Okay, um, talk to Nordstrom would you like us to wait a few more moments?
For other people to join, or it's entirely up to you my dear, whatever you think.
I would say, let's get started.
Let's get started then and this is a great 1. um, it's my real pleasure today to introduce our speaker Dr Thomas shanka law. He's professor and chair of the center for computational oncology at the University of Texas at Austin.
I've had the pleasure of knowing Tom from his days at Vanderbilt when he was on a part of the team that was with a quantitative imaging network, even served as the chair of the executive committee during that time.
The overall goal of Tom's clinical research is to improve patient care by employing advanced imaging methods for early identification assessment and prediction of response to therapy and cancers.
He works for the at the intersection of imaging and Informatics by developing tumor, forecasting methods, integrating advanced imaging, uh, technologies with patient, specific data, and building predictive.
Multi scale physical models of tumor growth.
With the purpose, of course, of optimizing therapies for the individual cancer patient.
It doesn't take long to learn that Tom is very dedicated to his work and it's my pleasure to introduce him to our community call today. So Tom, please take it away.
You on mute, uh, Tom, we can see the presentation.
But we cannot hear you yet. There we go. Can you hear me now? Yes, yes.
Fantastic, I am, um, I am a Webex novice, but thank you so much for that introduction. Dr Nordstrom. I I hope I don't disappoint. So, um, I was told, I had 35 minutes and so I tried to make it 30 minutes with the understanding that nobody has ever upset.
If you stop early. So, what you're looking at here is a, um, a prediction of Hurricane Sandy from 2012. it's projection.
Um, uh, 72 hours out for how it was going to impact the Northeast core door. And what was really interesting about this prediction. Was that 72 hours in advance.
Where the eye of the hurricane made landfall was actually correct.
To within 72 miles, you can imagine that this sort of predictive accuracy.
Is enormously important if you're trying to figure out how to evacuate, um, a 12 to 15Million people.
So, I'm, uh, I'm trying to learn some lessons from meteorology here and apply them to oncology. So a weather forecast is an estimate of weather conditions at some time in the future and there's basically 2 steps, and their names are sort of instructed for us. Right?
The 1st step is to estimate the current physical state of the atmosphere, so pressure gradients and humidity gradients and wind speed and all those kinds of things that's called the diagnostic phase. And then you have to do something with that data.
So somehow use that data to predict the future state atmosphere, and that's called the prognostic phase. And there's basically.
4 main ways that are to do this, and I promise if you're just joining, you're not joining the wrong seminar. I'm coming at this from a, uh.
What I hope is seen as a clever angle. It is. All right so the ways to make the prognosis, that is the forecast there's persistence forecasting and that's where you say.
The conditions that exist right now are going to continue to persist for some time in the future. And that's that works for very short, um, uh, forecasting times in the order of tens of minutes.
Then there's climate to logical forecasting methods where the idea there is that you look back at the climate record and you say all right we're at this time in the in the in the in the year and so climates. Logically what happens on this day is X and that's your prediction.
Then there's the analog method, which is a variation of that, where you sort of look at the last 30 or 60 days of the weather. Then you go back in the climate record. If you try to find 30 or 60 days that are similar to the ones that you just experienced and then you go to that 31st or 61st day.
And you say that's going to be the weather that we have tomorrow. And then there's the trend forecasting where you say, okay, the weather system is moving north at 15 miles an hour.
So, within an hour, the weather system that exists here is going to be North 15 miles.
The problem with all these things is they can't account for changes in Tennessee or direction, and they can't predict the formation or anticipation of major events over extended the time. Sort of. What you have is is what you get.
So the current methods for predicting the growth of responsive tumors, and patients are sort of akin to these weather forecasting methods that rely on historical data. But they, and so they're going to suffer from similar limitations this limitation over here in this box.
But there's a 5th way, right there's always another way, there's this notion of numerical, whether prediction, and it's based on the physical principles that govern the behavior of the atmosphere, and that can be expressed by system equation.
So, if you were ever dragged through fluid mechanics, you know, these things as the above your strokes equations, right? And so mathematically, you atmosphere is modeled by the state factor called X, and there's any of these important parameters. The pressure gradient.
You may be gradients the temperature gradients, et cetera.
Of those model variables in their grid points are at some initial time. T0. That's our diagnosis. And we get a lot of this data from satellite. We get it from the next generation radar systems that exist across the, um, um.
Across the across the country, and then you use that initial information to put it into your Bible equations, and all of your folks in this case to predict the value at some future time piece of it and that's called the prognosis. And so it's sort of natural right?
Why not do this for oncology? Right? We can make these patient specific measurements on the individual using advanced imaging and let's build a mathematical model that can take those data and make a prediction into the future.
So, hopefully, now you're all on the edge of your seats, and I can tell by your list of names because I can't see anybody that you're all. You can't wait for the next thing that's going to come out of my mouth.
So the next 30 minutes, or so I give you some quick results from stuff that you guys have probably know backwards and forwards. And then I'll try to give you an introduction to physics and biology based mathematical models of tumor.
And I might say some things that pull some feathers, but I'm just trying to.
Keep the interest level high, and then we'll talk about how we can use these things to do digital twins and equals 1 clinical trials. So, just a couple of slides, and some quick results on in the future grade in the context of breast cancer.
So, of course, just to we're on the on the same page D. C. is a serial acquisition of images before, and after the injection of typically again illenium based contrast agent. And so if you parked yourself in a region of interest, and you take a bunch of images.
As a contrast agent is profusing and you can imagine following a range of interest or a single Voxel and the time course.
