 Okay. Well, I wanted to thank the organizing committee for giving me the opportunity to present some of the work that me and a large number of colleagues have been working at at Emory and a number of other sites across the country. So a lot of the talks that have been here have been very genomics focused, and what our group is particularly interested in is sort of an imaging-based view of cancer. So one of my colleagues, Dr. Cooper, presented some of his work with glioblastomas and pathology data, and what we're actually focusing on in this talk is looking at some of the radiology data that is also available for a smaller set of the TCGA-GBM cases, but we're hoping to build that archive. So like many of the other groups, we're looking at correlations of certain features with outcome and also with genetic profile. So again, we're specifically focused on this talk is radiology-derived features. So as many of you know, glioblastoma is a great for astrocytoma and is the most common form of brain tumors with a very poor survival, and it was one of the first tumor types to be included in the TCGA pilot project. So as most of you know, mRNA data, copy number, DNA methylation data is available, but it's not as obvious on the portal, but there's also neuroimaging data on this cancer imaging archive as well as whole slide imaging, and so our group is mostly focusing on these two data types. So again, the general methodology employed in our silica center is to develop a human and our machine-based assessments of imaging features. So in order to actually do this, we have a large number of neuroradiologists that we've been working with between six and nine depending on the time of the year that have actually been going through and actually annotating these cases for different features. Now the question is, what are these features that they're actually marking up? And so one of the things that there's not going to go into detail, but there was a large amount of effort coming up with essentially a standard vocabulary to describe what these brain tumors look like. So similar to the pathology data, what we're interested in is looking for extra signal that is embedded in the radiology data and the pathology data. Every case that we've looked at has a diagnosis of GBM, but when you actually look at the images, similar to everything else we've talked about, there's a huge amount of heterogeneity, and we want to kind of capture that information in a structured way. So particularly Adam Flanders and Carl Jaffe came up with a standardized set, and there's lots of iterations of this, but basically it's all of the things you could think of that would describe the tumor, size, location, as well as different imaging properties. Now if anyone's curious what Vasari stands for, it took me a while. I thought it was an acronym like everything else. It turns out the dataset that we initially used to validate this feature set was called the Rembrandt dataset. Apparently Rembrandt was the biographer of Rembrandt's name was Vasari, so they called it the Vasari feature set. So don't try to spend any time trying to get that out. But one of the big points that we want to talk about in terms of heterogeneity is, again, since we're coming from an imaging-focused view of the world, you can have a piece of tissue that genetically is identical, but it can have significantly different outcomes. So you can imagine a small piece of tumor that is adjacent to the motor strip has a significantly, honestly, shorter outcome than one that was in the frontal lobe because when they actually go in and try to do the resection, you essentially, you know, you have to, you can't be as aggressive about it, otherwise you leave the patient paralyzed. So this is, again, other metadata and other ways of looking at this rich dataset that's available. So, again, I'm actually not a neuroradiologist by training. I'm actually a psychiatrist. So I know, you know, I can look at these things. We can identify things. And essentially what the radiologists have done is come up with kind of validated ratings of different imaging properties. The ones I'm actually focusing on this talk are things that you kind of descriptive of the tumor bulk. So one of the ones that's very kind of easy to grab your hands around is the percentage in acrosis. So essentially what the radiologists do is kind of in their mind, they have a mental image of how big the tumor is. And then they can essentially segment, you know, they can say, this tumor is highly necrotic and this tumor is basically has no signs of necrosis and they're trained to do this and these are some of the things that they happen to be good at. And we had very good inter-rater and inter-rater reliability and we were doing these sorts of things. Now another, you know, there's four or five features that I'm actually focusing on. You can see here actually I went back. Basically what happens is in order to actually standardize this across radars they actually have a PDF feature guide that, you know, they go back and look at it. And we spent a lot of time getting the vocabulary right so that people agreed what they were looking at, people agreed what the words meant so that we could actually get a standard set of ratings and then we had people read the cases. So the necrosis one is one of the features. The other one that is going to wind up coming up a lot is the proportion of enhancing tumor. Essentially what is very common and one of the key features at least from the MRI data in GBMs is contrast enhancement. What that means is you give gadolinium contrast age in IV and then essentially parts that were not bright on the T1 image suddenly become bright. And that's particularly interesting kind of from a genetic standpoint is it's associated with essentially a breakdown of the blood-brain barrier, microvascular hyperplasia and essentially kind of funny looking blood vessels. So you can imagine that would be something that would be interesting to look at. So again I don't want to spend too much time going into the tooling but the actual process of acquiring this data was quite a monumental effort that was greatly aided by a number of our collaborators at the NCI. So basically the neuroradiologists were given 10 or 20 cases. They downloaded them from the Cancer Imaging Archive. And then essentially they have this kind of radiology workstation where there's a little plug-in, the little Vasari plug-in where essentially they go through all of the different imaging modalities. And many of you are probably not particularly familiar with kind of clinical neuroimaging but they normally get five to ten different scans of the head all with different types of imaging parameters. And the neuroradiologists are trained to use these different imaging modalities to look at different features. So essentially they download the images, they can look at them here, they can do some cross, they can do some very simple measurements. And then they basically go through and look for these different features like the one I mentioned is eloquent cortex. Is it in an area that you really can't resect and then you can imagine that would have differential survival. So for the data that I'm about to present we had markups that were, we had cases that