At the International Symposium on Pediatric Neuro-Oncology, experts discussed how advanced MRI may enable more personalized brain tumor radiotherapy for children with brain tumors by identifying meaningful changes during treatment.

In this MedPage Today video, Caroline Chung, MD, of the University of Texas MD Anderson Cancer Center in Houston, discusses how advanced MRI could help shift radiotherapy from a reactive to a predictive approach.

Following is a transcript of her remarks:

When I first came and I got recruited to MD Anderson, it was actually to start up the MR program in radiotherapy. And so it's still an evolving adoption of leveraging multiparametric imaging and multiparametric MR into radiation planning. It's grown immensely over the last decade, but it is still an emerging field.

Many centers are still heavily relying on CT scans. And you can imagine in brain tumors -- now I look at a CT scan and I wondered how I was even doing radiation treatment planning without the MR, let alone leveraging the tools that multiparametric MR can actually bring. And there are many new pulse sequences that we're incorporating into radiation planning. As you know, with integrated MR linear accelerator devices, we now have the capability of using online MR guidance for therapy, looking at changes on a daily basis through the treatment.

We have a prospective clinical trial that I am the PI [principal investigator]. We're looking at weekly changes on the MR in patients with glioblastoma during the course of radiotherapy. And it was surprising, but not if you take a step back, and thinking that an aggressive tumor would actually sit completely idle through the entire course of radiotherapy in every single patient seems pretty unlikely.

When I had conversations with my own patients, this is where it got motivated. The very first funds to actually start this trial came from a donor who actually said, "This makes absolutely no sense. We need to look and if you don't look, you'll never see." And so we started to see changes in subsets of patients that were quite dramatic in that we allowed the radiation oncologist to adapt the radiotherapy plan partway through the treatment to make sure that we were covering the entire area that we were concerned about.

Having said that, I would say that we're still at the mercy of looking at the image as an image as opposed to what you were alluding to of what other quantitative metrics that are biologically meaningful could we actually tease out of this image? And this is where the really exciting parts start to emerge, is that can we actually see changes in white matter disruption that show microscopic infiltration of that white matter? Can we confirm biologically that this is what's happening? And there are prospective trials that are ongoing and that we're pursuing ourselves in terms of getting more meaningful imaging path correlates to say, when we see this change in this voxel, this is what it could biologically mean.

We've partnered with mathematical oncologists and computational biologists to start to design mathematical models that can actually anticipate what that behavior is, not only from an AI [artificial intelligence] data-driven method, but also applying the known physical features of tumor growth, the constraints around pressures in the tissues that we could actually potentially even measure with radiologic quantitative imaging biomarkers.

And so integrating all of this together, we can start to anticipate that we don't need to just react once the imaging on radiation has changed. A lot of medicine today still remains very reactive. We treat the patient until we see a progression and then we say, oh, we need to change course. What if we could actually base our decisions on predictions and anticipate and have that predictive adaptive capability to say, we're going to do this in advance of gross tumor progression once the patient's already had even symptoms develop? Reversing many of those symptoms can be quite challenging and sometimes are irreversible.

And so it's really changing the paradigm of medicine overall from a reactive perspective to a predictive piece.

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