Can I share my screen? You can or I have the slides up as well.
You know, why don't you do that? Please thanks. Can you see my PowerPoint presentation?
Yes okay. So then I'll move forward to the agenda slide.
Okay, good afternoon. Good morning. Everybody, depending on where you are. Welcome to the September edition of mission community webinar we are scheduling this out of the regular first Monday of the month cycle because of the labor all day.
Nonetheless, we're very happy to have with us today's presenter Dr. which were not from the case or university will present on computational imaging for precision medicine requests for generalizable models.
If I could give a bit of background Dr. which will not is currently assistant professor in the Department of medical engineering, at case, Western Reserve University.
The primary focus of his research has been developing new image analytics, radio mics and machine learning schemes.
We already already pathology and fusion of the two modalities and correlation towards more comprehensive, biological connotation of missing data.
The point is technologies to image, guided interventions, direct guidance and quantitative evaluation of response to treatment, including radiotherapy, chemotherapy and for coalitions in cast resent intestinal cancers and digestive disease.
Is research has been funded through the deal, the Cleveland D RCC, which you could tell us what that is and the image without further ado. I welcome today, speaker, thank you.
Thank you came can you tell us what the yeah, the digestive diseases research call center here.
Yeah, yeah, I'm actually going to be talking about some of that as well. I figured I might as well put it all in here.
Hey, thank you very much for having me as a statements and my talk is going to be about computational imaging for precision medicine or quest for models.
So,
we're gonna talk about each of these things and done done,
but to begin at the very top,
where we are is is the huge potential for a AI in the world that we're in today that is,
you know,
there's been acknowledgement from across multiple domains from across the media to,
to the number of different funding sources that have started to take notice of the fact that is an area that we need to focus on and and and really move up the ladder in terms of its impact.
And the question really is, what are we doing with in this stock? So this time I'm approaching it from the perspective of complication imaging.
So everything itself is begins work back, it begins with the idea of precision medicine.
So presume, medicine is where we try to develop prognostic or predictive tools to try and tailor therapy for a patient based of a specific profile. So, given a cohort of patients. If we have an effective marker, we might be able to figure out which patient should get, which kind of therapy.
So, along those lines, what we wanna know is how well is a patient going to do on a particular therapy. I was gonna see progression. We also would like to understand the idea of predicting response. So, can we figure out what's gonna happen to the patient? Both happy?
Can we figure out what's gonna happen to the patient after therapy?
And all of these are gonna govern that idea of precision medicine and this,
the sort of solution we're taking to this problem is to approach it from the context of competition and competition itself is the idea of trying to apply competition techniques to enhance or improve a standard of care geographic imaging
data in many cases.
Then radiographic imaging is commonly used in across a number of different diseases for noninvasively where the diseases, and to some extent trying to understand what is the response. How do you value treatment so on?
And so we're proposing is the idea of and hunting with competition. So how do we do that? So we have a general workflow here for how competition imaging works.
So, you know, you have a, you have a series of or scans we, you know, we typically run some kind of texture analysis on that, to compute text or features to characterize what is gonna be heterogeneity in the image over here.
The blue corresponds to things, which are less heterogeneous and red correspond to a more heterogeneous and then we can also look at different type of shape and morphological descriptor.
So,
what is the overall shape and specularity of these structures in the body based on imaging and then based on these measurements based on these quantity to descriptors we can then build a model where we,
we can identify the most relevant features and run that to a classifier and finally gain validation of that performance.
And in the stock, I'm actually gonna, I'm gonna be talking about some of the studies we've done in this context, but I'm also gonna be talking about. What do we need? How do we, how do we move forward?
How do we move from the space of competition meeting and radio mix and start talking about them as generalizable? What does that even mean?
And so sort of summarize that answer to challenges and I'll talk about this as we go along the first is, can we start to can we start to expand our features or can we start to look at features which capture additional characteristics of physiology on imaging?
So we have shape and texture can we start looking at new types of information and then from the perspective of the model generation, how do we start to design these features and models? So they start to generalize better across sites and scan. It has been a big focus of the work. We've been doing over the last few years.
So, in in, in to sort of summarize what I just said, we, we are only talking about competition, limited tools to enable better disease assessment. What do we treat? Where do we treat?
We're going to be trying to characterize different types of response.
What happened after we gave blueprint to a particular patient and just said that some of the work that has been showcased in recent news organizations and so on from our group.
And then, how do we start to make these tools more generalized? And again, I will, I will repeat these definitions for the down, but I just wanted to throw these ideas out there to begin with, which is, we can think of them in the context of being both repeatable and reproducible.
And both of these terms are critical, in order to understand the idea of generalizing. Okay. So that's it. Let's, let's dive into how we can start to use competition, leveraging tools to to enable this kind of disease assessment and response data strategy.
So,
a lot of the work that that's ongoing in my group is in colorectal cancers specifically we focus on retinal cancer,
which affects about forty thousand patients in a year,
forty thousand patients in the US and this timeline here just sort of a general clinical protocol for cancer patients,
you know,
they get diagnosed,
we confirm that is disease.
We see them the undergo some kind of therapy often therapy then another staging in order to figure out how well, the therapy worked.
Then they almost all undergo surgery and then we have a pathologic assessment to figure out.
Well, how well did we do with all of this extensive set of operations we've performed and then after that, we start to fall off the patient, depending on what is pathology, but it turns out that almost close to thirty percent of the patients.
When they look at this backlogs experiment after therapy, there's no tumor remaining on the specimen. Right? Which means that the therapy was very, very effective, but that is done. Begs the question. How do we start to what can we do differently with these patients?
The benefiting? So much from therapy, maybe they don't need to undergo surgery. Right? If they don't have any tumor, we don't need to send them into surgery. And so the question is, what are we, how do we sort of revamped this pathway to make it more precise?