The associate signal intensity time course is going to go up and come down to a degree determined on local profusion and permeability of the vessels. So you fit that data to a pharmacokinetic module, extract a bunch of parameters that can report.
On different type of tissue characteristics and so Here's an example for many years ago this is a responder that initially presented with an 8 centimeter tumor, um, a central cross section here. So, the nipple is here chest falls over here.
Um, and so this is Pre therapy, and this is 10 to 14 days later. So looking at the, the parameter from DC analysis.
If you're familiar with that, it shows a pretty big drop off in the intensity just 10 to 14 days later with really no change in tumor size.
We can trust them with a non responder where, if anything, there's been a, um, um.
Increase in in the in the in the perimeter over time and so this patient ended up being a non responder. This patient achieved the pathological complete response. So what we would call a responder.
So, when you do the analysis on our patients that we had an area to the curve point, 7, 7 for.
I just want you to put that in your back pocket for now we'll visit the fusion way and then we'll put these things together.
And have all the stuff we need for the, for the more interesting part of the presentation. So there's diffusion weighted. Right?
The notion is, you have a cellular tumor here with lots of barriers for free water, diffusion so you have a reduced ABC if you give a cytotoxic therapy and allow the cells died, you get areas of necrosis and your distribution of ABC shifts to the right here.
To higher values, and you can see that here when mapping the ADC across the breast right here.
That were the tumor is is much higher has a much lower ABC and interpretation is a much higher cellularity in the region below it in the fiber glandular adipose tissue.
Same idea as the previous slide, you have a responder and a non responder and after 10 to 14 days on on therapy, if anything, there's been an increase in the M, ADC over here.
So, you interpret that as a decrease and cellularity and sort of the opposite in the non responder. So when we do the analysis on this, we get a point 8, 1 value.
Same patient said, so the notion is, or the next step is sort of obvious you get pretty good AC values for each 1 of these parameters on their own.
So, then you should probably combine them and so speaking of, um.
From a high level here, if you combine the measures of profusion and cell density, then he could correctly predict who's going to achieve a pathological complete response.
After the 1st, cycle of new adjuvant therapy about 88% of the time. So that's really good. Um, by compared to having to wait till the completion of Neo adjuvant therapy, and seeing what the change in tumor sizes there and so this, these sorts of results have been recapitulate in lots of different studies.
But the key thing here is that these parameters on their own, because they have this nice statistical correlation with response.
It sorta gives them nice motivation for using them as inputs into a mechanism based model that's built on the underlying.
Biology and physics, and so just a sub, like, whether it's satellite, provide the data for weather forecasting, we want to use quantitative imaging data to provide the input for the tumor forecast.
So here's a a global humidity map that goes into a weather model to make a weather forecast.
We want to make these measures measurements, the profusion and celularity going to a tumor model to make a tumor forecast. And so the rest of this, uh, my presentation is is how we actually do that.
So, is everybody pseudo believe motivated and excited to see what's going to happen next?
I can tell by your list of names that you're all very excited about this.
Can anybody currently hear me just to show up hands, perhaps this, all just a M, a, and an elaborate psychological experiment to see who Tom will talk to if he thinks he's talking. All right there's some very good. Thank you. Very good.
That's all I needed 3 thoughts. All right here we go. All right.
So, I really hope I don't wrestle any feathers here, but it's designed to try to peak interest. So statistics statistical inference of course, it's enormously powerful.
Nobody can deny this, but it relies on properties of large populations that can sometimes obscure conditions specific to the individual.
So, high consequences decisions, we have to begin beyond just using data analytics so our decisions must incorporate bio physical processes that can be calibrated.
With patient specific day, to make patient specific predication predictions. The whole notion here is that each individual has their own set of characteristics and while we can use some things from the population to train certain models.
There's certain characteristics of those patients that are going to be lost in the avalanche of data from a population. And for important decisions.
And such a heterogeneous diseases cancer, we have to focus on the individual.
So, we posit that this transformation from population to patient based care is inevitable. And of course, it's happening.
So, how do we do this? So I want you to go back to your mathematical biology 101 class that I'm sure all of you took if you open up the textbook to section 1.1 the 1st thing they have is exponential growth. And so we're just gonna try to build up a practical mathematical model from Square 1.
So, and here is the number of tumor cells that exist the time team, and then zeros the initial numbers in case of proliferation. Right? So, the rate of change event is equal to just the K times. How many cells are there at that time T.
you can solve this in your sleep and so you get this exponential growth that happens over time but of course, this isn't what happens in vivo in vivo. There are different restrictions. There's spatial restrictions and so you can't just grow exponentially.
And so what happens is this exponential growth is damped logistically. So we let them be the carrying capacity.
As the end gets close to this carrying capacity data,
this number gets close to 1 so 1-a,
number,
that's close to 10 and the rate of change,
then goes to 0 and so your carrying capacity levels off asymptotically to this value data and you,
you see this ambitious,
you know,
you can have them,
you can put sales in addition to your will grow exponentially at early times until.
The, um, the cell number has sort of played at the surface and then they level off and they can't grow anymore.
Sometimes even see this in mice, but these models do not describe spatial variations of the tumor growth. This is all just temporal variation.
So, we have to go into include spatial variation and the next thing you do is you put on a diffusion diffusion term here. So this is diffusion of tumor cells. No longer diffusion of water from our.
So, if you read this from left to right it says the rate of change of end with respect to time is the rate at which, among tumor cells are diffusing outward plus the rate at which they're proliferating. Logistically.
So, we've gone from exponential to logistic growth to now a spatial temporal model of how tumors grow. That's determined by the, the random diffusion of the tumor cells and their proliferation.
