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

通过 FDG-PET/CT 自动测量淋巴瘤全身疾病和分期疾病的模型和方法

• 批准号: 10296059
• 负责人: STEPHEN J SCHUSTER
• 金额: $ 64.74万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-12 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10296059
• 关键词: 3-Dimensional; Abdomen; Adult; Affect; Age; Aging; Agreement; Anatomic Models; Anatomy; Automation; Body Burden; Body Regions; Chest; Clinical; Computer software; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Disease; Engineering; Family; Female; Gender; Geography; Goals; Image; Image Analysis; Individual; Institution; Knowledge; Lesion; Lymphoma; Manuals; Measurement; Measures; Medical Imaging; Medicine; Methodology; Methods; Modeling; Monitor; Names; Neck; New York; Normalcy; Organ; Outcome; PET/CT scan; Pathologic; Pathology; Patients; Pelvis; Performance; Population; Positron-Emission Tomography; Process; Prognosis; Protons; Radiology Specialty; Reproducibility; Sampling; Scanning; Specificity; Staging; Standardization; Systemic disease; Techniques; Testing; Therapeutic Intervention; Time; Training; X-Ray Computed Tomography; age group; base; burden of illness; deep learning; design; fluorodeoxyglucose positron emission tomography; imaging Segmentation; imaging modality; improved; innovation; large cell Diffuse non-Hodgkin' s lymphoma; learning strategy; lymph nodes; male; object recognition; outcome prediction; predict clinical outcome; predicting response; prediction algorithm; quantitative imaging; response; task analysis; theories; virtual

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.

定量放射学有很大的希望改变我们的诊断，监测，分期，精确， 并检测疾病以及计划和指导患者的治疗干预。但是，过程的 在医学图像中定位和描绘解剖器官和病理区域，称为图像 高度自动化的细分仍然是这些进步的主要障碍。最 图像分割的发展集中在特定器官或一小群对象上， 具体身体部位。一个新的方法或现有方法的主要调整是在以下情况下设计的： 这些参数改变了。这样的做法是不可持续的，成为处理时的绊脚石 需要全身图像分析的全身系统性疾病。需要一个关键的进步 在该领域中，克服两个主要挑战：（1）尽管认为关于正常解剖结构的先验信息是不可靠的， 对于图像分割和分析至关重要，其大规模的创建和使用还没有 已经尝试过了，非常缺乏。(2)使用这些信息和方法进行全身性检查的技术 不存在高度自动化的疾病量化。总体目标 是克服这些 通过开发全身范围和可推广的解剖学引导的深度学习图像分割 方法 并证明其在弥漫性大B细胞淋巴瘤患者研究中的应用 对于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。目标1：开发一系列全身解剖模型， 整个人类成年年龄范围。 现有的FDG PET/CT扫描的600名患者来自两个机构（宾夕法尼亚大学和 纽约质子中心），覆盖10个年龄组，将用于建立涉及50个器官的解剖模型 以及包括颈部、胸部、腹部和骨盆在内的延伸身体躯干中的50个淋巴结区域。 口之家 将从大约60，000个3D中创建代表4个身体区域和10个年龄组的40个解剖模型 对象样本。目标2：开发、实现和验证用于本地化对象的方法并 量化疾病，而不明确划定器官和病变。性别和年龄特异性解剖模型 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， 量化每个身体区域、器官和淋巴结区域的疾病。该方法将在400台PET/CT上进行测试 DLBCL患者的图像。目的3：开发和验证DLBCL疾病分期的自动化方法 和预后。疾病数量信息将用于开发自动分期和结局 预测算法将在上述400例病例中进行测试，并与当前的临床方法进行比较。 该项目的两个关键成果将是：一个前所未有的精心策划的全身图像数据库， 分段对象和模型家族;以及自动全身疾病的有效方法 DLBCL的定量和疾病分期。