Make it more targeted, catches that right up front as we start to look at the M, and I, and the biomarkers that required diagnosis, there's, these are not very predictive in their performance. So, what we would like to do is somehow try to use the imaging system.
So competition, we can talk to identify markers of response before treatment so we can noninvasively start to personalize the treatment for these patients. So the study that recently published in earlier this year, we had about a hundred patients.
All right all right. Quick answers. And it was conducted across three different sites here in Cleveland.
And what we did is in all of these patients, we had the diagnostic scan, the scan acquired just before canopy, and we defined the pathology, the pathologic outcome for these patients as no tumor remaining after they've gone through their entire clinical protocols.
All the patients are diagnosed chemo radiation surgery and then if they had notes, no tumor remaining on the pathology, they are considered to be a complete responder versus a there'd be an incomplete responder.
So that's great. We now have a signature that we can find on the diagnostic scan, but you will notice that there is a follow up scan that we can we can try to use as well. And so, when we started looking at the follow scan, we realized very quickly, that this is a very different beast from the diagnostic scan.
So, the catch is that after chemo radiation, there are lots of frequent effects, such as fibrosis. That are present in these. It needs to in these areas.
So, the problem is that visually speaking for the radiologist, they don't know where the two minutes in these patients to begin with. So they have a relatively low accuracy and re, staging the presence of in these patients applicable radiation.
So,
we set out to say,
okay,
what can we do to try and do something with this from the context of radio mix and competition and so,
again,
we had a a hundred patients in this case,
got published last month in cancers after being presented around sunny a couple of the past couple of years where we had a hundred patients close to a hundred patients from across the three different sites here in Cleveland that we're working with and over here,
we used to more Downstate.
So, in other words, the pathologic stage of the tumor, is it low after tumor radiation on the backlog we define those as responders.
And then we looked at what we did is, we got we annotated out the lumen and directly wall so that we don't need to worry about where the two minutes is going for these structures, which are much more straightforward to identify.
And we found that there was no real relationship between the intensity and the volume between these. So, from your perspective, radio mix needed two things. One is, we looked at the morphology of the sheet and the structure of these different things on imaging.
And then the other thing we looked at was a host of different extra features, including status, the board collage. So when we when we train on classify using one side, again, we validated on two different sides.
One of the things we found was that higher. Pathologic stages tended to exhibit more abrupt changes in the lumen and the active bond and the hypothesis we have for. That is that essentially if the tumor has regrets low pathologic stage tumor.
It's a very regular structure, versus if you have too much sort of shooting out from the wall, it tends to get more standard and more regular and so the shape features essentially became this characteristic. So it makes sort of intuitive.
The other thing we found was again, going back to our gradient features. Gradient. Heterogeneity was much higher in patients who did not respond compared to patients who did response once again we trained it on on one of the sides, and we tested it and validated or two other sites.
And again we have a performance of close to point seven consistently.
Interestingly enough combining the shape and texture did better than using either one individually and then first model again, we, we were able to develop it to be resilient enough to scan or incite differences.
And again, we're talking about a number of different scandals insights across in the study so.
That said before I sort of Segway on to the next focus of my talk, I just wanted to give a call out. So some of the tools that I've been talking about over here, I spoke about collage, which is our gradient heterogeneity feature.
So, through a recently funded U a one, we've actually started making these schools available to the community.
So if you go to this website to get hundred or get hub dot com slash, you'll find repositories for implementations of these tools. So, I'm showing you a video of the tool running in real time in three D slides.
It is loaded the image in the selecting parameters there and visualizing it for you.
So, it can be computed into the three D, and essentially gives you a visualization and a quantum data measurement of the gradient heterogeneity inside the region of interest since this is the general tool.
We can be showing it in the context of lesion in the brain. So this is in collaboration with my Co. Dr. also our case Western.
And then the other feature, which we're going to be releasing, I think this week very soon is a feature to do with that. But the idea of computing, more advance metrics of the surface. So looking at the surface topology and the regularity.
And again, this is again going to be released as a Python module integrated to the host of different platforms to this one.
And I, I encourage you all to follow up at radical tools to, to get the latest updates on the teams we're developing in this context.
Alright, so so we find some very interesting results with these, these sorts of tools. So now, the question is, what can we do more? How can we've started design competition, leveraging tools to capture physiology and make them potentially improve that generalizability.
So, going back to the retinal cancer image, when we look at the amount of material, what I've done is, I just overlaid the image with a heat map of maybe in the wall. And then I'm just sort of pulled out a few different regions from here.
And what's interesting is we actually have a a unique scene for these for the patient, but we also have remember, the patients underwent these are actualization surgery. So we also have the pathologic stuff.
So, the question is, how can we start to put these together? Can we somehow take the radio measurements randomly distributors that we have and connect them back the pathology and somehow use that to start to design the next level of features? Could we, you know, essentially, we have heterogeneity.
Can we start will be on heterogeneity?
So the first part to that is to try and figure out how do we start to map things from your imaging and figure out how to design design our features.
So,
this was a study that came out in academic radiology a year,
or so ago where we had a set of these patients we had backlog experiments together with we had our clinical experts,
help us,
identify landmarks and the pathology,
which we used to map these.
And then that allow that to get a very detailed mapping of the pathology onto the, which we then validated with an additional set of landmarks. And we found a relatively low target registration results with tool will actually be relieving to that. You are one that I mentioned a minute ago.
But what was interesting is if we start to plot how these, how these different pathologic annotations function as a marker of response. So, you know, we have our pathologic.
Annotation can be registered to the different colors here, correspond to different regions that logically map the regions on the imaging. And this is all on a post treatment specimen. So different.
So the first the blue region over here at a spat, the Y, axis in this plot is the patient and the X axis is just bin densities. And these are the same.
These are basically the distributions of these identities and these regions just sort of plotted side by side. What's interesting is that right? At the complete response region? We have very distinctive pathologies according even on the imaging scale. Right?
