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

基本信息

批准号：
10669029
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
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-06 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10669029
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 Electronics Environment 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 Prediction of Response to Therapy 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 cancer radiation therapy 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 tumor eradication usability user-friendly

项目摘要

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.

现代放射疗法（RT）的复杂环境包括来自患者丰富组合的数据特定信息，包括：人口统计学，高能剂量的物理特征重复应用图像引导（放射线学）和生物标记（基因组学，蛋白质组学等），在治疗期之前和/或可能跨越几天到几周的治疗期。这些快速增长可用和未开发的“泛词”数据，邀请了大数据分析的充足机会。在RT中对个性化医学的承诺。这在承诺中尤其如此，但是高风险的RT程序，例如立体定向身体RT（SBRT），由于早期的临床成功而见证了巨大的扩张疾病阶段和缩短高剂量治疗的社会经济益处。这导致了渴望但是，这些治疗方法将这些治疗方法分为更高级的癌症，但是，与之相关的未知风险毒性增加阻碍了其潜力。因此，强大的临床决策支持系统（CDSS）探索复杂的泛媒体交互环境，目的是利用已知原理迫切需要在分离RT过程中和期间的治疗反应。长期目标该项目旨在克服与预测不确定性和人类计算机相互作用有关的障碍，这些障碍目前正在限制做出个性化临床决策以进行实时响应适应的能力在可用数据的放射疗法中。为了满足这一需求并克服当前的挑战，我们已经组装了多学科团队包括：临床医生，医学物理学家，数据科学家和人类因素专家。具体而言，我们将开发和定量评估：（1）基于图的监督机器学习算法在RT之前和期间的强大预测结果；（2）深入的强化学习以动态优化治疗适应；（3）以用户为中心的软件原型用于RT决策支持，其目标更广泛建立一个全面的实时框架，用于RT中的结果建模和基于响应的改编。我们假设使用高级机器学习技术和以用户为中心的工具将解锁通过主观经验和启发式限制的当前基于人群的方法的潜力规则符合强大的，特定于患者的，用户友好的CDSSS。这种方法及其相应的软件工具将在肺和肝癌的两个临床RT部位进行测试，以证明其多功能性并突出显示相关人类计算机因素和特定于癌症的问题。影响声明：现在和/或在RT课程期间提供特定于患者的大数据，提供新的以及未开发的个性化治疗机会。这项研究将克服当前的缺点通过调查和开发一个基于人群的方法和数据在当前的RT实践中无法使用智能，计算机辅助，以用户为中心的个性化CDS，并在奖励但高度测试其性能风险RT方案。该方法也适用于其他现代癌症方案。

项目成果

期刊论文数量（30）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Radiomics in nuclear medicine and hybrid imaging: current standings on clinical applicability.

核医学和混合成像中的放射组学：临床适用性的现状。

DOI：
10.23736/s1824-4785.19.03222-9
发表时间：
2019
期刊：
The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of...
影响因子：
0
作者：
Veit-Haibach,Patrick;ElNaqa,Issam;Visvikis,Dimitris
通讯作者：
Visvikis,Dimitris

A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).

DOI：
10.1038/s41598-023-32032-6
发表时间：
2023-03-31
期刊：
SCIENTIFIC REPORTS
影响因子：
4.6
作者：
Niraula, Dipesh;Sun, Wenbo;Jin, Jionghua;Dinov, Ivo D.;Cuneo, Kyle;Jamaluddin, Jamalina;Matuszak, Martha M.;Luo, Yi;Lawrence, Theodore S.;Jolly, Shruti;Ten Haken, Randall K.;El Naqa, Issam
通讯作者：
El Naqa, Issam

A Primer on Dose-Response Data Modeling in Radiation Therapy.

DOI：
10.1016/j.ijrobp.2020.11.020
发表时间：
2021-05-01
期刊：
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
影响因子：
7
作者：
Moiseenko, Vitali;Marks, Lawrence B.;Grimm, Jimm;Jackson, Andrew;Milano, Michael T.;Hattangadi-Gluth, Jona A.;Huynh-Le, Minh-Phuong;Pettersson, Niclas;Yorke, Ellen;El Naqa, Issam
通讯作者：
El Naqa, Issam

Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice.

癌症管理放射组学的现状：临床实践的挑战与机遇。