And then hand the mic over to.
Thank you Lea. Hi everyone. Good afternoon. It's my pleasure.
To have Dr team liner, uh, of Mayo Clinic, to present on this topic of HR and cardiovascular imaging, um, with some.
Uh, some relevance, of course, to, uh, case of cardiac toxicity and oncology and I think, uh, we'll all find it very interesting. Um, the whole presentation. So we'll be looking forward to this. You have a.
Buy a sketch a slight correct. So I can use to.
Yes, uh, so Dr is a professor radiology and cardiovascular. Uh, and he's a cardiovascular radiologist at Mayo Clinic and he's also professor radiology at University. Um.
Medical Center in the Netherlands he received his empty degree in 2000 from mastery University, medical school and in 2022, he obtained his PhD from the same university.
Uh, he didn't spend 18 months as a postdoctoral research fellow at the cardiac center at Beth Israel. Taking those medical center. israel taking those medical center
For medical school in Boston, and then completed his residency atmospheric University. He also did 3 months vascular imaging, uh, research at, uh, University of Washington in Seattle.
Uh, teams, research interests, centered around the development and implementation of new cardiovascular and techniques. And obviously, as the topic of today's talk, uh, a, I machine learning in in these domains.
His author over 350 original papers review articles and book chapters as well as editor of several electronic radiology textbooks.
He is a past president of the and the new editor in chief of journal of cardiovascular magnetic prisoners as of January of this year. Uh, it is my pleasure to have. pleasure to have
Doctor team liner present to us today.
Thank you please, Tim. Go ahead.
Yeah, thank you for the kind of introduction cave on. Um.
Let me pull up my slides here and while I do that.
I will say to the audience that I'm on clinical service, and I was supposed to have a fellow here that unfortunately didn't materialize. So.
There's a slight chance that somebody may call me. Uh, but we'll see. So.
Can you all see my slides? Yes.
Okay, excellent. So, uh, the topic of today's lecture is a and cardiovascular imaging and escape on highlighted in the introduction.
There is indeed a strong connection to what's happening in on quality because they're, um, uh, increasingly, as we all know, um, cardio toxicity.
Becoming a hot topic and, uh, some of the developments here in cardiovascular imaging are very relevant to that topic as well. So, um.
These are my disclosures and, um.
In the lecture, I will discuss the.
Uh, in this field, I'll talk about the current status of API and machine learning and cardiovascular imaging, how we can leverage these techniques to improve cardiovascular imaging and I'll finish with some conclusions.
And many of the things that I say will equally apply in and other fields of of.
So also for cancer imaging.
And, uh, and things like that. All right so, how will machine learning affect medical imaging? And I think it's safe to say that it will affect all of the different steps of the imaging pipeline.
So, and this ranges from indication setting and patients scheduling to acquisition of.
So, in this lecture, I'll focus on as that is what I use most most of my research has been in that field.
And, um, this also encompasses image, reconstruction, image, quality assessment.
Uh, and then there's obviously segmentation and quantification, which is what most people associate with image and AI, uh, applied and clinical practice.
Then we have classification and reporting based on those measurements and these images and finally it's also important to realize that.
Cardiovascular imaging or a, I will have a huge impact on derivation of prognostic information from imaging studies that were maybe acquired for a diagnostic question, or some other question.
But within these images, there's a lot of information that can be used for prognostication.
Yeah.
And obviously that's the same and cancer imaging as well again. So it's safe to say that a I can and will be applied to all of these steps of the imaging process.
So, let's jump right in with acquisition and reconstruction and, um.
For every of these fields, I'll try to point out what the clinical relevance is. And for instance, when we talk about cardiac imaging or imaging in general, these examinations are typically quite slow. They take up to 1 hour. It's expensive.
Scanner hardware is expensive. And if we talk about there's a significant radiation.
With especially for repeated exposure, which is also very relevant in the context of cancer. Uh, lots of patients receive significant doses and I can really help us to reduce those doses.
So here's some examples. This is some work that my colleague Jeremy colin's is doing with 1 of the vendors that's focused on detection of scar tissue in the myocardium and you can see the heart right here.
So, this dark donut, if you will is normal myocardium and there's some scar tissue.
Right here on the sub cardio and for a lateral segment. So this takes about 4 minutes to acquire today. And if we use.
Which is a deep learning guided noisy algorithm that 1 of the vendors has worked on. We can actually accelerate these sequences, make them much faster, but still get good image quality.
