STTR Phase I: Automated object contouring methods and software for head and neck radiotherapy planning

STTR 第一阶段：用于头颈部放射治疗计划的自动对象轮廓方法和软件

• 批准号: 1549509
• 负责人: David McLauglin
• 金额: $ 22.5万
• 依托单位: Quantitative Radiology Solutions LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-15 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549509&HistoricalAwards=false
• 关键词: STTR; Phase; Automated; object; contouring

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is in the field of radiation therapy (RT) for head & neck cancer patients. RT planning involves designing a radiation treatment regimen so that tumors are irradiated to definitive doses while minimizing irradiation to normal structures. For devising an optimal RT plan, target tumors and critical anatomic structures need to be accurately contoured on medical images. In current clinical practice, organ contour delineation is performed mostly manually due to lack of automated contouring software. This makes RT planning error prone, hampers throughput, and does not allow re-contouring to handle changes taking place during RT. Such changes can cause under dosing to tumor and overdosing to normal surrounding organs. In 2015, 1,658,370 new cancer cases are estimated to occur in the US, where nearly two-thirds will have RT. Given that there are over 2,100 RT centers in the US, there is a strong commercial opportunity for producing an auto-contouring software system. Expected clinical outcomes are significantly improved speed, throughput, and accuracy of contouring compared to current clinical practice, and improved patient outcomes and cost-savings.This Small Business Technology Transfer (STTR) Phase I project addresses a technical hurdle related to auto-contouring in RT planning for cancer patients. Current technical challenges for auto-contouring occur since available contouring methods have been developed mostly for a specific object on images of a particular modality. This project will overcome these hurdles through a novel automatic anatomy recognition methodology which will employ anatomy models derived from patient populations by including all major objects in a body region. The models will codify the rich object anatomic relationship, and will exploit this information to automatically locate and contour objects in any given patient image. The project will have two aims. Aim 1 involves the development of the method and prototype software for contouring major head & neck organs on CT and PET/CT images. Models will be built from already existing image and contour data of 200 cancer patients. Aim 1 outcome will be prototype software technically validated to be accurate within 1 pixel boundary distance compared to ground truth and requiring 3 minutes or less per study. Aim 2 will be a preliminary clinical assessment of the software in RT planning in patients with head & neck malignancies.

这个小型企业技术转移(STTR)第一阶段项目的更广泛的影响/商业潜力是在头颈癌患者的放射治疗(RT)领域。RT计划包括设计放射治疗方案，以使肿瘤被照射到确定的剂量，同时将对正常结构的辐射降至最低。为了设计最佳的放疗计划，需要在医学图像上准确地勾画出靶肿瘤和关键解剖结构的轮廓。在目前的临床实践中，由于缺乏自动轮廓绘制软件，器官轮廓的描绘大多是手动进行的。这使得RT规划容易出错，阻碍了吞吐量，并且不允许重新等高线来处理RT期间发生的变化。这种变化可能会导致对肿瘤的剂量不足和对正常周围器官的过量。2015年，美国估计将出现1658370例新的癌症病例，其中近三分之二将接受RT治疗。鉴于美国有2100多个RT中心，生产自动轮廓绘制软件系统有很大的商业机会。与当前的临床实践相比，预期的临床结果是显著提高轮廓绘制的速度、吞吐量和准确性，并改善患者结果和节省成本。这个小型企业技术转移(STTR)第一阶段项目解决了癌症患者RT计划中与自动轮廓绘制相关的技术障碍。由于现有的轮廓绘制方法主要是针对特定形态的图像上的特定对象而开发的，因此出现了自动轮廓绘制的当前技术挑战。该项目将通过一种新的自动解剖识别方法克服这些障碍，该方法将使用从患者群体中获得的解剖模型，通过将所有主要对象包括在身体区域中。这些模型将编码丰富的对象解剖关系，并将利用这些信息来自动定位和轮廓任何给定患者图像中的对象。该项目将有两个目标。目的1开发在CT和PET/CT图像上绘制主要头颈部器官轮廓的方法和原型软件。模型将从已经存在的200名癌症患者的图像和轮廓数据中建立。Aim 1的结果将是经过技术验证的原型软件，与地面事实相比，在1像素边界距离内准确，每项研究需要3分钟或更少时间。目的2是对该软件在头颈部恶性肿瘤患者放疗计划中的初步临床评估。