Optimal Decision Making in Radiotherapy Using Panomics Analytics

使用全景分析进行放射治疗的最佳决策

• 批准号: 10416058
• 负责人: Issam M. El Naqa
• 金额: $ 45.72万
• 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/10416058
• 关键词: 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 Assisted; Computer software; Computers; Data; Data Reporting; Data Scientist; Data Set; Databases; Decision Making; Decision Support Systems; Development; Disease; Dose; Dose Fractionation; Environment; Equilibrium; Genomics; Goals; Graph; Hostage; Human; Infrastructure; Institution; Intelligence; Investigation; Knowledge; Learning; Liver; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of lung; Medical; Methods; Modeling; Modernization; Nature; Normal tissue morphology; Oncology; Outcome; Patient Preferences; Patients; Performance; Physicians; Play; Procedures; Proteomics; Psychological reinforcement; Quality of life; Radiation Dose Unit; Radiation therapy; Reaction Time; Regimen; Regrets; Rewards; Risk; Role; Schedule; Site; Software Tools; Source; System; Techniques; Testing; Time; Toxic effect; Treatment Protocols; Uncertainty; Work; base; clinical center; clinical decision support; clinical practice; clinical research site; computer human interaction; deep learning; deep reinforcement learning; demographics; experience; fractionated radiation; heuristics; high reward; high risk; image guided; imaging biomarker; improved; individual patient; irradiation; knowledge base; learning strategy; machine learning algorithm; machine learning framework; machine learning method; multidisciplinary; neoplastic cell; outcome prediction; personalized decision; personalized medicine; point of care testing; population based; predicting response; predictive marker; profiles in patients; prototype; radiation risk; radiomics; rapid growth; response; side effect; socioeconomics; success; supervised learning; support tools; therapy outcome; tool; treatment choice; treatment duration; treatment optimization; treatment response; tumor; 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 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 have assembled a multidisciplinary team including: clinicians, medical physicists, data scientists, and human factor experts. Specifically, we will develop and quantitively evaluate: (1) graph-based supervised machine learning algorithms for robust prediction outcomes before and during RT; (2) deep reinforcement learning to dynamically optimize treatment adaptation; and (3) a user-centered software prototype for RT decision support, 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 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 tool 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 an intelligent, computer-aided, user-centered, personalized CDSS 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