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、分期和治疗的主要手段 评估。尽管它对癌症的精确医学至关重要，但目前还没有一种方法 可用于地区和地区的自动疾病负担估计和标准化的PET/CT图像报告 在一个身体区域内或整个身体的解剖器官和淋巴结区。自动化生产- 采用标准化报告的全身性/全身性疾病测量模式会导致癌症 研究发现并将引起肿瘤学家、放射学家/核医学内科医生的极大兴趣， 医疗保险和私人健康保险公司，以及进行新癌症临床试验的制药公司 治疗和目前依赖于人工方法的反应评估。这件事的首要目标是 因此，第一阶段的项目是开发、验证和演示疾病的原型软件 通过FDG-PET/CT以上述方式在一个身体区域(即胸部)进行测量和报告，基于 可推广到全身的创新算法。该项目有两个目标：目标1：开发、实施、 并验证了FDG-PET/CT用于胸癌疾病负担评估的算法。目标2：发展和 演示了一个实现上述疾病测量和报告算法的原型软件。 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 对于胸部区域，将从现有的100名患者的全身扫描中收集。在这些数据集中， 在专家的指导下，将勾画出胸部7个关键解剖器官和5个关键淋巴结区。 这些数据将被用来根据我们建立的自动解剖学建立种群模糊解剖模型 识别(AAR)方法。另外100例不同类型脑血管疾病患者的全身PET/CT扫描 癌症将被收集起来测试我们的方法。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内病变的淋巴特性，并 作为疾病负担的参考依据。任务3：解剖引导的深度学习(DL)算法 AAR将被开发成非常准确地定位(但不描绘)PET/CT中的器官和淋巴结区 使用模型的图像。任务4：将开发基于模糊原理的新方法来自动 标记和量化位于所定位的器官和结节区域内的病理性区域(而不明确描绘它们)， 并将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 将疾病测量方法整合到一个名为AAR-DQ的原型软件中(任务6、7) 基于我们以前的软件平台CAVASS。AAR-DQ将以分层方式报告疾病负担- (I)在身体区域一级；(Ii)在每个器官/淋巴结区一级；(Ii)在每个病变/淋巴结一级。 预期的里程碑。目标1：AAR-DQ疾病测量与临床的偏差不超过10% 真理测量。目标2：在PET/CT研究中，每个患者在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.