We have very distinctive signature between that, and the other end is fibrosis compared to poor response where everything gets very murdered up and then you have this, you know, the two modes in that as well and it's sort of intermingling in there.
So that seems to be a, it's obviously very hard to visually tell these conditions apart would be, that seems to be sort of a spatial interplay in terms of how distinctive, how separated, how many distinct compartments we have on the imaging when we start to compare to pathology.
So, in order to understand this a little bit better, we, we designed a new descriptor and so what does distributor the way the descriptor works is we go in and we have our radio mixing. So then there's that's the heat map.
We have over here where blue is low heterogeneity and red is high heterogeneity and then we take that seen and remembering that the pathology has a series of different compartments. We convert the radio mixie into a set of different compartments based on the radio values.
So now we have essentially, you can see that there are different tissue types in here, all of, which are interacting, you have different compartments, all of which are also interacting. So we can then build a graph on top of this, in order to compute our descriptor. So, industry perform is made of two parts.
The first is a spatial distributor, which sort of describes how these compartments interplay with each other, how adjacent they are how many of them are adjacent to the other ones and then the txr descriptor.
So one point I'll note here is that, for the sake of simplicity, sort of partitioned into three compartments, we have low medium and high. We could obviously tweak this as we need to.
So, you know, when we started to look at this in more detail, so we know what I have is, we, we started to study this in the context of rhetoric answers. So, again, we have our images.
We, we have our annotation and now, in this case, we're looking at outcomes after therapy again so these are all post treatment scan so we're not discovering set. What we found is when we run our heterogeneity map, we see a difference. Visually. It looks different.
There's more red and yellow and then more blue and red, but if you look at the statistics of the thing, which is what we would typically use Iridium analysis, the statistics are not very separable. Right? So, that's a problem.
So, basically, we see something, but statistically, we don't, which is likely, because we are seeing the interactions and statistics on our capital with the interaction.
On the other hand, if we use rabbit stat or radio spatial descriptor, we're able to do is we're able to develop the signature for the technical component as well. The spatial component notices.
Detection component has a lot more interactions of one type, competitive, spatial computer, which has many more distinctive attractions, which goes back to what we were seeing the pathology as well.
So, again, we, we did our training on on one side about forty patients that we validated on our second side about thirty patients.
And again,
we found that the signature actually generalizes relatively well,
the same very,
very similar,
very same signature,
both in training and validation the same distinct compartments,
the same kind of special organization quantitatively from any UC perspective this sort of bump those up further going from that point seven sticking to close to a point eight now.
So the addition of the new piece of information is not only giving us sort of, more intuitive descriptors, but it's also potentially increasing our performance.
So, as we, you know, what, the grandstand method itself works based on a series of parameters.
So we conduct those parameters, sensitivity analysis and sort of gratifying to see that the signature was very consistent across all the different parameters that examined.
Then, the question is, what do these signatures? What are these compartments? How do we know for sure that they are really picking up what we want them when we say the pick. So we go back to our pathologic specimens. So we mapped our pathologic specimens onto imaging.
Then we computer that adage that match those specimens.
And we tried to identify which expression maps, which would be the expression levels correspond to which of the, the compartments.
And what we found was that if you actually looked at it, the, the fibrosis tended to be falling more within that low expression region on this map. Right. Versus the cancer tended to fall more than the high expression region.
And so that suggests that there is this these compartments are somehow being driven by those, you know, different tissue types that we that we started with. So sort of came nicely full circle.
And we also found that maybe an association between these signatures and continual pathologic tumor regression greater in these patients as well.
So we've spoken about sort of taking texture and enhancing it to the next level using sort of spatial ideas. So, then we don't our attention to ship.
So, in the context of shape, if you remember what I said was that in patients who regress, we tend to have much more regular structure, both in the lumen and the rectal wall compared to patients did not invest more in regular structures.
So, if we, I think think about what that means, what that means in the context of patient, who regressed, we essentially have a distinctive a difference in terms of how that structure has default. Right?
So, in the case of a patient who's addressed, we have much more sort of uniform definition supporting, versus in a patient who did not invest. We seem to have much more non uniform, regular definitions. Similarly, we can, we can sort of make the same sort of.
Leap if you will for the rectal wall similarly, the wall appears to be more regular in one case and more irregular.
So, if you're trying to compute this idea of regularity irregularity, and trying to enhance beyond just simple ship, descriptors, it seems that we're talking about, you know, almost like expansions and contractions. So, the idea is, we need to compute.
So, which would suggest that we need to start looking at definitions. So definitions might be sort of, the next level of shape but in order to compute a definition, you need a healthy sort of baseline to begin with. So you need the Atlas to begin with.
And Atlas, ideally, you should not have any disease in it because otherwise you won't be able to figure out what the attachments. But, you know, how how your, how? The tumor patient, how the tumor seen has changed with respect to the healthy address.
So, we went in and we built the first practical Atlas from healthy patients.
So we used a sort of healthy from from patients in this case, who had prostate cancer, sort of, done the same thing in the context of other kinds of other than logical cancers as well.
So, we've taken these annotations and built a healthy rectal Atlas with these are patients who have no tumor in the rectum at all. And then we take a patient who does have recommended, go, register these onto the healthy Atlas.
And we can then start to look at whether we have expansions contractions. And so that's what the data was correspond over here correspond to. We have expansions of contractions.
And then we can look at this and compute the magnitude of these contractor that expansion, and That'll give an explanation magnitude inside the rectum.
We can similarly compute the destination, man, to just outside the idea being that some relationship happening there, in terms of how that the structures are deforming expanding on contracting, both inside and outside. Compute.
That might prove to be very useful motto.
So,
the work that we presented in last year,
where we found that the defamation statistics within that rectal wall are distinctly different,
a lot more destinations,
occurring in patients were non responders compared to patients who are responses,
which sort of goes back to what we started with the hypothesis was the defamation with different do more irregular,
and that's reflected in the more,
you know,
irregular information that we see.
