SBIR Phase II: Cloud-based Automated Dose Accumulation for Online Adaptive Radiotherapy

SBIR 第二阶段：基于云的在线自适应放射治疗自动剂量累积

• 批准号: 1853110
• 负责人: Rodney Bosley
• 金额: $ 75万
• 依托单位: SEGANA, LLC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1853110&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Cloud; based

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is helping Community Cancer Centers serving the estimated 65% of patients receiving radiation therapies. Cancer is still the second leading cause of death in the United States. Currently accomplished only by significant investment in new equipment, successful therapy hinges upon the ability to delineate and adapt to a precise tumor location "on the day of" radiation delivery. Community Centers cannot afford to replace existing machines with more accurate and expensive modalities. Especially in rural communities with limited resources, few people diagnosed with cancer can afford to travel and incur the cost of staying 4-6 weeks in a local hotel to receive daily treatments from state of the art technologies located only in the wealthy institutions. Using a novel cloud-based architecture and proprietary software, this Phase II award will allow remote clinics, and any hospital, to treat patients with highly improved real-time accuracy. By enabling their existing radiotherapy machines to deliver an adaptive dose to the current location of the tumor, radiation oncologists can significantly improve treatment efficacy, a patient's quality of life, reduce patient re-hospitalizations, and reduce the cost of therapy.This Small Business Innovation Research (SBIR) Phase II project addresses the unmet need to see daily changes in anatomy throughout the course of cancer radiation treatment and effectively target only the tumor. Today, a physician designs a treatment plan and dose prescription based on images that are captured days or even weeks prior to initiation of radiation therapy, creating a margin of error around the tumor that encompasses healthy tissues. Unaccounted anatomical changes are quite frequent, leading to a diminished quality of treatment delivery, inferior outcomes, and a decreased quality of life. The phase I project investigated and developed a treatment-equipment agnostic approach to adapt the treatment to account for these anatomical changes. Phase II will focus on integrating artificial intelligence and deep neural analytics to predict two critical parameters for the treatment adaptivity: (a) treatment prognostics and (b) registration error quantification. Leveraging the company's cloud based, scalable, GPU computational framework, the project will develop and integrate patient-specific biomechanical models to automatically validate results and accurately predict future trends in patient-specific anatomical changes.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.

这一小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力正在帮助社区癌症中心为大约65%接受放射治疗的患者提供服务。癌症仍然是美国的第二大死因。目前仅通过对新设备的大量投资才能实现成功的治疗，成功的治疗取决于在放射治疗当天描绘和适应精确的肿瘤位置的能力。社区中心承担不起用更准确和更昂贵的模式取代现有机器的费用。特别是在资源有限的农村社区，很少有被诊断为癌症的人能够负担得起旅行，并承担在当地酒店停留4-6周的费用，以接受仅位于富裕机构的最先进技术的日常治疗。使用新的基于云的架构和专有软件，这项第二阶段奖励将允许偏远诊所和任何医院以极高的实时准确性治疗患者。通过使他们现有的放射治疗机器能够将适应剂量提供到肿瘤的当前位置，放射肿瘤学家可以显著提高治疗效率、患者的生活质量、减少患者再次住院并降低治疗成本。这一小企业创新研究(SBIR)第二阶段项目解决了未得到满足的需求，即在癌症放射治疗过程中看到解剖结构的每日变化，并有效地仅针对肿瘤。今天，医生根据在放射治疗开始前几天甚至几周拍摄的图像来设计治疗计划和剂量处方，从而在包含健康组织的肿瘤周围创造了一个误差边际。不明原因的解剖学变化相当频繁，导致治疗质量下降、预后不良和生活质量下降。第一阶段项目调查并开发了一种治疗设备不可知的方法，以适应这些解剖变化的治疗。第二阶段将侧重于整合人工智能和深度神经分析，以预测治疗适应性的两个关键参数：(A)治疗预后和(B)登记误差量化。利用该公司基于云的、可扩展的GPU计算框架，该项目将开发和集成患者特定的生物力学模型，以自动验证结果并准确预测患者特定解剖变化的未来趋势。该奖项反映了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。