Collaborative Research: A Generalizable Data Framework Towards Precision Radiotherapy

在癌症患者的放射治疗中，不同的患者对同一类型的放射治疗可能会有不同的反应。因此，根据患者的健康数据、临床情况以及随时间推移的反应来个体化放射治疗至关重要。该项目的目标是开发一个可通用的数据框架，以支持癌症患者个体的精确放射治疗。具体地说，将建立深度强化学习模型，并使用在个体患者的诊断、治疗和随访期间获得的多模式成像数据进行验证。医学成像数据与基因和临床数据的协调将创建一个宝贵的知识储存库，以供借鉴，同时呼吁进行新的分析。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产生的丰富数据，该项目旨在开发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，将建立一个集成模型来分析所有对患者结果预测有用的数据类型。该模型将用独立的数据集进行验证，以确保泛化。为了将来自多种成像模式的信息与治疗计划相结合，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样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，就有可能推广到各种其他癌症。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。该框架的目的是协调和综合各种证据和衡量标准，以便客观评估和量化结果和终点。这一战略最终将为高度个性化的患者管理提供新的患者分层和支持临床决策。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.