SBIR Phase II: A cloud-native, data-driven platform for automated quality assurance of radiation oncology treatment planning

SBIR 第二阶段：一个云原生、数据驱动的平台，用于放射肿瘤治疗计划的自动化质量保证

• 批准号: 2035750
• 负责人: Michael Bowers
• 金额: $ 100万
• 依托单位: Oncospace, Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035750&HistoricalAwards=false
• 关键词: SBIR; Phase; II; cloud; native

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improves patient outcomes in radiotherapy treatment. Healthcare providers are recognizing the growing imperative to lower costs and maintain quality of care by harnessing the digital health records available. In radiation oncology, only a small fraction of the valuable data produced during routine patient care is leveraged for the care of new patients. High-dose curative treatments frequently lead to side effects, such as dry mouth in head and neck cancer treatment, and rectal bleeding in prostate treatments. An optimized treatment plan could minimize complications. The proposed project will advance an information system to optimize and personalize radiation treatments. The proposed project leverages radiotherapy data to optimize treatment plans. Dose to organs at risk (OARs) must be kept low while ensuring the prescribed dosimetric coverage of the targeted disease. Intensity modulated radiotherapy (IMRT) treatments require significant optimization, creating extensive planning processes. This project will advance a treatment plan database for a data-driven clinical decision support solution that can predict achievable dose levels to each OAR. These levels can be used as intelligent optimization objectives by the treatment planning system (TPS) and as plan evaluation criteria. The system will be built for continuous refinement through ongoing augmentation with new patient plans, thus improving prediction performance with ongoing use, and allowing each clinical site to base predictions on its own data. In addition, integrated peer review tools enable physicians to efficiently share information and get additional clinical insight.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.

这个小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是改善放射治疗的患者结局。 医疗保健提供者认识到，通过利用可用的数字健康记录来降低成本和保持护理质量的必要性越来越大。在放射肿瘤学中，只有一小部分在常规患者护理过程中产生的有价值的数据被用于新患者的护理。高剂量的治疗性治疗经常导致副作用，例如头颈癌治疗中的口干，前列腺治疗中的直肠出血。优化的治疗方案可以最大限度地减少并发症。拟议的项目将推进一个信息系统，以优化和个性化放射治疗。拟议的项目利用放射治疗数据来优化治疗计划。对危险器官的剂量必须保持在较低水平，同时确保目标疾病的规定剂量覆盖范围。调强放射治疗（IMRT）需要显著的优化，创建广泛的计划过程。 该项目将推进一个治疗计划数据库，用于数据驱动的临床决策支持解决方案，该解决方案可以预测每个OAR可达到的剂量水平。这些水平可以被治疗计划系统（TPS）用作智能优化目标，也可以用作计划评估标准。该系统将通过新患者计划的持续增强进行持续改进，从而提高持续使用的预测性能，并允许每个临床研究中心根据自己的数据进行预测。此外，集成的同行评审工具使医生能够有效地共享信息并获得额外的临床见解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响评审标准进行评估来支持。