权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10689731
负责人：
STEPHEN J SCHUSTER
金额：
$ 54.77万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10689731
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 burden of illness deep learning design fluorodeoxyglucose positron emission tomography imaging Segmentation imaging modality improved innovation large cell Diffuse non-Hodgkin&apos s lymphoma learning strategy lymph nodes male object recognition outcome prediction predict clinical outcome predicting response prediction algorithm prognostication quantitative imaging response task analysis theories virtual

项目摘要

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.

定量放射学有望改变我们的诊断、监测、分期、预测、