Neuro-oncology: Neuroimaging for Brain Tumors*
Date: October 20, 2026
Time: 11:00 am to 12:30 pm
Room: Coral 5
Track: Traditional Special Interest Group (SIG)
Session Description
The theme of this year’s Neuro-Oncology Special Interest Group session is Neuroimaging for Brain Tumors. Advances over the past decade have expanded neuroimaging beyond structural techniques to include physiologic and molecular approaches that provide insight into tumor biology. Modalities such as perfusion imaging and amino acid PET are enhancing diagnostic accuracy and enabling earlier, more targeted interventions.
In the context of emerging immunotherapies and targeted treatments, improved imaging may significantly impact clinical decision-making and patient outcomes. This session will highlight current and emerging imaging techniques, their correlation with tumor pathology, and the growing role of artificial intelligence in advancing neuro-oncologic imaging.
Learning Objectives
At the conclusion of this session, attendees will be able to:
- Describe key neuroimaging modalities used in the evaluation of brain tumors.
- Explain the relationship between imaging findings and tumor pathology.
- Discuss the role of artificial intelligence in enhancing neuroimaging interpretation.
Speakers
- (Chair) Eric Wong, MD, MA, FAAN, FANA
- (Co-Chair) Adilia Hormigo, MD, PhD, FANA
- (Speaker) Peter LaViolette, PhD
- (Speaker) Kambiz Nael, MD
- (Speaker) Zhicheng Jiao, PhD
Radio-pathomic Mapping of Glioblastoma: Detecting Invisible Infiltrative Tumor
Description
In this presentation, Dr. LaViolette will focus on detecting invisible infiltrative tumor using radio-pathomic mapping techniques which leverage AI and rad-path correlation.
Advanced MRI for Brain Tumor Characterization: A Case-based Practical Approach
Description
In this presentation, Dr. Nael will provide a case-based presentation on advanced MRI (perfusion, diffusion) for brain tumors, highlighting practical tips to improve diagnosis.
Evolving Brain Tumor MRI AI: From Conventional Deep Imaging Models to Multimodal Foundation-Model Fusion
Description
In this presentation, Dr. Jiao will provide an overview of how brain tumor MRI AI is evolving from image-based models to multimodal foundation models for diagnosis, prognosis, and treatment.