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

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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.