were read by at least three neuroradiologists for 72 patients. We now have about 125 patients that have been read but this was all done not in time for this talk. And for the ratings that are going to present we have three ratings per person but these are actually collapsed down to a single rating. And so basically similar to the other analysis that have been presented we're essentially using these imaging characteristics as a probe to get a better idea of kind of genetic and survival implications of these sorts of things. So basically the first kind of the easiest analysis we did is basically we looked to see if there's any sort of correlation between the present, you know, if you have more of this feature what does it do to your survival. And so in this case the feature that stood out the most is the more contrast enhancement you have the shorter your survival was. And sort of as we build this argument that this imaging data can be useful we also started doing some kind of multivariate regression where we took a standard clinical model which usually has age at least for GBM the typical model has age, gender and a performance scale which is sort of how well the patient is doing at the time of surgery. And basically we tried to see if adding additional information from the from the imaging data would actually make you basically give you a better model give you better predictability. And this is basically what we're showing here is in this case is when we did stepwise linear regression, Karnowski's score was obviously highly significant. But basically when we started dichotomizing this and saying having a little bit of contrast enhancement versus a lot was again a significant predictor. Now probably more than I have to oblige to show a Kaplan-Meier survival curve and again this was this was significant in this. One of the things that's nice about these kind of qualitative assessments is we'll talk about kind of more sophisticated ways to do this but these sort of clinical rules of thumb become very useful for neuroradiologists to actually look at and kind of keep in their mind because I can do them relatively quickly. So unfortunately I don't have to introduce this concept but this idea of these molecular subtypes that are based on mRNA expression has been introduced multiple times. And one of the driving things that as we started doing our more molecular analysis is we basically asked if we have the pronural, the neural, the classical and the mesenchymal subtypes, are there certain imaging drive features that are more common depending on, you know, your specific molecular genotype? And the answer is at least in this analysis the mesenchymal type was noted to have significantly lower rates of non-conscious enhancement compared to other tumors. Similarly the pronural subtype which has a lot of interest in that specific subtype because of some survival differences was noted to have a large, a small degree of contrast enhancement. So essentially what this means is this is the area that essentially contrast enhances after gadolinium and basically what happens is they kind of in their mind they say this is, you know, X units. The entire tumor bulk in this case is basically this entire area of abnormality and again this is what neuroradiologists are trained to detect so these are the type of parameters that we're pulling out to use as our probe. Now, some of the other things, again this is just really to touch and highlight on this concept essentially is we actually looked at some of the mutation data. Now, EGFR mutants we discussed that very recently. Turns out of the 72 patients that we did these markups on there was only as of a couple months ago the mutation status was available only on 50 of them so it's a slightly smaller subset. But basically we wanted to see, you know, were there any imaging characteristics that defined patients who are likely to have EGFR mutations and obviously I'm showing it because there was so basically EGFR patients had a larger tumor essentially or a larger area of tumor abnormality and interestingly the TP53 mutants actually were smaller than the wild types and as a correlate that means EGFR mutants were larger in general than the TP53 mutants. So just sort of to conclude my talk, the main point I really wanted to make and this is an imaging based features can provide important prognostic information even after accounting for other clinical variables and as we start doing these genetically defined subclassifications and looking at things that predict survival keeping in mind some of these other kind of obvious clinical factors like location of the tumor and how that affects surgery and treatment becomes important as we try to subtype these things. Current qualitative work suggests genotypes may be associated with these imaging phenotypes and basically this really sets the stage for future work. So as we said, we're increasing the sample size so we can, you know, going from 70 to 120 is obviously gonna give us a lot more power. Also we're actually starting to move from ordinal assessments where there's these sort of categories that are actually easy for the radiologists to assess to a continuous based assessment because we think that's gonna be a more sensitive pro which in the fields called volumetrics that's just more technically difficult but this training data set that we have actually allows us to validate our algorithms and make sure we're actually doing what we're supposed to be doing. And kind of this is exactly parallels the work that Dr. Parvin and also Dr. Cooper and my group presented earlier with the pathology data. We can actually go in there and describe an even richer feature set that are things that are not easily to quantify for a neuro radiologist but you can go in and describe texture, you can describe exact shape and all sorts of these other kind of multi parametric properties of kind of what the tumor looks like. And we can start using deriving additional probes to basically get a better idea of what is the having an EGFR v3 mutation, what does it, does it make your tumor look different? And going back and forth. Now, that should have worked. I'll advance to the next slide. Okay, yeah, I'm about done. So again, I'm just about done my talk. I just wanna give an acknowledgement. This is a huge number of people have actually been involved in this and what's nice about this community has been very ad hoc. So there's been a number of people from Emory including Lee Cooper and Chad Holder and Scott, collaborators from Thomas Jefferson, Henry Ford, SAIC, Frederick, John Freiman and Justin Kirby, BU, Carl Jaffe. The NCI has been invaluable for statistical help, UVA, Rifka Cullen at Harvard, Northwestern. And then finally, this sort of make a pitch. If you're a TCGA contributing site and you happen to have radiology data, we'd love to have it. And if you can, we have ways to anonymize it and do a lot of the kind of grunt work to actually get your data out there and shared. And we think having this additional resource will really inform a lot of the other work that we're doing and that's it. Thank you. That's really exciting to see those clinical correlates. Thanks, David. I think we'll hold questions so we can have Elaine's talk at this point.