So, this is about half the scan time image looks a little bit noisy but if we.
And the noise, these images, they become really beautiful and give us the things we need for clinical decision making I.
E, the presence of this scar this linear scar right here with a much better image quality, and a very short acquisition time. So this is something that now already has an impact on the way we.
Why are our images clinically?
This is some, uh, these are some examples from another vendor and this focus is also on assessment of the cardiac structures in this case, using a black blood technique.
So the cardiac chambers are dark as you can see if you.
Have a fast acquisition. These images are quite noisy. Um.
Especially when using algorithms, like, compressed sensing, which are sort of the state of the art acceleration algorithms that we use today. But if we then the noise, these images, we can nicely see all the details. We need to see.
And in this case, you can start seeing the cardiac valves again. The mitral valve right here becomes.
Nicely visible, which is basically impossible to see here using.
The acceleration techniques that we commonly use so if we don't do noise these images here is some examples showing the beating heart.
Parallel imaging again, on the left, leading to pretty noisy images I would argue these are probably still diagnostic, but they're not nice to look at and you certainly will lose a lot of detail,
but those images become really nicely accessible and therefore, better inputs for AI algorithms.
Talk about in a 2nd, that would do image segmentation.
Um, when we di, noise them, so this is something that's coming into the clinic right now, uh, via various, uh, cardiac Omar, Omar vendors and and again, this is not only for cardiac also for.
Other organ imaging, so that will basically speed up acquisition to much faster imaging protocols.
If you're interested in, uh, how this technically works, this is a review article. We published a little while ago about the different techniques that can be used for this and how these work.
Now, 1 of the other things that that are facilitated by, uh, the algorithms that I just showed is moving to cheaper hardware, which ultimately is really good, because it increases access to cardiac Omar or in general.
So this is some work done by launch seminary from Ohio State.
Diversity and basically what this shows is beautiful images of the heart however, in contrast to what we use clinically, which is a 1.5, Tesla or 3 Teslas scanner, this was acquired on a 0.35 Tesla magnet.
So this is a much lower field strength, which will lead to much noisier images but because. much noisier images but because
Have those.
The noisy techniques now available, we can actually make it possible to image on these much cheaper, lower field magnets, which.
If we look at the price of these magnets, we can start putting them out and much higher quantities compared to what we have today or some other.
Uh, contrasts if you will, so, this is T1 mapping and you're some delayed enhancement examples using this as 0.35 magnet and this is perfectly acceptable for clinical use.
Now, the cost of these magnets is much cheaper as I mentioned. So, the, the hardware costs are.
Are really low, but the vendor sell these systems today for approximately 1, half to 4th of the price of a, uh, 1.5 or 3 Tesla magnets. So then it becomes.
Uh, not such a big jump anymore over and above the best echocardiographic machines that we have available today.
So, what about and image reconstruction there's also stuff going on there. So, this is some work. Uh, 1 of my PhD students did about using a machine learning for D, noises images. So, this is a phantom.
And when you see this red line profile, the jitter on this line, basically denotes the.
Boys iterative reconstruction is what the vendor supply these days for the noisy images and these are usually model based techniques but here's the result that we obtained with a convolutional network that we designed.
And when you apply this to patients who undergo coronary calcium scoring, which is a technique to detect calcifications in the coronary arteries, and the.
Um, scoring method that is used for that is heavily dependent on, uh, on a certain threshold of 130 Hartsfield units. So, if you have a lot of noisy pixels, they either erroneously get included in this measurement or or excluded.
So you get a lot of hours if you go to low dose, coronary calcium scoring. coronary calcium scoring
But if you use iterative reconstruction, you can see these images become better in terms of the noise level. But, um.
Convolutional neural network is also capable of doing this at a much at a much faster and reduced cost um, in terms of.
Processing these images, because these software packages that are sold by the vendors typically cost you about 100,000 us and this, um.
You know, this, this hardware set up to do.
Uh, deep learning the noisy is obviously much cheaper. Um.
And also, in terms of processing costs, because it's much faster and requires less resources.
All right, let's move on to segmentation, which is again what most people think about when they think about bringing deep learning to radiology and machine learning to deep radiology.
So what does the clinical relevance here? Well, it's obviously tedious work. It takes time. We have a lot of technologists here at Mayo clinic that do this work for us as radiologists.
