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

这个小企业创新研究（SBIR）二期项目的更广泛的影响/商业潜力是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。癌症仍然是美国第二大死因。目前，只有在新设备上投入大量资金才能实现，成功的治疗取决于“在放射治疗当天”精确描绘和适应肿瘤位置的能力。社区中心负担不起用更精确、更昂贵的方式取代现有的机器。特别是在资源有限的农村社区，很少有被诊断患有癌症的人能够负担得起旅行，并承担在当地酒店住4-6周的费用，每天接受只有富裕机构才有的最先进技术的治疗。使用一种新颖的基于云的架构和专有软件，该第二阶段合同将允许远程诊所和任何医院以高度提高的实时准确性治疗患者。通过使现有的放射治疗机器能够向肿瘤的当前位置提供适应剂量，放射肿瘤学家可以显著提高治疗效果，患者的生活质量，减少患者再次住院，并降低治疗成本。这项小型企业创新研究（SBIR）二期项目解决了在整个癌症放射治疗过程中观察解剖结构每日变化的未满足需求，并有效地仅针对肿瘤。今天，医生根据放射治疗开始前几天甚至几周拍摄的图像设计治疗计划和剂量处方，在肿瘤周围产生包括健康组织的误差范围。不明原因的解剖改变相当频繁，导致治疗质量下降，结果不佳，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。第二阶段将专注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)注册误差量化。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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.