The purpose of this project is to identify imaging-based biomarkers that predict whether an incidentally discovered adrenal mass has a high likelihood of being malignant, thus directing surgical intervention.
Body composition analysis
Most CT and MR examinations of the abdomen are performed to assess the condition of abdominal organs. However, there is increasing awareness that the composition of one’s body has significant value as a predictor of health and the ability to withstand significant stresses like major surgery. In this project, images of the abdomen are used to compute specific markers of body composition: intra- and extra-abdominal fat, and the thickness of the abdominal musculature. We then assess the value of these as predictors of health and survival. We are also examining related biomarkers for their value as health predictors.
Thyroid ultrasound evaluation of nodules
Thyroid nodules are common findings in the population. Differentiating benign from malignant nodules can be a challenge; in addition, not all malignant nodules need to be aggressively treated. This project attempts to identify properties of nodules as imaged by ultrasound for their ability to predict aggressive versus non-aggressive nature.
Cardiac amyloid involvement
This project uses machine learning methods applied to cardiac ultrasound imaging (echocardiography) to determine properties of the images that predict which subjects with involvement of other body parts with amyloid will develop cardiac involvement, even when there is not visible evidence of cardiac involvement.
Glioblastoma progression and pseudoprogression biomarkers
This project develops a biomarker using early magnetic resonance imaging (MRI) to determine if early enhancement is due to tumor progression or treatment effects that mimic tumor progression. An accurate determination of true versus pseudoprogression is critical — effective therapy should continue during treatment, but if the tumor is progressing, second line agents can be beneficial.
Brain tumor change detection
This project focuses on changes in any pair of brain MRIs, allowing for better detection of early tumor progression. An accurate detection of progression permits early institution of therapy, which can improve outcome. According to Dr. Erickson, this algorithm is used routinely in practice and when combined with proper display methods, can improve accuracy.
Oligodendroglioma therapy response prediction
This project develops a biomarker using early MRI to predict the most effective way to treat low-grade gliomas such as oligodendroglioma. It is known that there are some visual patterns that tend to correlate with chromosomal abnormalities (for example, 1p19q chromosome deletion), which in turn, predict responsiveness to temozolomide and survival. The lab is in the early development stages of MRI-based tools to better predict chromosomal patterns and therapy responsiveness.
This project is part of a multisite National Institutes of Health contract to develop software that allows patients to get images from their health care facility and store them in a personal health record. From there, patients can send a link allowing any physician to view them, or the records can be sent to another hospital. Dr. Erickson and his team hope to demonstrate reduced duplication of imaging exams and more timely access to imaging.
Polycystic kidney disease biomarkers
This project develops efficient, clinically viable tools that allow estimation of prognosis and therapy planning. The efficient characterization of disease can allow for better assessment of disease state and measurement of the benefits of possible therapies. This is in the development stage.
Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER)
This project quantifies and displays lung disease based on computerized tomography scans and allows objective and early reproducible assessment of lung disease at a single time point and over time. It is currently in the clinical evaluation stage.
Dicom-Enabled Workflow Engine System (DEWEY)
This project develops an infrastructure that enables efficient, reliable and flexible implementation of human and computer analytic tools into imaging departments. Workflows to support modern, multisite imaging departments are complex, and relying on humans to execute this workflow is inefficient and unreliable. Using workflow engines to supplement humans improves efficiency, reliability and quality of imaging departments.
Platform to Enable Sharing of Scientific Computing and Research Assets (PESSCARA)
PESSCARA is a novel infrastructure that enables groups of people to easily share data, metadata, algorithms and results. As such, it promotes team science and reproducible science. The team uses it for most of the research conducted in the lab.
The purpose of this project is to highlight regions that are suspicious for intracranial aneurysm, which has an incidence of about 1 to 2 percent in the general population. A number of magnetic resonance angiography studies of the brain have been done for other purposes, but detection of unsuspected aneurysms is reported to be as low as 60 percent. Aneurysms that rupture cause death in about one-third of patients and significant morbidity in one-third of patients. Detection of an aneurysm that ruptures can allow for treatment. This disease frequently affects young people, so the economic and social impact is large. Researchers at the lab have developed an algorithm that has shown high sensitivity in aneurysm detection in the laboratory setting (more than 90 percent accuracy). The lab is now doing a clinical trial of its value.