Collaborative Research: A generalizable data framework towards Precision Radiotherapy.

协作研究：精准放射治疗的通用数据框架。

• 批准号: 2313443
• 负责人: Enrico Capobianco
• 金额: $ 31.06万
• 依托单位: Jackson Laboratory
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313443&HistoricalAwards=false
• 关键词: Collaborative; Research; generalizable; data; framework

In treating cancer patients with radiation therapy, different patients may have different responses to the same type of radiotherapy. Hence, it is critical to individualize the radiation treatment based on the patient's health data, clinical conditions, as well as response over time. The goal of this project is to develop a generalizable data framework that can support precision radiotherapy for individual cancer patients. Specifically, a deep reinforcement learning model will be built and validated with multimodal imaging data acquired during diagnosis, treatment and follow-up of individual patients. Harmonization of the medical imaging data with genetic and clinical data will create an invaluable repository of knowledge to draw from, while calling for new analytics. The developed data framework will provide critical clinical decision support for individualized radiotherapyBy leveraging the wealth of data generated in the radiotherapy clinic, the project aims to develop a generalized deep reinforcement learning (DRL) tool for cancer risk stratification. Based on the DRL tool, an ensemble model will be built to analyze all the data types useful to patient outcome prediction. The model will be validated with independent datasets to ensure generalization. To account for information from multiple imaging modalities combined with treatment plans, a multimodal deep reinforcement learning (mDRL) model will be developed and trained with patient data stored in the electronic medical record system, as well as genomic information derived from blood and tissue specimens. The detection tool will be used in both lung cancer and colorectal cancer patients. Generalization to a variety of other cancers will be possible once the tools become available to the clinical research community. The ensemble model will allow integrated analysis of multiple data types recorded along the patient outcome trajectory, provide better discrimination between tumor phenotypes and superior predictive power. The framework will be designed to coordinate and synthesize various types of evidence and measurements into scores for the objective assessment and quantification of outcomes and endpoints. This strategy will ultimately provide novel patient re-stratification and support clinical decisions for highly individualized patient management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在对癌症患者进行放射治疗时，不同的患者对同一类型的放射治疗可能有不同的反应。因此，根据患者的健康数据、临床条件以及一段时间内的反应来个性化放射治疗是至关重要的。该项目的目标是开发一个可推广的数据框架，以支持个体癌症患者的精确放疗。具体而言，我们将利用个体患者在诊断、治疗和随访过程中获得的多模态成像数据建立并验证深度强化学习模型。医学成像数据与遗传和临床数据的协调将创建一个宝贵的知识库，同时需要新的分析方法。开发的数据框架将为个性化放射治疗提供关键的临床决策支持。通过利用放射治疗临床产生的丰富数据，该项目旨在开发一种用于癌症风险分层的广义深度强化学习（DRL）工具。基于DRL工具，构建一个集成模型来分析所有对患者预后预测有用的数据类型。模型将用独立的数据集进行验证，以确保泛化。为了考虑多种成像模式与治疗计划相结合的信息，将开发一个多模式深度强化学习（mDRL）模型，并使用存储在电子病历系统中的患者数据以及来自血液和组织标本的基因组信息进行培训。该检测工具将用于肺癌和结直肠癌患者。一旦临床研究界可以使用这些工具，将有可能推广到各种其他癌症。集成模型将允许沿患者预后轨迹记录的多种数据类型的集成分析，提供更好的肿瘤表型区分和卓越的预测能力。该框架将被设计用于协调和综合各种类型的证据和测量结果，以客观评估和量化结果和终点。该策略最终将提供新的患者重新分层，并支持高度个性化患者管理的临床决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。