But, of course tumors don't just expand in a radius symmetric manner. So what we do is we start coupling the diffusion of tumor cells to the surrounding mechanical properties to tissue in which it's growing. So different pressures that exist in and around the tumor causing different stresses.
And the tumor cannot grow equally in all directions. And this is this has been known for a long time. So we have our reaction diffusion equation. This is the same thing as this guy.
But now we're going to adapt the diffusion exponentially by this thing called, which is basically just a way to turn the call. She's stress sensor into a scaler.
So, in regions, where there's lots of stress, this diffusion value is going to go down and the tumor is not going to go as far in that direction as into an area where there's less stress. And then there's a.
Fancy way of writing ethical here that balances the stress the convergence of the stress tenser with the gradient of cell number here.
I'm, uh, right so this just isn't words what I said before.
So now, the idea is to take this model and calibrate it to imaging data. Mm. Hmm.
The last thing we do in this model though is add in a treatment term explicitly. So we know what kind of drugs are given to the patients. We have estimates of how, um, what their half lights are in the body from phase 1, clinical trials.
We want to try to calibrate our model to the data that we acquire. So, here, this is our movement term.
This is our proliferation term and this is our treatment term.
So the things we don't know a priority are this D this N.
This K, the state of and this alpha, so there's a lot of things going on in here. We get the betas from, um, from clinical trial data on how long these drugs stay in the system.
But we do have some measurements, right? We've measured things to tell you about cell density and measure things that have to do about, uh, uh, profusion.
From the DC, so what we do is we take our infusion data and use it to estimate the value the distribution of N.
Over time, the data is the carrying capacity sets the maximum number of cells you can cram into a region of space. And so what we do there is, you know, the voxel dimensions, and you have an estimate of the cell size and so you just use the magic of arithmetic to come up with the value for data.
And value here is this estimated by our diffusion measurements.
In our proliferation rate is estimated along with these parameters alpha here by taking 2 measurements 1 at baseline.
After the 1st, set of therapy, we calibrate our model to these 2 data to get the parameters that we don't do measure measure directly measure from the imaging data.
So,
once again,
the end is measured directly from the imaging data,
our concentration of drug is estimated by the profusion measurements I'll have more to say about that later and then the,
uh,
the values for K and alpha are estimated from from sequential data.
That's used to calibrate to this model.
So, what you do is your scan 1 and scan 2 you're looking at the cradle huddle view here and so you calibrate this model to this data and you'll have some proliferation map after that 1st, cycle of therapy. And you'll have an estimate of the drug distribution.
Which is changing in time that goes to make a model prediction that you can then compare directly to what happens at another time point here. And so if you look closely at this, you'll see in some places, we do pretty good.
There's areas of increased sale number here, which is predicted here and seen here there's areas of reduce cell number there. That's that's manifested here in the measurement over here. And then there's some areas where we don't match up very well.
So the size and shape is match pretty good and in some areas, we get a pretty good match of the cell density and others. We need to improve the model.
So here is a very recent study like, this is, like, held off the press a week or 2 old. This is working with the triple negative breast cancer moonshot data from, um.
Md Anderson, so here is the observed and predictive tumor values. The, the red is what was observed at the conclusion of chemotherapy. This is a triple negative breast cancer. So this Adrian license I talked to and then, here's our predicted value right here.
And you can see, the overlap was pretty good and in fact, when you put the metric cell count versus the predicted.
The concordance correlation coefficient is is quite high. The concordance correlation. Coefficient is just like the Pearson except for there's a penalty if you're off the line of unity. So, these values are are quite good.
Our predictions are matching pretty good over the whole 50 patient dataset with what's actually mentioned.
And we haven't completed the whole, um, analysis put amongst 33 patients are areas of the curve.
For predicting versus not path residual disease is again about 88%. So this is this is pretty good.
So, we think we're getting pretty good at predicting the spatial and type of the amount of these breast tumors in the new, the new adjuvants setting.
We do think we need to come up with a better way to estimate to the drug delivery. And so here's our next little foray into, um, a mechanism based models.
So, we're going to use imaging guided computational fluid dynamics and this is, um, about.
2 thirds of the way through the spiel. So this is where I'll check in and say, can I see some more hands to see if people are tracking with me if there's any questions or anything like that?
Questions or comments cries of joy or shouts misery.
It's all the same to the torture. All right.
See, half a dozen or so hands. All right so, here's the physics based model describing blood flow, interstitial transport and their interaction throughout the entire breasts. So the nipples over here.
Chest wall is behind the, the screen right here and there's a lesion here and these are the large vessels that we can segment on a high resolution contrast and enhance them are before. And after contrast what's delivered.
So, we're going to model a blood flow through the vessel network through this vessel network through pause these law. I'm certain I'm saying his name correctly. I've asked many people how you say it seems to be a lot of valves in here that don't need to be there. But, um, I think it's policy.
So this is my Visa, the flow through the vessels. I'm, uh, directly proportional to the race. The vessel equations been around for.
1st century or more then flux across the vascular walls. This is described by Starling, slow and interstitial flow via Darcy flow. So this tells us how the.
How the drugs this is our estimate for how drugs are being delivered through the pipes this is how they're getting across the pipes walls and this is how they're distributing once they're in the extra vascular space.
So, we calibrate these mathematical model, this mathematical model against patient specific data, to infer the unknown parameters that are in this model. This is a paper that we are really excited to have published in number now, a couple years ago about a year and a half. I guess about a year and a half.
And so when you do this, you get access to these data that you didn't have before Here's our reconstruct ambassador and she provides the same picture on the previous slide.
