Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders

点击翻译按钮获取中文摘要

Whole-body computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly used in staging of monoclonal plasma cell disorders and for evaluating treatment response. PET imaging provides additional means for diagnosing multiple myeloma patients, and is also acquired jointly with CT or MRI scans. All these imaging modalities provide localized information about the state of the disease in addition to an estimate of the global tumor burden, and one might expect that evaluating lesions at the local level may be more sensitive to changes than global systemic reactions diagnosed from blood samples. Still, a general difficulty in using whole-body MRI, CT, and PET for diagnosing and staging the disease is the amount of image information to be processed. Data sets are not easily readable even for trained radiologist and to date only basic information is extracted. Moreover, thoroughly reading full 3D volumes is a very tedious and time consuming task that is is prone to oversights and systematic errors when only a subset of the source images are analyzed due to time constraints. This problem exponentiates in clinical studies when hundreds of multimodal whole-body scans have to be evaluated, and general patterns of disease progression have to be abstracted from the data.To this end we will develop computational tools that will enable clinicians to quantify multiple myeloma status and to inter-actively track progression over time in whole-body image data sets. To access the full information of large multimodal whole-body image series, we will solve the problem of spatial correspondence in different modalities and time, to be used for identifying and re-identifying locations in whole-body data, and map lesions. Moreover, we will develop probabilistic and bio-mathematical models whose parameters can serve as new imaging biomarkers, and can be correlated with clinical, molecular, and genetic evidence to confirm and improve the diagnostic value of the available image information. Finally, we will model disease progression over time and across a large cohort of patients, systematically evaluating large data bases available from clinical diagnostics, which will help to better understand the observed disease progression patterns and to validate the tools and models to be developed in this project.