And I would say about 20 people right now are full time working on various segmentation tasks and image analysis tasks that could perhaps.
Also be done by a I techniques, um, another important observation here is that, um, they obviously cannot measure everything. That's interesting.
And therefore, a lot of relevant information is not extracted from images in a routine clinical fashion and we ignore it.
Um, and machine learning can also help identify poorly visible structures.
So, we could work with a reduced contrast, those, which is relevant and patients with for renal function or for cost reasons. Um, so, and I'll show examples of this and upcoming slides.
So, when we talk about segmentation of anatomical structures, this is an example of some really nice work that.
What's done, um, to identify quantify cardiovascular structures some cardiac examination. So this was done in a data set of 166 patients who underwent coronary scanning. Um.
underwent coronary scanning um
Some details about the methodology, but what's most important is that fully automated extraction of LV and RV volumes and mass was done by this network, or enabled by the network,
which can then be put into reports and use for prognostic purposes. So, this worked very well with regard to.
Uh, using, uh, human segmentations as a standard of reference and as you might imagine that these networks have become so good these days that you can wonder if humans are still the standard of reference for these segmentation.
So, some work we did again, here is, uh, I talked about, uh.
Um, identifying or or quantifying poorly visible structures as we try to do the same, but based on non contrast scans. So, the coronary calcium scoring scans that I showed earlier.
Are performed without injection of IV contrast and what we did is we trained a network on conscious enhance scans, but then what the purpose to segment the cardiac chambers on non conscious enhanced scans,
which allows to extract information about cardiac chamber size. Um.
Without injection of contrast, but using deep learning techniques and here's some data from this paper that shows we obtained very good accuracy with regard to.
Human, uh, experts for determining, um, these, these values.
Here's some more work so what will what we're seeing now being published is is not these sort of 1 off networks.
Uh, that do 1 single task, but, um, most investigators who do serious work are now focusing on, on entire pipelines of machine learning algorithms that can not only segment,
but also can look at quality control and things like that. So, in this work here, the authors have built a network that can.
Image quality and reject images for segmentation. If they're not good enough segmentation part is then performed.
Parameters are computed and, uh, the quality controls again, performed on the output values.
And this, then this, all of this, if this is, okay, it's going to be put into a report, which can then be used by the radiologist or cardiologist reading these images.
So, again, this is a full pipeline approach where all of these different steps are basically attacked by, uh, dedicated networks, but they function as an ensemble.
So, once you see these data, you know, that quality checks have been performed and you can.
Rely on these data, so this was highly accurate for determination of injection fraction and strain in the heart. And what's also really nice is that this framework is available through the author. So if you're interested in this, you can basically.
Get the, uh, software code for this pipeline of networks that, from the altar.
So, let's talk a little bit about diagnosis. What is the clinical relevance here?
So, obviously, 1 thing is that machine learning could serve as a 2nd reader and, uh, for instance in my clinical work here, we have a software package. That alerts me to the presence or absence of pulmonary embolism and certain scans. And it's very helpful.
Because sometimes, especially if you're not.
Performing the scan for this syndication, it can be useful to get this information. It's still not perfect. But, uh, the accuracy I would say is pretty good.
Machine learning can be helped to triage examination so when I pick the next study, from my work list, it alerts me to studies with relevant or urgent findings.
And, uh, this can allow me to pick up the most urgent study, which is good for the patient. Of course.
Sometimes machine learning can also be used to avoid invasive examinations and I'll show some examples there and again to reduce contrast to.
Agent usage, so, um, 1 of the.
Nice examples in cardiac and more imaging that was recently published as this paper.
And this is about using non contrast, Cindy imaging. So, uh, basically loops.
Of the beating heart and taking some diastolic images from these and running them through texture analysis software with the objective to identify cardiac scar.
Uh, myocardial scar and that's important because now we inject contrast. We wait 10 minutes and then we perform a dedicated sequence for this.
So, if this could be replaced by analysis of Cindy images, we kill 2 birds in 1 stone because we don't need to inject contrast. And we, uh, we can use the city.
This is also for detection or measurement of injection fraction and things like that.
So, this was done in 120 patients with small and large infarct. So, texture analysis open source package was run on these Cindy images and.
Reproducibility was tested and it worked out pretty well for detection of both scar large scars as well as small scars. And this distinction is important, because it can have ramifications for.