Then we have our masks map, a vascular permeability. Then we have our tissue hydraulic conductivity the pressure within the vascular network.
Some of the pressure on the surface of the tumor, and then the flow within the tumor. Um, but this is and this one's pretty cool.
Because this is clearly the input for the tumor and this is clearly the output. You actually see that here on the, at the, um.
Sort of anatomical level, the input here and the output down here corresponds to this instance, this is a vector field. You have both some information about direction and, uh, magnitude of the flow feeding this tumor. And once you have that, you can make these estimates of how.
The drug is being delivered interestingly so, this is within the pipes.
If you will, I'm not being too crude there and then, of course, how it flows extravascular is.
So, with this estimate, we can get a pretty good.
Or, with this, uh, analysis, we get a pretty good estimate. We think of how, um, of how drugs can be delivered to the patients.
So we want to use this to do this digital training stuff.
So put this this nonsense here in your back pocket,
we're going to have a couple of slides on digital training and then bring it back in to show how we can use this nonsense along with our mathematical models from the previous slides to make predictions on a patient specific basis,
and then try to use this to optimize therapy on a patient specific basis.
All right, so Here's a mathematical abstraction of a digital twin. It's associate physical asset. This is a fantastic paper. This is written by the Karen wall. Wilcox is the director of our computational Institute and her team put this together.
It's, it's just a lot of fun and we're very much worth a read. But the idea here is that you have, unless she's an aeronautics. So her examples from.
Complaints the idea is you have a physical asset, the thing that actually exists in physical space. Um, and you have these 3 components you care about the control inputs.
The physical state, and the measurements you can actually make so, for her, she's looking at, you know, the measured strain or accelerometer data because she wants to look at things that have to do with whether or not um.
She can have her planes go where it's supposed to go with the Amazon package is going to be delivered where it's supposed to. So you have digital the digital version of all the things that actually exist in physical space up here.
So your digital state are parameters that define the computational models that comprise the digital twin. So, this is the things that actually go into your model. The reward is this is quantifiable. Overall performance. So did the mission wasn't successful.
How much fuel did you burn the wings? Need to be inspected for maintenance, these kinds of things and then there's the quantities of interest for them. It's stress strain, displacement, fields, failure, stress, remaining useful life. These kinds of things.
So, the next slide is going to take this sort of abstraction of digital twins and apply it to our case. We're trying to predict.
The response of breast tumors to new adjuvant therapy, but these are these, these, these notions of the physical and digital state still play a very powerful role for us.
So, here we go, here's our physical state.
So these are, this is the actual anatomy more fail, morphology and mechanical and physiological state of the regional tissue under investigation. For us. It's the breast tumor.
Then we have the observational data, the things we could actually measure through so it's anatomy, it's profusion permeability, cell density metabolism.
Perhaps, if you're looking at FTG pet proliferation, if you're looking at sequential data or targeted them, uh, targeted tracy's from pet or perhaps microscopic technique from EMR, et cetera, those are the things you can actually measure.
Then there's the things that you can control when to do your days, when to take biopsies. And at the end of the day, you want to optimize your treatment.
So these are the abstractions, these are the things that are going to be abstracted in our digital space.
You have your domain that you get from segmenting the high resolution images to build your final element mesh and assign the boundary conditions.
And the parameters that you measure, the things that measure the mechanical properties, your treatment regimen, the things that are input into the system.
Then you have your quantities of interest, the distribution of therapies, the shape of the tumor, how the cell density is changing over time.
These are all sort of the digital analogues of the row above them of the item that's directly above.
And then you have the rewards what we want to be able to do is maintain tumor control for, as long as possible by controlling the dose and the, and the scheduling of the treatment, which are the things that you control. You don't have very much control over these measurements here.
This is what nature gives you.
So, you start with your physical state, you make measurements of that that then gets fed into your digital state to populate it on the computer. So that you have, in some sense, an accurate representation of what's actually happening in the physical state of the patient.
You input the measurements that you make that are describing that you get from your observation data.
And then you try to do some sort of optimal control to be able to to control maintain tumor control for, as long as possible. And then the whole thing iterates, until you've done this properly.
Dive in a little bit more on this box here.
So, we use our fluid mechanics analysis to try to come up with an optimal therapeutic regimen. So, this thing's going to cycle around in a moment here.
So, that big bullets that you see, that's a single injection of Adrian toxidant their 1st, therapeutic delivery to the patient. And what you can see is, there's initially a very high concentration of the drug and then it dissipates rather rapidly.
So, the question becomes, everybody knows, everybody knows this is not the best way to give therapy, but there's not enough patient and resources and clinical trials to sort of explore the solution space of all the ways. You could give Adrian license. I toxic attack. So.
Doing a triple negative breast cancer patient. So the idea is that you can build a model, a mathematical model that gives you an estimate of how drugs are being distributed and then you can go on the computer and try a bunch of different options. So here's a multiple injection same total dose, but you can see it's now pulsing.
So over time, the concentration in the patient's tumor is higher on average overtime.
Then in the individual injection, so if we could go and look at this a little bit closer.
Hopefully, it will advance. Yes, here we go. So here's the change in Doug drug concentration from a single dose. Just zooming in on the tumor right here.
And so this is the concentration in the blue right here it's high initially very high and then it dissipates rapidly. Due to the pharmacokinetics of the drug.
And so that corresponds to this, we've just pulled out the drug here. The red is when the concentration is highest and then the size is changing over time.
So, if you can trust that, you can trust that to the change in drug concentration for the multiple dose.
You see, that the initial dose is not nearly as high. The initial concentration is not nearly as high, but if you're doing a pulsing and then the concentration on average is higher inside the tumor then the surrounding tissue and the area underneath here is bigger than the area underneath here.
