Image-based Biomarkers in Brain Tumors
Improving the biophysical characterization of brain tumors remains a highly relevant clinical objective. Currently, conventional anatomic magnetic resonance imaging (MRI) methods are used to guide almost all aspects of brain tumor clinical management, including surgical biopsy and resection, the planning of radiation treatment, and posttreatment surveillance for response assessment.
Unfortunately, accuracy of conventional MRI remains limited, which creates significant clinical challenges. For instance, contrast-enhanced MRI (CE-MRI) provides the primary means to detect tumor recurrence and guide treatment modification. However, CE-MRI fails to distinguish tumor regrowth from nontumoral posttreatment effects in up to half of cases following standard therapy. Thus, clinical decisions require surgical biopsy for definitive diagnosis, which increases medical costs, patient morbidity and mortality, and resource utilization.
We believe that MRI-based biomarkers can overcome these limitations and could positively impact radiographic diagnosis, response assessment, and image-guided biopsies. To that end, our research focuses on the development and validation of several image-based biomarkers, including dynamic susceptibility contrast (DSC) MRI, dynamic contrast enhanced (DCE) MRI, diffusion weighted imaging (DWI), chemical exchange saturation transfer (CEST), and positron emission tomography (PET) imaging of tumor hypoxia, glutamine metabolism, cellular proliferation, and inflammation.
Digital Reference Objects
To facilitate multi-institutional comparability and consistency, several national initiatives (NCI’s Quantitative Imaging Network (QIN), RSNA’s Quantitative Imaging Biomarkers Alliance (QIBA), and the National Brain Tumor Society) are underway to standardize acquisition and analysis protocols for several advanced MRI techniques.
A challenge to such efforts is the relative paucity of data that systematically evaluate the influence of acquisition and analysis methodology on parameter accuracy. Often times, validation studies are not feasible in patients due to the lack of a noninvasive, gold-standard measure for reference. As an alternative to in vivo validation, in silico digital reference objects (DRO)s provide a means to compute synthetic MRI signals and derived kinetic parameters for a range of clinically relevant input conditions.
We are developing brain tumor DROs that yield MRI signals across a range of contrast mechanisms, recapitulating the heterogeneity and characteristics of signals measured in human disease. The synthetic signals are also intentionally designed to reflect the underlying biophysics of the contrast mechanism of interest.
MRI Cytography
Each organ in the human body is composed of specialized and uniquely shaped cells that serve specific biological roles. Cell volume regulatory mechanisms actively influence physiological systems and functions in an attempt to maintain homeostasis. Changes in cell morphology are known to alter:
- Metabolism
- Intracellular signaling pathways
- Cell cycle progression
- Nutrient uptake
- Gene expression
Furthermore, cellular atrophy and hypertrophy are frequently observed as cells respond to a variety of exogenous stressors or pathological conditions.
The elucidation of the biological basis of these cytoarchitectural-dependent regulatory mechanisms continues to be a major thrust in biomedical research.
We recently developed a novel strategy called MR cytography that is directly sensitive to cytoarchitecture at the subvoxel level. This approach could be highly useful across a range of clinical conditions, including:
- Cancer
- Muscular dystrophy,
- Amyotrophic lateral sclerosis (ALS)
- Hypertrophic cardiomyopathies
We are validating MR cytography in brain tumor patients and preclinical models of cancer, muscular dystrophy, and ALS.
