Following the American Society of Neuroradiology (ASNR) meeting, MedPage Today convened three leaders in neuro-oncology and brain tumor imaging for a virtual roundtable discussion on the evolving role of advanced MRI in brain tumor care. Moderator Suyash Mohan, MD, of the University of Pennsylvania in Philadelphia, is joined by Caroline Chung, MD, of the University of Texas MD Anderson Cancer Center in Houston, and Steven Brem, MD, also of the University of Pennsylvania.
In this second of four episodes, the panel explores whether advanced MRI and quantitative imaging can help move glioblastoma radiation planning beyond conventional anatomy-based margins. The discussion examines the need for standardized imaging biomarkers, the potential for biologically-guided dose escalation, and early findings from a machine learning-guided precision radiotherapy approach.
You can view the first video here.
Following is a transcript of their remarks:
Mohan: So let me ask you my second question, that we talked about heterogeneity of glioblastoma, but many of our treatment frameworks, especially in radiation oncology, are still built around anatomy-based target volumes and margins. Current guidelines-based target delineation still starts from postoperative contrast-enhancing residual tumor, surrounding FLAIR [fluid-attenuated inversion recovery] with standardized margins, which I agree is practical, is easily implementable across multiple institutes and enterprises, but obviously this is not the same as being biologically personalized. The uncomfortable question here is that how do we get beyond a one-size-fits-all approach? Dr. Chung?