And you can see that you get this pulsing. The tumor is shrinking more here than it has here. In fact, it shrinks by about double. So, here at the end of tumor, it's about 10 cubic centimeters in here. It's about 5 cubic centimeters. Now.
This is all in simulation land, but this guides.
This provides motivation for guiding prospective trials where you could potentially alter.
The way that you're giving the same therapy, um, in the individual patient to, to achieve better tumor, controlled, given the, the individual vasculature of the of the patient.
So, we actually did this sort of in a, in a small group of 12, triple negative breast cancer patients.
I'm a simplified version of what I just finished showing you on the on the previous slide.
But the results are kind of incuring, uh, kind of encouraging, and sort of point to wanting to perspective trials.
So each 1 of these little on the horizontal axis here you have patients, there's 11 of them and then this is the percent change and tumor from Pre therapy to the conclusion of need your adjuvant therapy.
So the blue line, or the blue dots are what the patients actually achieved. So when there's a blue dot here, there's been very little change stable disease. When the blue dot is down. Here. You have a partial response, a 50% decrease and over here, you've got, um, a 50% increase.
So this is progressive disease. So we did this thing where we took the AC, and we alternated on the computer.
How this was being delivered either the bolus doses that were given separate by 3 weeks for the standard regimen, or we cut in half and did the half dose every week and a half or a quarter dose. Every, um.
3 weeks divided by 4, et cetera, tried all these things on the computer and found different better alternative doses for each 1 of these 11 patients. And you can see in some cases.
There was no improvement. So, like here, you know, very little improvement, very little improvement here here, but in some cases, there is a sizable improvement in particular for these 2 patients. We went from.
From a 50% growth in tumor to stable disease, and almost know.
Change the tumor size, so on average differences in reduction and tumor size between the standard.
And the optimal alternative regiment ranges from 0 to 50% of an average of about 21% and this is just changing the timing that you're giving.
Daydreaming talks, and it's not changing the order with them uh, Adrian license approximate and, um, uh, or anything else is complicated like that.
The other key thing to take away from this is that each patient's optimal therapeutic regiment was different, which makes sense, right? Because no, 1 person's piping and their tumor is the same as somebody else. And so it makes sense.
That the best therapeutic regimen for Miss Jane Doe a, is not the same that's therapeutic regimen for Miss Jane Doe um, a B.
All right, so I'm, I'm looking at the clock here at 631 minutes so I think we're.
We're right on track here for for 35 minutes total our summary we have to get beyond just assessing response. You know, the resist criteria just assessing that. These 2 lesions have grown over time.
Is not where we want to be so we want to get to this notion of being able to do prediction and we refer to at the very beginning to, you know, trying to steal some ideas from meteorology.
The idea of forming a diagnostic phase of collecting the data and then putting it into a prognostic phase to make your prediction. Your forecast. We talked about the importance of doing physics and biology based modeling to capture the spatial and temporal dynamics of the of the tumor.
And then we talked about a physics based fluid dynamics modeling of how drugs are being delivered to patients and found that, you know, giving it perhaps more frequent doses, more frequent dosing at lower concentrations.
At least in this preliminary work is pointing to a better tumor outcome.
And the digital twins, plus physics and biology based modeling provides practical means to optimize.
Patient outcomes for the individual patient and then I'll just conclude here with something that is I have to say to some audiences. Probably not this 1. but this is more than just computer games.
It's more than just doing physics on a computer because it makes cool graphics. And it's fun to do.
Cancer, of course, is not just 1 disease right? Depending on how you parse it up. It's somewhere between 102 and 200. so all of them, it just doesn't get cancer. She just doesn't get breast cancer. She doesn't even get just triple negative breast cancer. Right? There's 6 currently, no subtypes of triple negative breast cancer.
So there's no way that the 8 weeks of AC followed by the 12 weeks of tax or whatever, it is 12 and 8, depending on where you are in the trading position. There's no way that that's the best thing for all pop for all patients.
So, we have to get to get away from doing assessing, based on population averages to predicting based on the individualized.
These are the people who did the most of the work, and the studies that I showed today. Um, uh, I, I can't separate. This is now 2 years old, but I can't replace this live picture with a zoom screen capture. It's just too depressing to me.
So this is what's.
The lab looked like 2 weeks ago they do all the work. Of course I just sit in my office and worry about funding but thank you of course, to the patient to agree to come onto these studies for us.
Back in the day, when I actually used to run the scans myself, I always ask ask the patients why they were participating and they all universally said that they wanted.
Health care better for their children than themselves and I, I just thought that was.
That's a real impact on me as I'm sure it has for you guys who have had similar.
Situations all right so thank you very much for your time and attention. If you're into this thing, we have a Twitter account and Emma here is our webpage. So thank you very much for your time.
And, um, I'm, I'm happy to answer questions. Do you have a full slate for the rest of your.
I'm a meeting, I'm happy to get out of the way whatever you'd like. Oh, please questions as everybody can see, your enthusiasm is unbridled. Thank you. Thank you for all of that. Including the weather forecast there.
We have a lot of questions here.
Yeah, uh, this video it goes into the high Tom pretty impressive. I mean, the.
The last slide would you shout you know, about the improvements.
When you are trying to tailor the treatment, uh, individually, I mean, that that is pretty powerful.
So, how many how many parameters.
Going to, you know, making improvement like that happen.
And would set of these parameters, which is used in your modeling very, depending on the disease.
Yeah, so that's an excellent question. There are about 4 parameters that we have to calibrate. We tried very hard to build the most parsimonious models as possible, which is just a fancy way of acquiring Einstein.