So look into the studies, we have our, we have our maps with the arrows showing the contractions and expansions and then we have the destination magnitude where we have the red corresponds to regions, which are high definitions.
And then we are using these destination features, actually takes us all the way up beyond point. It takes us closer to point eight five in terms of differentiating between good and bad responders. And now, this again we validated across sites.
So the speech also seems to be facilitated to sites specific to subject.
So, before I sort of going into the next part of my talk, which is on the idea of generalizability and what we need to think about in that context, I just wanted to make a quick Segway to throne cities.
So, this is some work where it sort of brings together some of the other features and methods I've been talking about.
So, I just wanted to quickly touch upon some of the recent findings that we've had, where we found that we can use radio MCs to try and assist in this idea of trying to find biomarkers to predict outcomes in Chrome city. So it's a big problem. But seven hundred thousand patients suffer from it, it's not cancer.
It's a disease, but the catches that, even though we have high resolution, and I don't quite have good markers to figure out what's going on with the patient.
And so we did, you know, when you started the study a couple years ago, we did a study where we had a set of patients where we had bowel and eye scans. So, these patients since this is not cancer questions. How do you define response? How do you find somebody who is gonna respond to therapy?
So, somebody was not we said, okay, if your patient has Thrones and for your after, they were diagnosed with to a stable consider them to be a respond. Okay sorry? Within a year, if they get surgery, they're considered unstable.
They'd not benefit from the so they are non responder. And then if otherwise you was stable for longer that's for this and so we got the tone Ilium here. That's the region of interest that we validated.
And we did radio mix on that standard radio mix to begin with. And what we found was that we found very distinctive differences between the patients who did respond and did not respond again. Usually non responders tend to have more heterogeneity.
So, again, it's interesting that the signature seems to generalize across different diseases, across different sites and the performance for this sort of limited cohort of about forty five patients is to, you know, point seven, seven in cross validation.
But don't disease actually has multiple hallmarks.
So,
one of the hallmarks approach to these,
so we,
here,
we have an MIT scan this right here is where the disease is it's sort of the ball gets Ticker,
but then you have all of these other regions in the body that that are of interest as well,
so the mesenteric fat or visceral mesenteric fat that builds over here and then you have the subcutaneous fat that appears on the outside.
And what they found in crohn's disease is that these different fat regions also have a relationship to the information. Right?
So, when that ready or printed design, new features and interrogate new regions with radio mix, we start to ask a question could be somehow start to interrogate these relationships a little bit better. So so in this study, so what we did is we went in and we built an automated tool.
So these are three different patients. So, the top patient patient, who had no disease, actually a healthy control. And then these two patients both had crohn's disease. But one of them was stable on therapy, and the other one did not respond to therapy.
And so in all of these patients annotated out, we got we automatically segmented out the region of the mesenteric fat inside the body.
And then we started to look at radio mixing as a sort of sanity check we said, okay, first, let's look at radio mix on the outside. So technically the subcutaneous fact is this fact, it's not very informative. We found that there was no audio signature from the dealer, which in that context.
But if we instead we look at the mesentery and we start to actually interrogate that with the new mix.
We see that the activity in that region keeps increasing as a function of the disease the most, you know, if a patient is not responded, they tend to have a lot more activity compared to responder who has more activity compared to a healthy patient.
So it's interesting that there's a signature there that we can interrogate with radio and validating this across two different sides. We achieved an agency about pointing to about seventy patients from across two different sites. There's some work that they're just putting out for under review.
So, that brings me to sort of, the next phase of the talk, which is like, we have spoken a lot about different competition, leveraging tools. We've spoken a lot about, how do we start to design them and interrogate other regions with them but how do we start to make them generalized?
But what do we mean, by this idea of generalizability? So, generalizability itself, you know, essentially comes from the concept that all machine and deep learning models are going to be impacted by imaging artifacts and changes in imaging conditions.
So, yeah, we've just, I've just shown bring data from the and so, you know, we have, we have a lot of.
Scans, which are of great quality, but then we also have our scans, which is to be expected since, and so clinically acquire cohort where we have a series of different artifacts we can have artifacts amazing noise.
And shading problem is that these artifacts are going to dramatically distort anything that we're gonna try and do with these images. So, maybe we pick up the artifact. And then we're not sure if that's an artifact or it's some kind of certainly cancer.
The other problem is, you know, if we start to look at the Watson resolutions of these patients across disciplines, we tend to see a huge variable resolution process.
So then,
the question is,
how do I try to figure out if my method is going to generalize canoe sites,
if I'm gonna have fundamental differences in such a thing as box and resolution,
when we go from inside the site to so,
to address these and other questions we've,
we've developed a new tool called marquee or so Marty is essentially a quality control tool for data sets.
It allows you to check the quality of large cohorts, visualize the trends and quality and it's intended to be a very quick, robust way of evaluating what's going on in a cohort before you start getting into analysis.
So, given large data set, you can run marquis on the data set. It's pretty quick. We've optimized to be about forty seconds per data set. You know, there's no real specialized hardware runs about forty seconds on my laptop. It's open source is available online.
I'd put the link up in a minute, but the idea is, you take the data set, you run Marcie on it.
You check what's wrong with it, fix it. So you practice around Marty on it and then you sort of on this process still you're happy with the data set, and then you move on to the radio. So it's not intended as a Pre analytical step.
So a very quick video to show you how Marty works. So we can just loading in the Marty file, which is a locally hosted text file HTML file, which then loads in the results of running marquee itself. So the front end interface.
So this is the front end interface it runs in a regular web browser so there are different sections.
That are, there are there are there are tables of the different measurements that we extract to extract the of measurements and the data noise and variance and signal to noise ratio and so on.
And then we visualize these measurement using the very interesting visualizations below. So this is this one is not a valid one it is to show you, which, where you see a lot of variance that you see a lot of agreement.