Uh, the treatment that the patient gets, some work that we did again was looking at the ability of machine learning to help us identify significant coronary artery stenosis.
And presently, this is done, invasively by inserting a catheter in the coronary arteries and measuring the pressure drop across.
The analysis.
And that's what we use as input in this project. So we took a bunch of patients that had a coronary fee angiogram as well as 1 of these innovations in basic measurements. And then we designed a network that could triage patients.
Into either having a significant stenosis or not, which.
And if you have a significant analysis, you typically get a coronary artery dilatation uh, if you.
If there's no significant pressure drop you leave that stenosis along.
And so we trained a network to identify these patients.
This was also a relatively small study and 166 patients were 40 were used for development 126 for validation.
And what we do is we analyze the LV myocardium in these patients, using deep learning analysis and tools in a patch by patch basis, as you can see in this image right here. And then. see in this image right here and then
It should start classified.
As having either yes, somewhere in the coronary artery stenosis with a flow limiting lesion or not. And, uh, this was actually reasonably accurate. It wasn't perfect. And we're currently working on additional.
On getting additional data to, to work towards this call because this was a relatively small data set.
Uh, but the, um.
Uh, the data shows that there's definitely a signal in the noise and, uh, we weren't able to achieve accuracies of close to 80%, but obviously for clinical application, this is not yeah, good enough.
So, we're, we're still working on this. This is also some interesting work from Elliot group.
Um, and what they did is they used.
Um, 4 dimensional and diagrams to detect left ventricular wall, motion abnormalities and.
The reason why I show this paper is because I think it's quite interesting because it's.
Very interesting in the way they did this so normally we would feed the raw data into such a neural network, but that's such a huge data set. These data sets run and the tens of thousands of images.
That the authors basically generated a volume rendering of the CP data, and then input this into the.
Neural network to detect wall motion abnormalities and this was very computationally efficient.
And also allowed the calculation of a lot of parameters and identification of wall motion abnormalities with very high accuracy.
And this can then be applied in a variety of clinical scenarios. Of course. So this was done also in a reasonable sample of patients. And, um.
Because they use volume renderings instead of the source data. This is very computationally efficient, because it can take these very small, basically data sets and run the.
Uh, analysis on them, instead of using multi gigabyte.
Data sets, which comprise the raw data.
Um, some other work that's interesting, I think is, uh, we're.
Something we do quite a lot of in our clinical practice is following up of aortic diameters.
Um, and this is a very tedious and time intensive task for human observers. Uh, but this is now also being done with deep learning techniques. And this is some work from my colleague Alex brat, who has written, uh.
Uh, some software to do this completely automatically as you can see right here. So segmentations are done and the images.
A different time points, and then, um, we can measure the aortic diameters at any level in the aorta with very high accuracy.
Follow it up over time so also for.
The world, we see these ensemble networks and multi center. Datasets increasingly being used to generate.
Machine learning tools that can generalize quite well to external data sets, which has been a traditional limitation.
Uh, so this work, um, from cedar Sinai, medical center, uh, was based on 6 derivation, slash training, cohorts and close to a 1000 patients. And then.
Once the network was developed, which was capable of assessing coronary stenosis and the significance of those analysis was validated in 3 cohorts.
So this is an example of a of a study that did a good job at creating a representative machine learning tool, and then testing it out in different cohorts and showing that. It works across these different cohorts.
So here are some data from that paper. So what we normally do when we.
Report coronary artery disease we use a system called rats and you can see that the human expert.
Agreed quite well with a deep learning expert, or the deep learning network. Sorry.
Uh, and also with regard to coronary and geography, uh, in addition, this tool was capable of measuring coronary artery plaque volume and diameter of the stenosis. And if you input this into.
Um, um.
Follow up analysis you see that this, uh, this technique is capable of triaging.
Patients who are at higher risk of getting coronary events versus patients who are much lower risk.
All right, so worse about monitoring monitoring and therapy and, um, 1 of the hot topics that links cardiac imaging with oncology is obviously cardio toxicity and in this review,
the authors look at advance imaging modalities and how they can be used to.
Address this topic and.
Um, all of the prior tools that I showed, you would be great to apply in this field, especially.
Assessment of cardiac volumes and longitudinal shortening,
which is sometimes called strain and this turns out to be an important tool for identification of cardiac toxicity and is probably the most powerful method here. If you sum up all the pluses for these different imaging modalities.
