Collaborative Research: A generalizable data framework towards Precision Radiotherapy.

在用放射疗法治疗癌症患者时，不同的患者对相同类型的放射疗法可能有不同的反应。 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性化放射治疗至关重要。 该项目的目标是开发一个可推广的数据框架，可以支持针对个体癌症患者的精确放射治疗。 具体而言，将使用在诊断，治疗和随访个体患者期间获得的多模态成像数据构建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造一个宝贵的知识库，同时需要新的分析方法。 开发的数据框架将为个体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中产生的大量数据，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，将构建集成模型以分析对患者结局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，以确保通用性。 为了解释来自多种成像模式与治疗计划相结合的信息，将开发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训练。该检测工具将用于肺癌和结直肠癌患者。 一旦这些工具可供临床研究界使用，将有可能推广到各种其他癌症。 集成模型将允许沿着患者结果轨迹沿着记录的多种数据类型的集成分析，提供肿瘤表型之间的更好区分和上级预测能力。该框架将被设计为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的分数。这一战略将最终提供新的病人重新分层和支持高度个性化的病人管理的临床决策。这一奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.