And then below that, you have scatterplot to show you the visualizations of how the data sets in consulting space based on these measurements. So you can very quickly look at any given data side. It'll highlight it across the entire interface. So we got two candidates.
It highlights it here. There's also you can also visualize the trends in terms of a backlog to the backlog. It shows you, which are all this outliers will obviously be either very low buildings or very tall buildings in the in the block.
And then you can do this for different measures and as you go through, you visualize images. So if you find and I can very quickly see what's going on with that particular data set.
And then it's a completely interactive interface you can click on any of the points in these things to show you. So it shows you the different clusters that appeared in the data that just based off the raw data. And then that allows you to sort of go back and forth and sort of say, oh, I see a problem here.
Let's go up here and so on so Marty is available online it's available to get help right now. I can go and get it please feel free to it's an active development. We have a wiki and tutorials and how to use it.
It is, it is a system project of of, of an effort going on at case to who's who's you may have heard of has done created a tool called history.
Juicy Marty, the sister tool to that again intended now here for quality control of imaging data sets instead.
So we've actually generated some very interesting results with marquee on cohorts themselves, and these results are available right now on the so these measurements already available on the for people to look at.
So, we've actually done it on three different cohorts and we found artifact and batch effects, batch effects, essentially site, specific variations in this cohort. So this is just a visualization of the market interface.
So, if we just zoom in to this particular block right here. So this is for about a hundred and thirty three brain data sets. These are all D, one post scans with about seven sites in them. If we zoomed in.
So, we found that, you know, I'm just showing you a quick example of how you can find artifacts. In this case. We found that on this particular measure, which is the measure we found that the, that we had a very clear outline when we visualize the outline.
We found that there was very clear contrast difference between the outline and the respite cohort.
Going on to the rest of the measurements combine all the measurements and you sort of, and you make a Scott scatterplot of all of them, you actually find seven distinct clusters in this cohort, just on the raw data.
So that means you can define, identify any one cohort with over ninety percent accuracy, or use this data without doing too much to it. Of course, we, we have our pressing pipelines.
We ran that on this,
which included,
you know,
different steps of bias,
correction,
standardization,
amount of re,
sampling and with that done,
we were able to fix some of these issues related to the outliers as well as sort of,
and make this whole space more modernized.
So, in theory, now, the radio mix should be a lot more resilient to the site specific differences that do have other.
We've done this for multiple dates as we've done it for another gbm data set as well as the is actually an endometrial cancer data set is also available going back to our regular cancer cohort that I mentioned earlier.
We ran it on that as well, just to sort of see see if marquee will generalize no matter what you're requiring them for. And so, over here, we have about a hundred different from three different sites.
And again, you can see we are able to identify very clear differences. We found an outlier right? This patient had more noise than than the has to the patient.
And now again, if we look at the three sides over here, this is now wrecking cancer mind you, we're still able to find two sides variations indicating we need to do some amount of processing on this data set.
Before we do all the radio make this one, so we're able to fix the imaging data before we start running video mixed on it. But is that enough? Does that suffice to ensure that we have some kind of generalizable model?
So, the catch here is that we have differences in canvas, magnetic field strengths. We have differences in the books and resolutions. We have differences in the meeting parameters and all of these are going to contribute to the radio makes me different.
Even though we might have process the data many different ways and Marty reveals that it's already homogenized and may still be that there are differences.
And we actually see this when we look at for these two patients coming from these two sites, we actually found that the signature is quite significantly different. There's, there's almost no relationship between site one inside to the question.
That is how reproducible are remaining features. How can we evaluate them? And.
So, in a study that we published that we presented at, at honestly, in D. W, the first place we looked at this, in the context of was in the proponents of groans.
So, here, what we have is, we're now looking at imaging, so we're not trying to figure out how consistent already with features across different CP parameters. So we can, we actually have we have cross patients who have disease and not have disease.
And then we have a few different variations of those so we have a hundred percent, those reduced dose. And then we have different types of reconstruction as well. So, question is, can we identify data me pictures, which are resilient to these variations?
And so over about a hundred patients, so what we found was the in general again, disease patients tended to be more heterogeneous. And what was interesting was the signature actually did generalize across these different variations.
Because I think one of those collage features, and then if we looked at the performance, you know, training on on on, even just the full, those gave us very consistent performance across all the different doses and variations with no real significant differences in the class. That performance.
We've also conducted a similar study where we've looked at variations and imaging parameters and our imaging parameters now, in a multi sites setting. If you published in general medical imaging a couple of years ago. So, what we did here is we looked at four different sites.
We have a hundred and forty seven emerites and then we looked at the reproducibility of the stability operating feature.
So in this block, this is, you know, a feature that is that has good stability or is it is is have low instability.
Highly reproducible has very consistent values across the site, whereas a feature that is not very good. Had inconsistent value. The prospect was interesting is that if you look at the descriptors themselves, we found that in general hardware features.
The features tended to be very, very stable across all the different types versus something. Like the last feature tended to be very unstable across the different sites. So.
That's interesting, but bytes that just tells you that different dating features have different characteristics.
If you compare that to the of the features and others, we're now looking at a trade off between the reproducibility and and the discoverability or how well, how well, the feature is able to discriminate between tumor and entrepreneur. In this case.
What we found is that we actually have very distinctive clusters that tend to appear so the last few last features are very stable, but not really just going to move a few laws features very unstable and not very dismal. Revives either really?
For features comparatively. We have had features appeal, which have a nice trade off there relatively stable and disposable board features tend to show a little bit of variance in terms of how stable they are.
So, this revealed to us that there were some very important trends in the radio feature. That we needed to account for if we want to actually use this using the feature more consistently.
So applying that context, applying that concept to canvas. We decided to run the study where we to help directly memorize in order to compute how stable the features are based on our healthy.