Is is the most.
Let's say, versatile technique for assessment of all of these different things that you may run into. 1 patients, get chemotherapy or cancer. Otherwise.
And, uh, you're trying to assess the, uh, effects on the cardiovascular system.
So, here's some examples of this paper, uh, so fully automated segmentation of cardiac chamber volumes assessment of global longitudinal strain here of the left ventricle. You can display this and curves.
And this is a very sensitive marker for future cardiac problems. And.
With.
Um, patients with, um.
Um, who undergo or who who get chemotherapy.
So, um, we can also combine us with mapping, uh, which are techniques to look at tissue composition.
If you will, that are very sensitive to the presence of cardiac fibrosis score imaging, as you can see right here. And myocardial perfusion a lot of work on this field. Has been.
Done at the Peter, for instance, has done a lot of fantastic work and fully automated measurement of cardiac profusion.
Which is relevant, obviously in the context of coronary artery disease, and also coronary artery damage, for instance, uh, as a consequence of radiation therapy.
And this brings me to this paper. So this is a recently started Consortium in Europe.
That, uh, is focused on a stereo tactic, radio therapy of cardiac structures and this consortium is run by a good friend of mine used for hoof from.
And, um, he, uh, got this grant funded, which is a, uh, 30 Institute, European, wide, uh, project where they, um, aim to.
Apply radiotherapy for refractory cardiac arrhythmias. So there's been some work that has been published in this field.
And what's happening now, is that, um, this turns out to be a relatively good last resort treatment for patients who have these tacky.
That are untreatable by traditional methods and obviously, all of the tools that I discussed are interesting there, because you need to carefully counter the heart and machine learning really helps there to to do this.
And the unique thing about this project is that it's being done on an Mr Linux system.
So, it's a linear accelerated integrated into an Mr scanner instead of the traditional approach where we use scanning, uh, as a basis for, um.
Looking at the heart, and the nice thing about cardiac Omar and and in the radio therapy machine basically, is that you can take into account the motion of the heart and be much more specific when,
and where you deliver your radiation therapy.
So that's also a relevant link here to the oncology field to the radiation therapy field.
So all right, let's finish with prognosis.
So, as I mentioned in the introduction images made for diagnostic purposes, often contain prognostic information, which, for instance, can be used to adjust the intensity of therapy or,
especially in this day and age when we have.
Very expensive medical therapies you want to direct those therapies to those most in need.
And a couple of examples here 1 of them is pulmonary hypertension. So we have some drugs there that, uh, recently were brought to the market, which however cost about 45 K per year.
And you can see the severely dilated. Uh. uh
Right.
Um, ventricular chamber here, uh, with hypertrophy and, um,
wouldn't that be nice if we could use machine learning tools to analyze while motion and say something about prognosis and perhaps the effect of therapy so that's exactly what these authors did in their work. So, they segmented the.
Right. Ventricle they, uh, what you see right here on the right is a 3 D model of the right.
Ventricle and those loops denote the excursion of those pixels over the cardiac cycle and you can see some loops are very long and, uh, which means there's normal motion but some loops are very.
Short and shallow, which means that there's very little motion of this part of the RV and what the authors did is, they took this information and they put it into a survival network compared this with, let's say the.
Conventional tools, the normal tools we now have for analysis of.
The right, ventricle, which is injection fraction, and basically visual assessment of wall motion,
and they were able to show that this machine learning motion analysis was actually much better capable of triaging patients who did relatively well versus those who did very poorly. And this means that.
This information could potentially be used to guide more intensive treatment to those patients at higher risk for, for problems.
Now, here's some other examples in the field of coronary.
Artery disease where inflammation is now sort of the last frontier that we can.
Uh, attack with new drugs and, um.
Because we know how to manage blood pressure and cholesterol quite well. Uh, but the inflammation is now the next target. And also, there, there are some new drugs on the market, which are quite expensive. So, kind of keep them up as the drug that I'm referring to.
And, uh, in this work here, these authors from Oxford, uh, basically use.
Cd scans.
Where they derived a radio transcript tomic signature.
Uh, that indicates the risk for future cardiovascular events.
Now, why is this relevant? Well, if we know that patients have a higher risk, we can perhaps give them these very expensive drugs to reduce inflammation and get a further reduction in coronary events.
So, what did they do they performed a number of studies in approximately 2000 patients to validate their findings. findings
So so they.
