SBIR Phase I: A Data-Driven, Knowledge-Based Platform for Peer Review in Radiation Oncology Treatment Planning

这个SBIR第一阶段项目将利用放射肿瘤学实践常规产生的数据，使放射肿瘤学家评估计划治疗质量的同行审查过程自动化。每个癌症患者都有与他们的疾病相关的独特的正常解剖结构，这影响了所提供的治疗的特点。一个例子是，更接近受辐射肿瘤的器官更难达到低剂量。今天，对计划治疗的质量评估实际上假设每个患有特定疾病的患者的解剖几何形状是相同的。这项拟议的创新通过将患者的解剖结构与数千名过去的患者进行比较，使放射治疗计划的评估个性化。达到的最高标准被用作当前患者的标准。此外，计划评估从主观的、容易出错的过程转变为高效的标准化过程，包括复杂的异常检测。对患者的好处是健康器官的剂量更低，治疗受到错误影响的风险也更低。这可以减少副作用，提高生活质量。对诊所的好处是工作流程效率和改进的质量指标，从而节省成本并证明高标准的实践。拟议的软件产品是一个基于现有患者治疗计划的大型数据库的放射肿瘤学治疗计划自动同行审查系统。这项创新的独特优势在于它基于一个庞大而多样的数据集，可以提供参考患者，以便与当前患者进行有意义的比较。在治疗计划过程中，该系统将允许放射肿瘤学家检测剂量处方和结构描述中的异常，并获得患者特定的剂量学目标，以此来评估治疗计划的质量。机器学习方法将用于异常检测，其中偏离正常标准的情况将被标记为需要检查。剂量学目标将使用搜索程序获得，该程序考虑到每个目标体积和每个危险器官之间的几何关系。自动同行审查将允许在治疗计划过程本身进行计划评估，而不是在患者开始治疗过程后的回顾步骤。前瞻性审查允许根据个别患者的特征进行定制，从而将同行审查的作用从质量保证扩展到放射治疗的个性化。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。

This SBIR Phase I project will leverage the data generated routinely by radiation oncology practices to automate the peer-review process in which radiation oncologists evaluate the quality of planned treatments. Each cancer patient possesses a unique layout of normal anatomy relative to their disease and this impacts the characteristics of the treatment delivered. An example is that it is harder to achieve a low dose to an organ that is closer to the irradiated tumor. Today, quality evaluation for a planned treatment effectively assumes that the anatomical geometry of every patient with a given disease is the same. The proposed innovation personalizes the evaluation of radiotherapy treatment plans by comparing a patient's anatomy to thousands of past patients. The highest standards achieved are used as a yardstick for the current patient. Furthermore, plan evaluation is transformed from a subjective, error-prone, procedure to an efficient standardized process, including sophisticated anomaly detection. The benefit to the patient is lower doses to healthy organs and lower risk of their treatment being impacted by errors. This can reduce side effects and improve quality of life. The benefit to clinics is workflow efficiency and improved quality metrics, resulting in cost savings and proof of high practice standards. The proposed software product is an automated peer review system for radiation oncology treatment planning based on a large database of existing patient treatment plans. The unique strength of the innovation lies in it being based on a large and varied dataset that can provide reference patients to which the current patient can be meaningfully compared. During the treatment planning process, the system will allow radiation oncologists to detect anomalies in dose prescription and structure delineation, as well as obtain patient-specific dosimetric objectives against which to evaluate treatment plan quality. Machine learning approaches will be used for anomaly detection, where deviations from the norm will be flagged as requiring examination. Dosimetric objectives will be obtained using a search procedure that considers the geometric relationships between each targeted volume and each organ at risk. Automated peer-review will allow plan assessment during the treatment planning process itself rather than it being a retrospective step after the initiation of a patient's treatment course. Prospective review allows for customization based on individual patient characteristics and thus expands the role of peer review from quality assurance to the personalization of radiation treatments.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.