Dose Unit; Radiation therapy; Radiology Specialty; Reaction Time; Regimen; Rewards; Risk; Role; Sample Size; Schedule; Site; Software Tools; Supervision; System; Techniques; Testing; Time; Toxic effect; Training; Treatment Protocols; Uncertainty; Work; application programming interface; base; clinical decision support; clinical practice; computer human interaction; cost effective; data sharing; deep learning; deep reinforcement learning; demographics; experience; federated learning; fractionated radiation; heuristics; high reward; high risk; image guided; improved; individual patient; irradiation; knowledge base; learning algorithm; learning strategy; machine learning algorithm; machine learning method; machine learning model; neoplastic cell; open source; outcome prediction; personalized decision; personalized medicine; population based; predicting response; predictive modeling; profiles in patients; prototype; radiation risk; radiomics; rapid growth; response; socioeconomics; software systems; success; supervised learning; support tools; therapy outcome; tool; treatment choice; treatment duration; treatment optimization; treatment response; usability; user-friendly

The complex environment of modern radiation therapy (RT) comprises data from a rich combination of patient- specific information including: demographics, physical characteristics of high-energy dose, features subsequent to repeated application of image-guidance (radiomics), and biological markers (genomics, proteomics, etc.), generated before and/or over a treatment period that can span few days to several weeks. Rapid growth of these available and untapped “pan-Omics” data, invites ample opportunities for Big data analytics to deliver on the promise of personalized medicine in RT. This is particularly true in promising but high-risk RT procedures such as stereotactic body RT (SBRT), which have witnessed tremendous expansion due to clinical successes in early disease stages and socio-economic benefits of shortened high dose treatments. This has led to the desire to exploit these treatments into more advanced stages of cancer, however, the unknown risks associated with increased toxicities hamper its potential. Therefore, robust clinical decision support systems (CDSSs) capable of exploring the complex pan-Omics interaction landscape with the goal of exploiting known principles of treatment response before and during the course of fractionated RT are urgently needed. The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data. To meet this need and overcome current challenges, we will develop and quantitively evaluate: (1) federated graph-based supervised machine learning algorithms for robust prediction outcomes before and during RT; (2) federated deep reinforcement learning to dynamically optimize treatment adaptation; and (3) a user-centered software prototype for RT decision support using the extendable XNAT platform, with the broader goal of building a comprehensive real-time framework for outcome modeling and response-based adaption in RT. We hypothesize that the use of advanced federated machine learning techniques and user-centered tools will unlock the potentials to move from current population-based approaches limited by subjective experiences and heuristic rules into robust, patient-specific, user-friendly CDSSs. This approach and its corresponding software tools will be tested within two clinical RT sites of lung and liver cancers, to demonstrate its versatility and highlight pertinent human-computer factors and cancer specific issues. Impact statement: Patient-specific big data are now available before and/or during RT courses, offering new and untapped opportunities for personalized treatment. This study will overcome current shortcomings of population-based approaches and data underuse in current RT practice by investigating and developing a federated user-centered, personalized CDSS with the need for centralized data sharing and test its performance in rewarding but high-risk RT scenarios. The approach is also applicable to other modern cancer regimens.

现代放射治疗（RT）的复杂环境包括来自患者的丰富组合的数据