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We design and validate advanced imaging biomarkers that assess cellular and physiologic heterogeneity of tumors, metabolism and the immune- and micro-environments. The pathway from biomarker discovery to clinical translation includes a range of scientific methodologies including computational and biophysical modeling of MRI contrast mechanisms, validation in rodent and patient-derived models of primary and metastatic brain tumors (using technologies like light sheet or confocal microscopy, immunofluorescence, histology, spatial transcriptomics, etc.), MRI pulse sequence development, radiotracer synthesis, first-in-human studies, clinical validation (via image-guided tissue analysis and verifying sensitivity and specificity to disease mechanisms) and establishing analysis algorithms suitable for clinical studies. We are currently developing MRI and PET imaging biomarkers targeting:

• Vascular and hemodynamic features
• Cellular composition, architecture and heterogeneity
• Neuroinflammation
• Neurofluids (blood-brain barrier integrity, glymphatic system, cerebrospinal fluid flow)
• Invading brain tumor cells
• Interactions between neurons and cancer cells

Our research integrates imaging biomarkers with quantitative image analysis techniques to establish tools for predicting tumor progression, early therapy response and recurrence risk. These predictive tools will empower clinicians to make proactive, data-driven treatment decisions and enable treatment strategies tailored for individual patients. We are currently developing the following tools:

• Early detection of glioblastoma (GBM) response to therapy using multi-contrast and multiscale dynamic susceptibility contrast techniques
• Predicting patient-level recurrence risk and spatiotemporal GBM progression using the integration of serial physiologic imaging and deep learning analytics
• Mathematical tumor forecasting models to more accurately and efficiently differentiate progressive lesions from radiation necrosis
• Preclinical validation of mathematical tumor forecasting models for patient-derived GBM xenografts via spatially registered comparisons of light sheet microscopic images and MRI-based predictions of whole brain tumor burden
• Predicting directional glioma growth using deep learning analysis of MRI images sensitive to microstructural disruptions and cancer and neuron interactions

We develop and apply advanced neuroimaging tools to investigate and monitor how cancer and cancer therapies impact brain function and structure, particularly in relation to neurotoxicity. Our current research focuses on:

• Identifying multi-parametric neuroimaging biomarkers of chemotherapy-related neurotoxicity in breast cancer using resting-state functional MRI (fMRI), structural MRI and diffusion-weighted imaging
• Examining the long-term effects of head and neck cancer treatments on brain aging and psychobehavioral health
• Leveraging machine learning in structural and diffusion MRI to better understand the cognitive and psychosocial dimensions of Neurofibromatosis Type 1 (NF1)
• Investigating how different therapy arms affect brain function, structure, resting-state cognition and sleep symptoms in patients with advanced cancer and sleep disturbances

Conventional image-guided therapies in brain cancer primarily rely on anatomic imaging methods that are insensitive to a tumor¡¯s underlying biological or cellular status. We are leveraging imaging-based biomarkers and data science-driven analytics to establish bioimaging-guided therapeutics. These innovations improve tumor targeting accuracy and reduce treatment-related side effects. We are currently investigating:

• MRI-guided focused ultrasound (MRgFUS)-enhanced radiotheranostics
• Bioimaging-guided radiotherapy using metabolic and molecularly targeted PET radioligands
• Imaging localized stem cell delivery of oncolytic viruses

A critical aspect of our work is developing standardized imaging methodologies to ensure accuracy and reproducibility across multi-center clinical trials. Our recent studies primarily focus on establishing accurate dynamic susceptibility contrast (DSC) acquisition and analysis protocols and guidelines for clinical use. We are currently establishing:

• Anthropomorphic DSC-MRI phantoms for benchmarking clinical algorithms and software
• Harmonized multi-vendor pulse sequences for spin and gradient echo (SAGE) based DSC-MRI
• Automated analysis solutions for clinical SAGE applications
• Optimal acquisition and analysis techniques for accurate DSC-MRI parameter quantification across field strengths and brain tumor types