And then we have our patients who who actually have the most and we can compare these to identify which features are stable. And we want to keep those features, which unstable we'd like to get rid of those. So, potentially we like to improve our performance by doing that.
So, you're gonna plot of the performance of the different features are with respect to the reproducibility. We set up, we keep the features that we care about.
And we look at these features in more detail, we found the features that we kept that highly stable and highly dishonorable tended to have very consistent signatures between discovery and validation. Right? So, we have on the on the left here. We have a responder on the right here.
We have non responder. You can see that. The signature generalize is very nice. Me between is quite different between the respond to the non responsive, but very consistent between the discovery and the validation.
This is actually, I think it's a collage, heavily feature compatible and if you were to look at the last feature, we actually see that the trains almost completely invert themselves when we go from training to validation. So that's a problem with the kind of feature that would have been included.
We were not applying some kind of reproducible criteria. And in order to prove a feature set.
Using those features to run a pass by we find that, you know, regardless of the feature selection scheme, using a stability informed features, feature selection approach tends to significantly improve our performance and actually generalize even better in validation.
So,
with that sort of drama to a close,
so we spoke about competition tools,
how they can enable new types of disease assessment and treatment evaluation and enhance the use of imaging we've presented for the new reading with feature that might be able to better capture known disease phenotypes,
it has organization and destination and then starting to then those kinds of features are the ones that tend to be more generalized.
So, and in order to ensure that we need to control for social variation, we need to identify how reproducible stable features are. I have the links to some of the tools that we put out there.
So we have Marty, which is our quality control tool and then we have the tools that I mentioned coming out of our, you will one on new competition and features available also on GitHub.
And then you can always follow me on Twitter and before I conclude, I just want to give a shout out. We have very exciting events coming up of case.
We have our artificial intelligence in oncology symposium, according to happening on to the ninety you can register. The registration is still open. I think early bird registration is still open and then.
Dr, and I are actually Co, organizing a tutorial and meet at twenty, twenty talking about some of the challenges and treatment responses has been to know quality as it relates to. Are you one that I just mentioned and registrations are open for that as well? We have already in, in both symposiums.
We have an excellent line up of speakers. We've invited speakers from across radiology, pathology, oncology, drug discovery. We're gonna be talking about different aspects of artificial intelligence, quality and treatment response and so on.
Because I like to acknowledge the funding agencies that made all of this possible. And and of course, the excellent team that I work with, who do amazing work everyday no matter where they are.
And then I get to sit here and tell you all about it. So, thank you very much for your attention. I can take any questions you have.
Thank you. So, that was a great presentation. You have any questions.
Yeah, this is a big trouble. So the actual council work, you showed some area under the area under the.
Good graphs, snap resume. These are all based upon the post treatment Pre, surgical scans correct? We've done it in both. We've done it on both the Pre treatment post treatment.
Okay. My point is that with the Pre surgical scans, the decision point is should the patient have surgery or not?
And if the patient is has a very high probability of having a pathologic to complete response, you might skip the surgery.
But I think the bar for that is going to be very high and I don't know if you have a, you, what your clinicians have said about how high the board needs to be. On the other hand for the Pre treatment scans.
If you can identify the patients who have a high likelihood of not having a pathological compute response, then you can intensify the treatment. And I think the bar for that might be actually lower.
I was just curious whether, you know, what are your, what is the feedback from the conditions about where the bar is for the Pre treatment scans versus the Pre surgical scans because the suspect for that, or the bar would be much much higher. Probably close to one point. Zero.
Uc yeah, no, you nailed it almost correctly. Exactly. The, the bar is There'll be a little bit lower for the Pre treatment setting down the post treatment setting.
That said the post treatment setting they are it they won't even, you know, they're already currently there are only two trials, and they're already in many cases, they're trying to move to this paradigm of doing the watch and wait the monitoring.
So this would essentially be a three hour mechanism to put them on, give them more confidence in putting that patient in to watch and wait. So, yes, the bot has to be high in that.
We have to be very concerned about how, you know that there is no tumor remaining, but it may not need to be super, super high either, because they are going to be doing very intensive follow up for any patient that they don't send it to service.
So, it's also a question of, you know, how that into place with the clinical, how are the printers we're building into play with the clinical protocol. So, the idea is, if we're able to improve that, then, of course, we will be able to do much better in the prefix and setting again. You're right.
The idea is to try to identify treatment for the non responders. The catch. It's actually a little bit trickier on the Pre treatment setting because the, the idea of of sort of, what do we do with that patient is a little bit more of an open question, right?
They could intensify treatment, but if the patient is just not gonna respond to.
More radiation then, is there something else I can do? And that's where it starts getting a little trickier. So the feedback I've gotten on that, that's that's very helpful for selecting patients for clinical trials. Yes. That would be perfect.
Yeah, and that's what I was about to say is they said, well, you know, we have clinical trials and we're trying we're trying these in some cases, they don't even know Tempe, they're trying mortality therapies. You know, maybe they wanna try interrupt through surgery radial surgery.
So that we could start to look at those kinds of therapies if we knew the patients are going to benefit more from that, rather than the standard of care. So.
So, to make a long story short, what is the area under the curve for the Pre treatment scans so the right a predictor, or what they want.
No, in terms of predicting for pathological, complete responds, is it better than better than flipping a coin? Yeah, I mean, yeah, I mean, right now we are, we are currently at somewhere between four and seven end points on Friday.
You see, granted, that's not. It's not for shattering right now, but it is validated across multiple sites is more confident to me than it would be.
Otherwise, right now they don't have right now the existing stratification model they have are not, you know, are not predictor. They can use them for assessment to some degree, but they're not predictive yet.
So we appear to be pitching more towards a prediction side of things on that front.
Okay, I'll talk to you more offline about it. Yeah.
Other questions for the speaker.
When you're normalizing the images across centers, I don't think you talked about in detail. What you do. Are you saying that's the preferred practice before doing any oasis to do the correction and if so, what is the?