We want to make them as complicated as necessary, but no more complicated. So, every parameter is in the model, we tried very hard to make sure that we can either measure it directly or calibrated from the measurements that we can make. So, there's very few stuff.
We have to, uh, to, uh, to pull from the literature to assign. So it amounts to about 4 parameters that we have to calibrate 1 of those parameters. The proliferation is a field.
So, that means we have to calibrate it at, at every voxel within the tumor.
Sometimes we downsampled, so we're not having to calibrate as many parameters, but we have a lot of data in that situation because we have multiple time points of which each 1 contributes boxes within the tumor. So, for parameters 1 of, which is a field.
So, um, so that's tractable. The 1. that is the most important for this is that proliferation parameter that's changing as a function of how the drug is being delivered. So, the 2 things that are most important to doing.
This are the 2 that I highlighted your estimate of, of tumor celularity and your estimate of how the drug is getting there.
So, in the interest of making sure the full disclosure.
Our estimate, you know, imaging is great, it's not, it's not invasive and you can do it multiple time points, but since it's those things, it's not.
It's not the gold standard, right? So, our estimates of tumor cellularity are are are good forced to order of estimates because they come from distribution.
And if everybody else knows on this call, there's lots of other things that go into diffusion measurements besides just cell numbers.
So, that's the thing and then our estimates of how the drug is being delivered, it's built on contrast enhance and there's probably a pretty good correlation between how I believe is reaching the targets.
And in the small molecules, but for bigger things, uh, like, um, a targeted, uh.
Targeted, uh, like, trustees, a mapper acceptance. The, our estimates of how drugs are being delivered based on the guideline is probably not as good. In fact, it isn't our predictions and triple negative breast cancer is much better than it isn't her to positive. We think almost. Certainly.
Because our estimate of the tumor.
Of the delivery of the of the drug to the tumor site is not as good her 2 positive cancer as it is in triple negative. So that was a long winded way of saying the most important things are your estimate of celularity of delivery, and there's 4 parameters that have to be calibrated.
Okay, so anyway, the, the treatment needs to start.
Because you're 1 of, you know, some of the parameter is how the drug is being already delivered and then you eventually might modify it.
You cannot make this decision ahead of starting the treatments.
So, that's that's I really appreciate you saying this. Yes. So for many years, we've been doing this baseline and then after 1 cycle therapy so that we can see the effects of the intervention to calibrate the model and then run it toward.
And then do exactly what you say, update it in a sort of a data assimilation approach. Exactly. How whether forecasters do it, it's more data come in. Um, we've been trying.
We've been trying very hard. I think we've hatched a plan, but we haven't even gotten to the point where we could submit it to a conference on how I'm trying to use trying to marry machine learning with the mechanism based model. So you have your population stuff that she used to initialize.
Some of your key model parameters at baseline to make an initial prediction that you've been rapidly update as you get your patient specific information in. So you can make a prediction from baseline.
If you rely on,
um,
a population and a machine learning approach where you take the individual,
you're trying to predict for and then use different kinds of basically dictionary lookup or matching things between this individual and your training population to initialize the model and then update it with their next batch of patient,
specific data as soon as it comes in.
Sounds great Thank you. Thanks for asking. I appreciate it. Yeah.
Do we have questions in the chat box? Yes, we have 3 questions in the chat box and, uh, Emily asked the 1st question and Emily if you would unmute yourself and just ask the question, please.
Hi. Hi. Hi, Tom. Um, great talk. Yeah, so, um, I guess well, I put in this question before you got the digital twin section, but I think it's applicable there too.
I'm just trying to get a better understanding of how generalizable the, um, the models are.
And,
you know,
if you,
I,
I have not read the,
the,
I triple E publication,
but,
you know what types are classes classes of drugs were used in that I guess the generalizability I guess,
I think for leads and my question to the,
to,
to the tissue.
Type as well. Um, so if you could just yeah, I appreciate this question. Yeah, the, I'm, uh, the 1st, 1 is about how generalizable is to the tissue type so this is the beauty of mathematics, right? The equations are agnostic to how they're being applied. They have no.
No knowledge of of what tumor type they're being applied to to so.
Where we make these, we make the claim that it can be applied to any tissue type for which the requisite data can be acquired.
So, if you can get the quantitative and quantitative diffusion and high resolution spatial information to do the segmentations and and to set your your computational domain, then it can be applied.
So, the areas that we've had the most success in are zeroed in on breast today. But, we've also had really good success in the brain and prostate and we're trying to do this in other areas. We'd like to work in the Neo adjuvant setting because, you know, it's it's a tight timeframe.
And then you have tissue at the end. So, you know, if you did it, right or wrong so we're looking at expanding this to erectile cancer.
We'd like to work in the pancreas increases. Probably everybody on this call knows is.
Incredibly difficult to image quantitatively. Um, uh, but so, um, applying it is, um, uh, it is very generalizable brain breast and trust is where we've had our most success. Um, but wherever you can get the data, it, it, it, it shouldn't work equally.
Well, the, um, my other question had to do with, um.
Had to do with your availability for different drug types. Right?
And so, that was what I was trying to get at, um, in in my previous responses that our estimate of where the drugs are getting is, is tied intimately to where the headline based contrast agent is getting.
So, and that's a that's like 500 dollars. So 492 dollars or something like that. So, I think it's probably pretty good for the small molecule drugs that we, that are used Adrian license side talks and texts. All these small molecules are probably pretty good, but for the.
The, the larger targeted, um, uh, antibody based things intercept and just Susan patent these kinds of things. It probably doesn't work as well. And that's been born out in our.