Basically, what they do is they analyze the fat around the coronary arteries. They look at the density of this fat at imaging. They look at the texture of this fat.
And for this, they determine a, an index that, uh, that shows tells you something about inflammation and the fat around the coronary arteries, which is thought to pretend the higher risk for future coronary events.
And, uh, if you look at this signature and add it.
And compare it to patients who don't have it they have approximately 10 times higher risk for future cardiovascular events.
Now, obviously we want to use this on top of the.
Currently accepted tools and this works as well. So there is additional incremental prognostic value. For instance, if you have this, uh.
Adverse fat signature, if you will, uh, and you don't have any.
Traditional signs of high risk black, which.
Indicates also a high risk for future coronary events. There is additional prognostic value.
In doing this, so, uh, and this is only possible by using good machine learning tools, because the human observer's unable to, to capture this, or assess this with any, uh, reasonable degree of reproducibility.
Final study I want to show here is precision pheno typing, which is obviously again relevant also far outside of the cardiovascular field.
But in this case, this concern to study looking at dilated cardiomyopathy, which is mostly a set of.
Genetic diseases, and what these authors status, they took 426 of patients that very deep pheno typing, using clinical data data imaging, data, genetic analysis.
And then they basically used a bunch of machine learning tools to analyze these patients and come up with pheno groups that had different, um, prognostic features if you will.
And, uh, they were able to sort of.
Come back to a 5 point risk score.
Uh, which is explainable and understandable by routine. Let's say clinicians and people managing these patients.
And that means something in terms of of data, and they were able to derive this using a set of machine learning tools.
I'd come up with different prognostic subtypes.
So this is also very important work. I think that that goes way beyond the simple segmentation task, but it shows you, you can use these machine learning tools to get new. Let's say, insights and disease etiology and prognosis.
1 used carefully.
So all right, so, let's finish up. Um, yeah a, and cardiovascular imaging is making a lot of headway. There are a lot of connections to other fields, including the oncological field.
And I think it's important to say that it's a tool and not a goal. And there are huge opportunities.
To apply this in the clinic and get meaningful insights and data that we can use for getting better insights and to disease.
But also to get better images that we can use for clinical management.
So, lots of vendors are working on this, um, clinical applications are now.
Coming into the clinic, uh, for commercial use and, uh, many more to come in the next couple of years. So.
Um, however more important, uh, work needs to be done in making sure that these algorithms generalize the way they should the way we expect them to, which they don't right now.
But this is going to be key for clinical adoption and fortunately, we're seeing more and more high quality work, but we need more of that. So there's still a lot of work to do with that. I'd like to thank you for your attention.
Thank you for sharing my screen.
And taking your questions, you may have.
That that was a great, uh, tour to force, uh, presentation on various applications of a, I.
And machine learning to cardiovascular imaging particular.
I imagine there are probably a lot of work going on in the echo choreography. Are there things that will make.
Equal competition for more applications as a more versatile and cheaper system.
Yeah, that's a good question, but I, I see these tools as complimentary so, uh, certainly, there's a lot of work going on in that field and for instance, yeah, I think.
Just as Omar, and or or being attacked across that entire pipeline. So as Echo.
Cartography and, uh, clinically, uh, there are now protocols or algorithms that can help you obtain the correct views. For instance, if you're off a little bit, the algorithms can can adjust these things for you.
There's obviously a lot of tools and automated analysis echocardiography data.
Thing I didn't touch upon, is that, uh, but that's also very relevant is even in the field of of.
Analysis there's a lot of work going on and AI and, uh, my colleague Peter noteworthy here at Mayo Clinic has a lot of has done a lot of work in this field and shown that even from.
Analysis you can get a lot of, um.
You know, direction with regard to disease the patients may be suffering from.
Okay, thank you. Uh, like to open it up to questions from the audience. I think Vikram has a question.
Yeah, fascinating talk, I'm a radiation oncologist and you mentioned some tools for predicting, uh, cardiac toxicity after chemotherapy.
I wasn't clear how applicable those are to cardiac toxicity after radiotherapy.
Since the dose of radiation tends to be much more heterogeneous, you know, when you're treating lung cancer, breast cancer and so on.
Yeah, so there's that's an excellent question and there's a bunch of tools that we currently have available. Some work we did that I didn't even touch upon was automated extraction of coronary calcium from radiotherapy planning scans.
