Models and methods for automatically measuring disease body-wide and staging disease via FDG-PET/CT in Lymphoma

Quantitative Radiology holds great promise to transform our ability to diagnose, monitor, stage, prognosticate, and detect diseases as well as to plan and guide patient therapeutic interventions. However, the process of 在医学图像（称为图像）中定位和描绘解剖器官和病理区域 segmentation, at a high level of automation has remained a major hurdle to these advances.大多数 图像分割的发展主要集中在特定器官或一小组对象上 specific body region. A new method or a major adaptation of an existing method is engineered when any of 这些参数发生了变化。这种方法是不可持续的，并且会成为交易时的绊脚石 需要全身图像分析的全身系统性疾病。需要取得关键进展 在这一领域克服两个主要挑战：（1）尽管有关正常解剖学的先验信息被认为是 对于图像分割和分析至关重要，但它的大规模创建和利用尚未在全身范围内实现。 已经尝试过并且非常缺乏。 (2) Techniques to employ such information and methods for body-wide disease quantification at high levels of automation do not exist.总体目标 is to overcome these 通过开发全身范围内的、可推广的解剖学引导的深度学习图像分割来应对挑战 方法论 并展示其在弥漫性大B细胞淋巴瘤患者研究中的应用 (DLBCL)，基于 PET 的分期和反应评估至关重要。 该项目有三个具体目标。 Aim1: To develop a family of body-wide anatomy models representing the 整个人类成年年龄范围。 Existing FDG PET/CT scans of 600 patients from two institutions (Penn and 纽约质子中心）将覆盖10个年龄组，建立涉及50个器官的解剖模型 以及延伸身体躯干的 50 个淋巴结区，包括颈部、胸部、腹部和骨盆。 A family of 将从约 60,000 个 3D 模型中创建代表 4 个身体区域和 10 个年龄段的 40 个解剖模型 object samples.目标 2：开发、实施和验证本地化对象的方法并 quantify disease without explicitly delineating organs and lesions.特定性别和年龄的解剖模型 将用于自动定位任何给定患者 PET/CT 图像中的上述 100 个对象，并 量化每个身体区域、器官和淋巴结区域的疾病。这些方法将在 400 PET/CT 上进行测试 DLBCL 患者的图像。目标 3：开发并验证 DLBCL 疾病分期的自动化方法 and prognosis.疾病数量信息将用于开发自动分期和结果 预测算法将在上述 400 个病例中进行测试，与当前的临床方法进行比较。 该项目的两个主要成果是：前所未有的精心策划的全身图像数据库， 分段对象和模型族；以及一种经过验证的自动全身疾病方法 DLBCL 的定量和疾病分期。

Quantitative Radiology holds great promise to transform our ability to diagnose, monitor, stage, prognosticate, and detect diseases as well as to plan and guide patient therapeutic interventions. However, the process of locating and delineating anatomic organs and pathologic regions in medical images, known as image segmentation, at a high level of automation has remained a major hurdle to these advances. Most developments on image segmentation have focused on a specific organ or a small group of objects in a specific body region. A new method or a major adaptation of an existing method is engineered when any of these parameters changed. Such an approach is not sustainable and becomes a stumbling block when dealing with whole-body systemic diseases where body-wide image analytics is required. A critical advance is needed in this field to overcome two main challenges: (1) Although prior information about normal anatomy is deemed vital for image segmentation and analysis, its creation and utilization body-wide on a massive scale have not been attempted and are sorely lacking. (2) Techniques to employ such information and methods for body-wide disease quantification at high levels of automation do not exist. The overarching goal is to overcome these challenges by developing a body-wide and generalizable anatomy-guided deep learning image segmentation methodology and demonstrate its application in the study of patients with diffuse large B cell lymphoma (DLBCL) for which PET-based staging and response assessment are of paramount importance. The project has three specific aims. Aim1: To develop a family of body-wide anatomy models representing the entire human adult age spectrum. Existing FDG PET/CT scans of 600 patients from two institutions (Penn and New York Proton Center) covering 10 age groups will be utilized to build anatomy models involving 50 organs and 50 lymph node zones in the extended body torso including neck, thorax, abdomen, and pelvis. A family of 40 anatomy models representing the 4 body regions and 10 age groups will be created from roughly 60,000 3D object samples. Aim2: To develop, implement, and validate a methodology for localizing objects and to quantify disease without explicitly delineating organs and lesions. Gender- and age-specific anatomy models will be utilized for automatically locating the above 100 objects in any given patient PET/CT image and to quantify disease in each body region, organ, and lymph node zone. The methods will be tested on 400 PET/CT images of DLBCL patients. Aim3: To develop and validate an automated method of DLBCL disease staging and prognosis. The disease quantity information will be utilized to develop automated staging and outcome prediction algorithms which will be tested on the above 400 cases in comparison to current clinical methods. Two key outcomes of this project will be: an unprecedented well-curated database of body-wide images, segmented objects, and family of models; and a validated methodology for automatic body-wide disease quantification and disease staging in DLBCL.