Predictions where I'm doing much better for triple negative breast cancer, and we are for her. So for the bigger drugs, I think we have to come up with a better scheme or more intelligent scheme for estimating how the bigger drugs are delivered.
And that probably requires animal work or if maybe someone, maybe you have a clever idea on how we can.
Perceived there Thank you.
Yeah, thanks for asking. Hmm. Um.
The next question on the chat was from, and I hope I pronounce this correctly. Yes, he pronounced it correctly. So this is about time I'm a fan of physics based simulation, but often, you know.
We need the results before reality arrives. So, are there constraints you, you did not mention simulation times, or what are the constraints in this setting? Yeah, so the simulation time thanks for asking that. We actually.
At the risk of giving a shameless plug, we just published our whole, we got to publish our whole EMR pipeline from data acquisition to post processing to visualization to prediction.
It was just actually published last at the end of last year in nature protocols.
So, the whole thing is there, and the computational time is really almost entirely done by calibration of the model all the Pre processing stuff that's liquidity split. Um, and it depends on the size of the tumor.
So you're talking about a couple hours.
To 12 to 14 hours for a really large tumor.
Thank you. So that's not that's not a that's not on a beast machine. That's not a machine with maybe a dozen or so processors.
Okay, thanks. Uh, the next question comes from then.
Then, do you want to ask directly? Sure sure. It was, uh.
Forgiven ignorance on my side, uh, you ended with showing the performance of your model and with expected benefits. Have you applied these algorithms to to adjust real dosing profiles yet?
And it's not just what's stopping deal yeah, I really appreciate this question. This is, um, um.
The short answer is no, we haven't applied to perspectively. The longer answer is, I think we now have enough data to show that we can do this because we've done it. I'm a.
Our predictive accuracy has kind of been right at this 88% value from our studies at Vanderbilt 2 studies with other collaborators to our study that were just completed with MD Anderson. And I think we have good collaborators. It'd be interesting.
That are that are willing to now prospectively on a small scale have us do this calibration after the 1st cycle therapy, make a prediction of response. And if they're not predict to achieve a path, which is going to happen between 25 and 33% of the time.
To alternate the way that we're giving the therapeutic option of giving the, the dosing to them. I try to enhance some tumor shrinkage. So I think we're right at.
That I hope to initially to answer your question, I hope to initial initiate that paperwork this calendar year and it's very exciting. And of course, it's also very terrifying. It's exciting because you're gonna be affecting patient care. It's terrifying because.
You're going to be affecting patient care, so yeah.
The next question comes from cave on.
Thanks thanks, Tom. That was a fantastic presentation. Um, you may have.
Address my question in response to ZFS. Uh, but, uh, I'm just wondering, how does the model take into account the ever changing biology and the dynamics, or the tumor monitoring environment? Right? Not s, there's not I mean, the system.
Changes or against the subject doesn't change, but the environment changes. So does the methodology, but in in.
In the disease setting the patient, also it's changing.
It's true, and, you know, at 1st, I, I used to hide behind the cloak that, um.
That they have better data than us and so their data's constantly being upgraded and to some extent,
this is true,
they can put sensors on the,
on the wings of a plane,
for example,
but those sensors quickly become too heavy to really put them at all the places you want to have it on the on the wing or the fuselage,
for example.
So they are sort of data starved to although not to our extent the model itself. If you have the 2 time points, it will make a prediction for how the tumors cells are proliferating, how they're, how they're migrating, how they're responding to treatment. So that is being updated.
We didn't talk about it today, but we have an extension of the small that also updates how the, the vessels are being updated, only the big ones. Right? Because we're, we're limited by the spatial resolution that we can get from from our, which is basically a cubic millimeter. So only the large vessels.
And we do have a a similar reaction to Fusion equation that describes how the the vessels are evolving in time based on 2 time points, the baseline and after 1 therapy.
And then you project that forward to your next measurement time port, in which you update the model parameters that point, and then make your prediction going forward. So the models do make predictions of how the biology is changing over time.
That's that's 1 of the really cool thing about the physics and biology based models is that the underlying biology is baked into the system and so to make your prediction, you have to make a prediction of how the biology is changing. Otherwise, it doesn't make sense. It doesn't work.
Thank you.
And so far last question comes from Iliana.
I think you already answered the question that I was initially asking, which was related to the perspective implementation, but since I have my line in the queue, I'm going to ask you a different question.
Um, how the, how dependent on you on the scanner fields threat sequences.
So, I'm assuming you have tested this in 1 system, but should you move to a different 1?
Super appreciate this question, I left this detail out so I'm a.
We actually we're actually doing our own studies here in Austin in the community center setting, so we're not doing it a dental medical school. We're not doing it at an academic research, um, environment with a fleet of PhDs hovering over the system.
So, we're lucky here in Austin, it's, it's a it's a Siemens town so we have developed our sequences on the Siemens on campus.
And we've taken it to put it on 4 different, um, uh, scanners out in the community settings. So we're actually collecting our data and community providers. We're working at with Austin, radiological associates. If there's a, for profits, private practice.
You know, the practitioners, they're not research sites so we, when we scan a patient, we send a post doc or a staff scientist over who sits next to the technologist and then we use the equipment that's widely available in the community setting.
Um, uh, and to collect our data and that's how we run our studies. Uh, we also partner, of course, with academic research centers and, uh, right now, University, Chicago, and be interested in, um, a Baylor College of medicine. Those are.
Our primary academic settings, but in, in that case, you know, we're using standards from all 3 vendors. So I hope that doesn't come across as too used car salesman.