Because there's many ways one could do that. That that's a great for the great question.
So,
I'm suggesting that when we start dealing with multi site,
cohorts,
multi scanner,
or we need to be careful about the way we start to normalize and correct them and potentially restart,
we need to start applying these kinds of quality control mechanisms to really confirm that.
There are the issues that we found to begin with are actually being removed, because we've actually found cases where, if, you know, for example, in the rectum cancer cohort we started out with. Okay. You know, the standard pipeline is we put a do some amount of re, sampling.
We may have to correct the bias field. We may have to do some standardization, and we found that the Marty plots that I'm showing you designed to get worse and worse in terms of the differences between sites because some of those operations are not needed.
So well, you know, as opposed to laboriously sitting in going through a hundred, it give us a very quick way of say, okay, now we need to correct these aspects of the scans. Maybe we need to do some additional processing of the scan in order to.
Correct the area of interest for us, and then run the downstream analysis. So, in terms of the specific pipeline, I can tell you the patterns we've used over here, but I think it's more critical piece. The more or the message I have is.
When we're running those pipelines, we need to be having some point of almost quality control to confirm that the pipeline is working and doing what we want.
I have a related question. How do you decide whether what is indicated by market is not a false positive effect.
So, that's where that's where the interface comes into play. So, you know, be me.
In this interface, so this, the backlog that we have here, essentially, if you think about what you're trying to do with the idea of processing the data for what you're trying to do is you're trying to make the more consistent. Right?
So, it's really it's, that's why I was saying that the way you would use it is, you would run marquee, run a set of processing steps, and then rerun it and see if your metrics have overall improved or become more consistent.
And that was something we had seen in this study where we had a significant deviation here after we ran the analysis we found that we were able to sort of make the data set much more consistent. And that's what's important from the radio mix perspective is not consistent.
The radio is going to pick up the differences just due to those, you know, just do two, small variations. Okay. Thank you.
Any other questions for you. Yes, this is George. Do you see this effort going beyond deep learning to support cancer imaging research?
Yeah, yeah, I think I think the, the ideas, the idea of doing these kinds of generalizable models is kind of generalizable tools. That can pick up better signatures. I think that's that's crucial.
For in this case.
I'm sort of primarily in the context of radio mix,
but if you would imagine deep learning,
if you would imagine,
even clinical imaging programs,
I think the same sort of ideas resonate there because over there,
too,
you need to ensure that the data sets are of consistent quality that that are the artifacts and are gonna throw off the models or the,
the radiology,
et cetera.
So, I think it becomes a really key key part of, of any of these pipelines is to make sure that these things are working the way we would like them to before we start running all our analysis.
Somebody had raised their hand. I don't know what.
This is set for Reno from May I ask a question. So so you mentioned the difficulties with radio mix features, being robust.
So that you run it again. You may have completely different answer. I think because of all these uncertainties. Of course, you do have case studies.
Where with a hundred patient cohort,
you were able to demonstrate certain features that seemed to be robust enough,
but it still,
I mean,
it could be flaking and it could be just your ristics.
The question is, I think the word predictive is too strong for what the actual results demonstrate and it should only be correlation.
And have you ever done any,
like,
patient kind of measure,
or metric for which you can you,
you can show a stronger than correlation between those features and and how the,
the tumors respond.
So you're asking.
Have we done a vision modeling to figure out how predictive the features truly are? Is that yeah, yeah. Or or any metric that you can so so, in this context, we've not done division modeling.
We've done it in the context of other cancers and diseases, and found some interesting trend. Your point about the predicts about calling and predictive. I agree.
That that does, you know, we're not quite a predictor yet, because to truly confirm prediction, you would need to run it sort of prospectively or on a clinical trial data set of some kind where you show that.
It really did, you know, differentiate your treatment groups are your response groups in a truly blinded fashion we're creating it and then revalidating it.
So, yeah, so your point is well, taken about calling it probably more associations or more correlations if you will in terms of frequent response versus radio.
But the, the other point you mentioned about the robustness and whether it's, it's sort of how how sensitivities and so on. So there are some efforts that we we are undertaking right now we were trying to understand exactly what the performance bonds are.
Because if you think about it, all markers have a certain amount of variance present in them. Correct? So, it's not as though the existing clinical markers are always exactly where they need to be. These are all biological measures. There's going to be some amount of variance. And then I think the point with the radio mix is.
We need, we are now we need to move it to place where we have those bound better defined for us, where we can say, well, this is the this is your range. This is the sort of input that is given to it.
This is the range of values you're gonna see and this range is as tight or as broad as one for a particular problem. This okay. So based on this and now confident of making this a marker for actual clinical use. So, I think it's a little bit of both.
I suspect we're gonna start as the efforts will be starting to move radio mix in that direction in terms of really validating it in those settings as well as understanding the performance bonds on radio. Where is how robust how repeatable is it?
And we're seeing all these studies coming out nowadays and those are all towards that same idea is how how robots would be to move sensitive is radio mixed to the input data Levi is it?
We, we've all seen the studies in deep learning where they have this really robust, deep learning network supposedly, and they give it an image with a little bit noise and it goes completely bonkers. Right?
So, the difference here is potentially with radio mix, we might have a way of of of the efforts, going on in deep learning. These are really trying to figure out ways to make the networks more robust.
But I'm saying those kinds of efforts have to be undertaken in video links as well to figure out what are the to make the more robust response. Then we get to that truly predictive assays that, that we're all looking for.
A quick question. Yeah, this is Fred says, really interested in your approach to identifying issues with the data quality, and trying to improve those.
And I absolutely agree with you. This is the central problem that we, we all face when we try to use particularly large collections of public data, but you are working on, by specific point, has to do with cleaning up narrow imaging data.
So I was interested in your approach for doing that. I would've used fix to solve the same problem, which characterizes the, the image quality issues in a different space and in an icaay space.
