On Old Dominion University Week: A non-invasive method for detecting an aggressive brain tumor could be key for patients. But how does it work?
Khan Iftekharuddin, Professor and Eminent Scholar, delves into this.
Faculty Bio:
Dr. Khan Iftekharuddin is a professor and Batten Endowed Chair in Machine Learning in the department of Electrical and Computer Engineering (ECE) at Old Dominion University (ODU). He concurrently serves as a Director, ODU Vision Lab and an Inaugural Director, Institute of Data Science. Dr. Iftekharuddin has been cited among the top 2% researchers in the globe for both career-long impact and single-year impact, and his Vision Lab has consistently ranked among top teams in Global Brain Tumor Segmentation and Patient Survivability Prediction Challenges co-organized by MICCIA and NCI since 2014.
Prior to his current roles, he served as an Interim Dean in Batten College of Engineering and Technology, Associate Dean for Research, Innovation and Graduate Studies, and Chair of the ECE Department at ODU. He received his MS (1991) and PhD (1995) degrees in Electrical and Computer Engineering from University of Dayton, OH.
Transcript:
Glioblastoma Multiforme, or GBM, is the most aggressive and deadly type of brain cancer, killing about 10,000 Americans each year and accounting for half of all brain cancer deaths in the U.S. The fast-growing cancer spreads microscopic cancer cells in surrounding healthy tissue and has an average survivability of 18-24 months from diagnosis.
Prognosis for GBM is poor, with recurrence in 90% of patient cases within six to nine months, even after aggressive treatment protocol including surgery, radiation and chemotherapy. Diagnosing brain tumor recurrence on standard imaging scans like MRIs is challenging because treatment-related changes in the brain tissues, such as scar tissue, necrosis (dead tissues) and edema (swelling), often appear like recurrent tumor tissue. Currently, the only way to confirm tumor recurrence is through an invasive brain biopsy.
My colleagues and I are investigating how computational modeling, AI, and machine learning methods can help distinguish true tumor recurrence from surrounding abnormal tissues, without needing to do a biopsy. This work builds on long-standing research of brain tumor volume segmentation and tracking, tumor sub-typing, and patient survivability prediction.
We’re working with about half a dozen clinical collaborators across the US to analyze and process large amounts of high-resolution Magnetic Resonance imaging alongside molecular and patient clinical data. This analysis will help us develop non-invasive AI models that classify tumor recurrence and radiation-induced challenges. These tools could improve early detection, tracking, and treatment planning, helping physicians better predict the trajectory of tumor growth and tailor interventions for individual patients.
Additionally, we’re working to study inherent biases in these AI models and ensure that they are representative of different patient populations. This will bolster their robustness and efficacy in clinical settings.