But I am super psyched about the fact that we have been able to get from the bridge, the gap between the academic ivory tower to the community setting because in cancer that's, we're about 85% of people get their care is at the community.
Sending so we've, we've managed to deploy it out in the community.
So, basically, what you're seeing is that you're not very straight, you know, you don't need a certain minimum, adequate time, or a certain PR or a certain number. So that's that cuts both ways. We want to make it as broadly applicable as possible. So, we're not using any whips from any 1 of the 3 vendors.
Um, so, so that's very good for broad generalizability. It does, um, potentially limit you for somebody who comes up with a new whizbang way to do X Y, or Z, because we're trying to make it broadly useful.
That's the end of it.
Yes, that's the end of the questions in the chat. Wonderful. Thank you all very interesting questions. Thanks so much. I appreciate it. Thank you. Yeah, I want to thank you so much for your enthusiasm for the time. You've given us.
Yeah, I guess we're ready to go to the next, uh.
Next up yes.
Uh, next, I would like to give a brief update of what's going on at the imaging data comments and I have a number of links here on the slide, which I will post into the chat in a moment. Uh.
The imaging data comments is moving into the into a more regular data release cycle and,
uh,
in on this link,
you can find the release notes for each of the data releases.
The last release was on January 10th. It was what?
We called version 6,
and 1 of the major changes in version 6,
was that we had to retract images of several head,
head neck and brain tumor collections and retract not in a bad way.
But in order to, uh.
To protect patient privacy and, uh, so these images have been removed from the imaging data Commons, but the associated data is still available.
And another thing that I would like to mention is that.
The imaging data comments is the imaging that comments.
Data are now available on the Google public data set program and the big advantage of of having the data there is that you can use all the computational tools that are available.
But you also have the option to download the data again. I will post the link into the chat window in a moment.
And last, but not least, I wanted to point out that the imaging data comments has released pathology collections and what's, uh, important.
And what's interesting about these collections they have been converted from files to di com.
A TIF,
dual personality files,
and the data can be visualized in the slim viewer and I'm showing a screenshot of the slim you were here,
and we hope to present to the IDC users in the near future more collections of com,
uh,
pathology data.
Thank you that's the update from the imaging data comments this month.
We have, uh, Justin. Yeah.
Uh, 3rd.
Yes.
Okay, so we wanted to start by just announcing that we have 3, new pediatric data sets available on 2 of these are clinical trials from the children's psychology group.
The 1st, 1 is 533 and the other is area. No, 5, 3 4.
Both of these are looking at pediatric patients with tumors.
Um, then there was also a community contributed pediatric data set that came out recently, titled pediatric chest, abdomen, pelvic scans with expert organ contours.
So that one's really useful for folks looking to develop out a segmentation methods for radiation therapy to cemetery and diagnostic algorithms.
We also released the pathology data set this month, the catch dataset, canine, cutaneous cancer histology.
This says 350 histologic samples from 7 different canine cutaneous tumors and it also includes annotations of 6 different tissue classes.
The authors of the dataset provided the annotations in a couple of formats 1 that opens and.
And open source tool called slide runner, which is their links on the page there to be able to.
Visualize the images and annotations with that tool and then they also provided the annotations and Jason Coco format, which is something that's common for people that are doing machine learning to use.
Then we also wanted to highlight there was a paper that came out on the radio genomics data set that someone from the community pinged us with a Tweet.
Just wanted to take a 2nd to remind everyone that we're always happy to spread the word about your research publications. And so, if you publish a dataset using any of our images, if you tag our Twitter account, or always happy to re, Tweet those things and help get the word out.
And then last is the item that we already mentioned, which was that we did.
Restrict access to some data sets that had any kind of facial features in them.
There's currently no rule under HIPPA that requires this sort of thing, but we're looking ahead to the future and acknowledging that has 3 reconstruction and facial identification technology continues to approve. This could improve that.
This could become an eventual risk.
To protected health information and so now there's just a PDF and additional duties form that you have to fill out in order to obtain access to these types of data sets.
That's everything I have. Thanks.
Any questions for Justin I wonder if that will be like any other phone images.
Um.
For for the pediatric data sets that I mentioned, or.
Can you repeat the question.
I think it was asking something to do with our ultrasound image is available, but I'm not sure.
With the context of the question was correct I'm asking, uh, so you mentioned that there is would be like, uh, some kind of imaging modalities.
But, uh, I wonder if there is any other sound images available either, uh, with these data sets or in, uh, in, uh.
In the future, if there is any intention to do have something like that in the future.
Yes, I don't think it was the primary focus of these 2 wounds tumor studies, but there are ultrasound images for a subset of patients. Um.
And we do have a couple of other ultrasound data sets available in.
So, if you had to the cancer imaging archive dot net, and then use the, uh.
Image type column when you're looking at the list of available data sets, uh, you can see which ones have, uh, U. S for the modality column.
Thank you nothing else.
Well, do we have any special announcements? Uh, I just have a few just.
As remind us, uh, updated information about the.
Community webinar series can always be found under wiki, but we also have established this great working relationship with the communications office and you receive the information on a number of different channels by email, et cetera.
The recordings of this webinar and the previous webinars can be found on the wiki. So I will post a link in the chat in a moment. In case, you.
Want to go back and listen to it again. Our next calls are scheduled for March 7th and will present.
On the state, and the following webinar will be on April 4th, 2022.
Those were all the announcements I had wonderful.
Wonderful Thank you. All for attending the day's meeting. I thought it was very fun and interesting if things are moving forward. Um.
We will be together again on March 7th.
Well, thank you for your time.
Thank you. Bye. Bye.