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。

基本信息

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

项目摘要

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.

在治疗放射治疗的癌症患者时，不同的患者对相同类型的放射疗法的反应可能不同。因此，根据患者的健康数据，临床状况以及随着时间的推移的反应来个性化辐射治疗至关重要。该项目的目的是开发一个可推广的数据框架，该框架可以支持单个癌症患者的精确放疗。具体而言，将通过在诊断，治疗和随访的个别患者的诊断，治疗和随访期间获得的多模式成像数据来建立和验证深厚的增强学习模型。使用遗传和临床数据将医学成像数据统一将创建一个宝贵的知识存储库，同时要求新的分析。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，通过利用放射疗法诊所产生的数据丰富，该项目旨在开发一种广义的深层增强学习工具（DRL）进行癌症风险分层。基于DRL工具，将建立一个集合模型，以分析所有对患者结果预测有用的数据类型。该模型将通过独立数据集进行验证，以确保概括。为了说明来自多种成像方式的信息与治疗计划相结合，将开发和培训多模式深钢筋学习（MDRL）模型，并使用存储在电子医疗记录系统中的患者数据以及源自血液和组织标本的基因组信息。检测工具将用于肺癌和结直肠癌患者。一旦工具可用于临床研究社区，将可能对其他癌症进行概括。整体模型将允许对沿患者结果轨迹记录的多种数据类型进行整合分析，在肿瘤表型和优越的预测能力之间提供更好的歧视。该框架将旨在协调和合成各种类型的证据和测量，以分数，以评估和量化结果和终点。该策略最终将为高度个性化的患者管理提供新颖的患者重新分层并为临床决策提供支持。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来支持的。