So what's your thought on how do we come to some standard or some agreed upon this is the best way of solving this problem? That's that's a good question.
I think the the catches, we all have our own tech pipelines and our own back to, in some sense. So, you know, you spoke via facile fixed, you know, there are, in the new imaging space.
There are tools there are so many different tools, which is a tool that works not similar to ours, but along similar lines.
There is the fish there's another piece of for base, too that I know about the lab. So there are few these tools that exist out there to try and do these kinds of quality assessment. I think the, the, what?
The, the place we're sort of getting lost on is a lot of the tools end up going towards the idea of just trying to find good quality.
And I think, you know, just sort of say, okay, I have a thousand data since one hundred data set, which eighty data sets in them are usable in, which twenty are not usable.
And I think the fact of the matter is, for some of these problems, we can't really afford to throw out a data set without, without being sure that we can somehow make its quality more consistent with the rest. So, then quality.
So,
we have to basically come up with a way to say,
okay,
I have a,
I have a cohort I've,
I've not defined the performance,
the sort of the quality bounds of my cohort,
if you will like this is the,
this is the level of noise and no majority and so on that I have in the squad and now I'm running my predictor.
And how did I do it? It didn't do so well, or did well for these reasons. Okay. Great. Now, can I, if I get a new cohort, can I somehow match these performances so that I can now the tools, Baltimore than life? Remember that tested the tool in a particular setting?
Can I now confirm the tool continues to as long as I keep bringing it back the setting so I think the the should be answer your question is,
I think,
where we have to focus is more on the idea of,
you know,
rather than focusing on,
you know,
just purely the processing or just purely the,
the specific tool we're using for quality assurance.
The key is to try and figure out. What are the bounds on our quality that we are working with right? How do we make sure that our tools?
That are images and features are working with a particular level of quality so that we can ensure that all of our things are working better, more consistently. Overall.
And, you know, I'm speaking about the specific you mentioned, that was one of the reasons we developed marketing the way we did is because I was trying to run that I was trying to run some of the schools on the ranking candidates. And I thought there was nothing, they were all very, very specific to brand they all use brain Atlas and so on.
So we had to go in and sort of recreate those a little bit for our, for our application. So, hopefully that answered your question. Yes. Thank you.
Okay, thanks. We're at the top of the hour and if there are no further questions, I'd like to think our speaker doctor. We should not. Thank you. That was an excellent presentation.
Thank you so much. Yes, we'll continue this session with a update on TC. I think John is gonna give that update.
Yes, I'll take take a few minutes to to update the latest developments from tca. Let me share my screen.
Try that again
here we go.
Yes, thanks. So much Dr mishmash for that. Really interesting talk.
And I just wanted to start by showing everyone on the call the actual page on, with your murky quality measures for these three data sets.
The two of them from and cervical and then gbm dataset.
Cp tech,
so you can access the actual output of the murky tool from this page and see if that,
you know,
the exposed batch a picture with what have you from from the murky tool has impacted any analysis that might have been done on these data set.
So, we, thank you for contributing that data in terms of last month. Well, let me get down here.
The one thing we wanted to note was, was that.
The NCI had asked cancer imaging program and tca to dedicate some of this research resources to the urgent need to get data out for covert research.
This isn't a permanent development for tca.
But in the short term,
it just made sense to leverage the capacity that a tca has to quickly curate and present data to support getting these imaging data out data set out and with partners group.
And the first imaging set we were able to host is this data from patients at Arkansas.
So it's a unique data set and has viral genetic data associated with it as well. So we encourage people to take a look at this.
But, in addition to this, we haven't yet announced publicly, but we've just posted the second Coby data set. This is the page for that. This is from the from patients in China.
That were the data was collected at the clinical center on collaboration with Dr Brad wood. And others and get on his team.
So, this data now is also available in nifty format, six hundred and thirty two patients data with quite a bit of meta data as well.
Over the last month, or so also there's been a number of major upgrades of existing collections the L. G. G. one P.
nineteen to deletion data set has got brand new augmented segmentations to the entire T to signal format that's been added to the page for that collection.
And Additionally, there's been a new analysis data, a data set, added to the oral dataset that through infusion, arrested, cavity, segmentation disease, long data set.
So those new segmentations are now available. And there's a, quite a few corrections of the previous segmentations as well.
We also wanted to remind people that is partnering with the integrated canine data comments,
and we hope to have the first pilot glioma dataset from the canine data comes project posted on in coming months.
We also are announcing the brand new dataset head. Nick sarcoma. M. I. M. I. C. comparison data set.
Comparison to American essays in the coma.
They provided to the group is provided standardized data set over eight hundred images that can be used for calculate calibration and comparison of different methods.
And finally,
just today,
the CP tech lungs,
cell carcinoma data discovery study was released,
by the program is partnering with the program to make available the radiology and pathology images in that in in synchronize with their releases for these discovery data sets.
So, this data, the imaging data that we've been able to collect for these, this new data set that's been released can now be accessed on tca and matched to the protea genomic data that's available on the other CP tech portals.
And, okay, well, that's that's my update for the day.
Great Thank you, John. I'll turn it over to Julio. For announcement of upcoming presentations yes let me share my screen.
Does is always a quick reminder that invitations to the community call. Go out.
We are the Google more information about the call on our I will also make the recording of today's call available on the wiki and send an email via the Google.
Our next calls are scheduled for October fifteen. Th.
Response time,
and our team are going to present key ops on November second we're going to have the more presentation by and his colleagues and for December seven we have
scheduled a presentation of the tuberculosis bio portal by Dr.
intolerant heart. We hope you will, we will see you at those calls came back to you. Thank you. Lead. These are exciting presentations coming up. Okay.
I just want to thank the speaker for next one presentation, and all of the attendees, and also Who'll be for managing these webinars. Thank you again and have a great rest of your day.
Alright, thank you. Bye.