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.

在用放射治疗治疗癌症患者时，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、临床状况以及随时间的反应进行个体化放射治疗至关重要。该项目的目标是开发一个通用的数据框架，可以支持个体癌症患者的精准放射治疗。具体来说，将利用在个体患者的诊断、治疗和随访过程中获得的多模态成像数据来建立和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可供借鉴的宝贵知识库，同时需要新的分析。开发的数据框架将为个体化放疗提供关键的临床决策支持。通过利用放疗诊所产生的大量数据，该项目旨在开发一种用于癌症风险分层的通用深度强化学习（DRL）工具。基于 DRL 工具，将构建一个集成模型来分析对患者结果预测有用的所有数据类型。该模型将使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，将使用电子病历系统中存储的患者数据以及来自血液和组织标本的基因组信息来开发和训练多模式深度强化学习（mDRL）模型。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，就可以推广到各种其他癌症。该集成模型将允许对沿患者结果轨迹记录的多种数据类型进行综合分析，提供更好的肿瘤表型区分和卓越的预测能力。该框架旨在协调和综合各种类型的证据和测量结果和终点的客观评估和量化分数。该策略最终将提供新颖的患者重新分层，并支持高度个性化患者管理的临床决策。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。