Development of a prototype software for automated PET/CT interpretation and reporting in thoracic cancer

抽象的。在癌症中，全身FDG-PET/CT是诊断、分期和治疗的主要方式 考核尽管它对癌症的精确医学至关重要，但目前没有任何方法 可用于区域性PET/CT图像的自动疾病负担估计和标准化报告， 在身体区域内或全身范围内的解剖器官和淋巴结区域中全局地存在。自动化生产- 模式全身/全身区域疾病测量与标准化报告将促进癌症 研究发现，并将极大的兴趣，肿瘤学家，放射科医生/核医学医生， 医疗保险和私人健康保险公司，以及对新癌症进行临床试验的制药公司 治疗，目前依赖于反应评估的手动方法。这个项目的首要目标是 因此，第一阶段项目是开发、验证和演示一个用于疾病诊断的原型软件。 - 在一个身体区域（即胸部）中以上述方式经由FDG-PET/CT进行测量和报告， 创新的算法，是普遍适用的全身。该项目有两个目标：目标1：制定、实施、 并通过FDG-PET/CT验证胸部癌症疾病负担估计的算法。目标2：发展和 演示一个原型软件，实现上述疾病测量和报告算法。 目标1将分3个阶段完成：任务1、2：放射学上接近正常的PET/CT图像数据集 将从100名患者的现有全身扫描中收集胸部身体区域的数据。在这些数据集中， 将在专家指导下勾画胸部7个关键解剖器官和5个关键淋巴结区。 这些数据将用于建立人口模糊解剖模型后，我们建立了自动解剖 识别（AAR）方法。另外100例不同类型的患者的全身PET/CT扫描 癌症将被收集来测试我们的方法。使用可用的商业临床软件，PET摄取 将手动测量器官中的病变和淋巴结区域中的患病淋巴结的性质， 用作疾病负担的参考基础事实。任务3：解剖学引导的深度学习（DL）算法 通过AAR将被开发用于在PET/CT中非常准确地定位（但不描绘）器官和淋巴结区域 使用模型的图像。任务4：基于模糊原理的新方法将被开发，以自动 标记和量化所定位器官和淋巴结区域内的病理区域（而不明确地描绘它们）， 并评估疾病测量的准确性（任务5）。目标2将通过以下方式实现： 将疾病测量方法纳入名为AAR-DQ的原型软件（任务6，7） 基于我们早期的软件平台CAVASS。AAR-DQ将以分层方式报告疾病负担- (i)在身体区域水平;（ii）在每个器官/淋巴结区水平;（ii）在每个病变/淋巴结水平。 预期的里程碑。目标1：AAR-DQ疾病测量与临床基础的偏差不超过10% 真理测量目标2：在每位患者PET/CT研究的5分钟内进行疾病测量/报告。

Abstract. In cancer, body-wide FDG-PET/CT is a prime modality for diagnosis, staging, and treatment assessment. Despite its paramount importance to enable precision medicine in cancer, no method is currently available for automated disease burden estimation and standardized reporting on PET/CT images regionally and globally in anatomic organs and lymph node zones within a body region or body-wide. Automated production- mode body-wide/ body-region-wide disease measurement with standardized reporting will foster cancer research discovery and will be of great interest to oncologists, radiologists/ nuclear medicine physicians, Medicare and private health insurers, and pharmaceutical companies that conduct clinical trials of new cancer therapeutics and currently rely on manual methods of response assessment. The overarching goal of this Phase I project is, therefore, to develop, validate, and demonstrate a prototype software for disease measurement and reporting via FDG-PET/CT in the above manner in one body region, namely thorax, based on innovative algorithms that are generalizable body-wide. The project has two aims: Aim 1: Develop, implement, and validate algorithms for disease burden estimation in thoracic cancer via FDG-PET/CT. Aim 2: Develop and demonstrate a prototype software implementing the above algorithms for disease measurement and reporting. Aim 1 will be accomplished in 3 stages: Tasks 1, 2: PET/CT image data sets which are radiologically near normal for the thoracic body region will be gathered from existing whole-body scans of 100 patients. In these data sets, 7 key anatomic organs and 5 key lymph node zones in the thorax will be delineated under expert guidance. These data will be used to build population fuzzy anatomy models following our established Automatic Anatomy Recognition (AAR) methodology. An additional 100 whole-body PET/CT scans of patients with different types of cancer will be gathered to test our methods. Using available commercial clinical software, the PET uptake properties of lesions in organs and diseased lymph nodes in lymph node zones will be measured manually and used as reference ground truth of disease burden. Task 3: Deep learning (DL) algorithms anatomically guided by AAR will be developed to very accurately localize (but not delineate) organs and lymph node zones in PET/CT images using the models. Task 4: Novel methods based on fuzzy principles will be developed to automatically tag and quantify pathological regions (without explicitly delineating them) within located organs and nodal zones, and the accuracy of disease measurement will be evaluated (Task 5). Aim 2 will be accomplished by incorporating the disease measurement methodology into a prototype software named AAR-DQ (Tasks 6, 7) based on our earlier software platform CAVASS. AAR-DQ will report disease burden in a hierarchical manner – (i) at the body-region level; (ii) at each organ/ lymph node zone level; (ii) at each lesion/ lymph node level. Expected milestones. Aim 1: AAR-DQ disease measurement not to deviate more than 10% from clinical ground truth measurement. Aim 2: Disease measurement/ reporting in under 5 minutes per patient PET/CT study.