SUMMARY
Leland S. Hu, M.D., is a leader in neuro-oncological imaging. Dr. Hu's research focuses on the development, validation and clinical integration of advanced MRI biomarkers to characterize intratumoral heterogeneity, invasion and treatment response in human brain tumors, specifically glioblastomas.
Dr. Hu leads multidisciplinary imaging-driven research programs that integrate quantitative and physiological MRI, particularly dynamic susceptibility contrast perfusion imaging, with image-localized biopsies and spatially matched molecular profiling. His laboratory applies computational and biophysical modeling to link regional imaging phenotypes with tumor biology and microenvironmental states, enabling image-guided assessment of tumor evolution and therapeutic resistance.
Dr. Hu served as principal investigator on multiple National Institutes of Health-funded projects. His projects include two consecutive R01s that aim to establish and validate a consensus dynamic susceptibility contrast MRI protocol for diagnosis and response assessment in high-grade glioma. And they include two concurrent U01s focused on modeling regional imaging-molecular heterogeneity within the invasive glioblastoma microenvironment. These efforts have directly informed multisite clinical trials and standardized imaging workflows in neuro-oncology.
Dr. Hu has held leadership roles in developing imaging guidelines and response assessment frameworks in neuro-oncology for multiple national research organizations. He also is committed to training the next generation of physician-scientists.
Focus areas
- Predictive mapping of regional molecular heterogeneity and genomic drivers in glioblastoma. Intratumoral heterogeneity confounds the accuracy and biological relevance of conventional nonlocalizing approaches to artificial intelligence (AI) and machine learning (ML)-based radiogenomics. Dr. Hu's team pioneered an image-tissue sampling framework that spatially aligns MRI phenotypic biomarkers with genomic and transcriptomic profiles. The approach uses regional image-localized surgical biopsies to optimize the predictive performance of AI-ML models. His team identified intratumoral genetic diversity and imaging signatures that predict clonal subtypes, informing models of tumor evolution and recurrence. Dr. Hu's work has thus far provided the foundation for four U.S. patents. His research continues to identify imaging biomarkers and develop predictive models of key genomic drivers and molecular signaling pathways that are identified as potential therapeutic targets.
- Computational modeling of glioblastoma's invasive tumor microenvironment. As glioblastoma tumor cells invade surrounding brain tissue, they interact with resident nontumor cells such as neurons, immune and myeloid populations. The tumor cells create regional microenvironmental states that drive tumor invasion, therapeutic resistance and eventual recurrence. Regions of glioblastoma invasion have remained poorly understood because they are not typically biopsied or resected.
Dr. Hu's team has developed and applied computational frameworks — for example, graph-based frameworks — to study a unique cohort of image-localized biopsies from regions of glioblastoma invasion. His team has identified predominant neuronal- and immune-states of tumor invasion. The team also has found unique biophysical signatures that distinguish these states using advanced MRI, including dynamic susceptibility contrast. Dr. Hu's lab is now developing clinical predictive pipelines, such as with graph convolutional networks, to guide individualized therapeutic targeting for improved patient outcomes.
- Consensus guidelines for response assessment in high-grade glioma. Conventional contrast-enhanced MRI cannot distinguish tumor recurrence from nontumoral posttreatment radiation effects, such as pseudoprogression and radiation necrosis. This shortcoming has limited its use as the benchmark for clinical response assessment in high-grade glioma. Leveraging image-tissue sampling frameworks, Dr. Hu has led multisite collaborations to develop and validate consensus dynamic susceptibility contrast MRI thresholds. These thresholds have dramatically improved diagnostic accuracy in distinguishing tumors from posttreatment radiation effects. Current lab efforts include the development of consensus dynamic susceptibility contrast MRI criteria to:
- Identify molecular markers of tumor aggressiveness.
- Quantify recurrent tumor burden.
- Guide integrated decision-making within clinical trial design.
Significance to patient care
Dr. Hu's research helps medical teams use imaging tools to diagnose brain tumors and guide treatment decisions. His work shows how far invasive brain tumors, such as glioblastoma, have spread within the brain and how the tumor cells behave. These tools can help guide more-accurate treatments, such as surgery and radiation therapy, and potentially avoid unnecessary procedures by showing whether a tumor has truly come back or if the changes are side effects of treatment.
Dr. Hu's overall goal is to improve personalized care and clinical outcomes for patients by better guiding surgical biopsy and resection, radiation targeting, and decisions about treatment response and medicine.
Professional highlights
- Program director, M.D.-Ph.D. Program, Mayo Clinic College of Medicine and Science, Mayo Clinic, 2024-present.
- Co-chair, Neuroimaging, Phase II and III randomized trial of Veliparib or Placebo in Combination with Adjuvant Temozolomide in Newly Diagnosed Glioblastoma with MGMT Promoter Hypermethylation, A071102, Alliance for Clinical Trials in Oncology, 2012-present.
- Co-chair, Neuroimaging Subject Matter Track, Society of Neuro-Oncology, 2026-2028.
- Co-chair, Dynamic Susceptibility Contrast-Magnetic Resonance Imaging Biomarker Committee, Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, 2022-2025.
- Robert D. Zimmerman Award, Eastern Neuroradiological Society, 2021.
- Lucien Levy Best Research Article Award, American Journal of Neuroradiology, 2019.
- Chair, Neuroimaging Symposium, International Primary Central Nervous System Lymphoma Collaborative Group, 2017.
- Gabriel H. Wilson Award, Western Neuroradiological Society, 2007.