That turns out to be an excellent predictor of outcome and patients and this is these are non cardiac triggered scans.
Right so, but regardless a large analysis that we did in patients with breast cancer shows, that this information is therefore the taking and.
Be used to predict adverse outcomes, adverse cardiovascular outcomes and patients having undergone.
Uh, both chemo and radiation therapy um.
With regard to, uh, functional data I think assessment of strain is very important.
So, uh, as long as you have information about how the heart contracts you can measure this parameter.
Turns out to be an excellent predictor of a future cardiac problems and it's much more sensitive than the injection fraction because the injection fraction only starts to fall later.
And the information you can obtain from strain shows, altered strain patterns in patients who later develop injection fraction drops.
So, this could be a very good measure for assessing.
Let's say overall, uh, problems with cardiac function as a, as a function of various treatments. So both radiation and chemotherapy.
I hope that answers your question. Yeah, thank you.
Other questions for that.
Yeah, I have another 1 about the, uh, you mentioned the large European study.
Uh, of Mr Linux radio therapy for recalcitrant.
Read me, as is anybody in this country working on that in a serious manner do you know.
Um, well, I know that there's a bunch of people working on, uh, on this, but based on as far as I know. So guide at, uh, or radiotherapy if you will. Sorry.
Um, so that the people who published the original investigation, and the New England Journal of medicine a few years ago, they, they were working with conventional radio therapy equipment, uh, which includes a, a scan.
So, but but you may or may not know this is 1 of the sites where this whole idea of was was 1st pitched and, uh, people have been working on this for 15 years there.
And they now have, um.
Yeah, they're, they're actually using this correctly there. So, but I'm, I'm pretty sure that in the U. S. several sites will have this type of equipment. Uh, although I, I don't know any specific, uh, names right now.
Uh, but I can perhaps provide you with that information later.
Thank you so any other questions.
If none, I would like to ask you a quick question, although the answer might be wrong, but at the dead previous installation of this webinar was on and we heard from Dr George she radiologist at Cornell,
giving you a nice presentation on potential implications of.
And similar to large language models for, uh, both research as well as clinical practice, and having such an expert like yourself in cardiovascular imaging.
I'm compelled to ask you what your views are in the role of large language models in cardiovascular imaging.
Yeah, that's a that's a very interesting and very rapidly developing topic.
And, uh, in fact, last week and radiology, artificial intelligence, uh, which is 1 of the sub journals of of radiology,
there was a paper that showed that these models can be used to convert non structured radiology reports into structured radiology reports.
Um, and and the field of of radiology, the, the desire to.
Create structured reports as as old as radiology. Uh, but permissions really are radiologist.
They don't like it if they have to work this way that they would rather be concise to the point.
Um, and and not, uh, put in lots of template texts that, uh, may not be relevant to the current problem. But.
You know, a large language model would be fantastic to convert these reports and to such.
Uh, more structured reports, which can then be used, uh, or mind for insights, uh, through electronic health record research, or outcomes research, and things like that.
So, I think this will be usually valuable for for our field and radiology in general and, and.
That will trickle down into the clinical disciplines as well and, uh, allow us to research much better. The value of imaging for patient care.
Oh, great, thank you. And your your pages didn't go off, which is great.
Thank you so, it actually did, but it was turn to silent. Okay anyway, thank you so much. Really appreciate your your presentation and, uh, great to have you again. Thanks.
Uh, with this alternative over to leave for the remainder of the program.
Thank you, um, I would like to give a brief update on the imaging data comments as we do doing all of these calls, the imaging data comments had a release in March.
And 1 of the, uh.
New accomplishments of the imaging data Commons team is that, for the 1st time, they released an analysis collection that was generated by them. And as you.
If it works, um, so it contains both volumetric segmentations and slice level annotations for body regions and landmarks, and they were applied to to collections the National lung streaming trial,
or subsets of those collections and the.
Video mix collection, um.
Here is the citation for the data said, which was placed into the and.
The segmentations are available, of course, on IDC and can be visualized.
With the, um.
The 2nd, part of the March release was that the imaging data comments.
Added, uh, the ability to visualize images that are stored in.
Uh, cloud, storage pockets.
With an, and they can now be visualized with the whole view viewer from the capability that this viewer adds is the 3 D rendering as you as the cycles through,
you can see this in the upper right corner here. And.
And it also adds the ability to combine, uh, images with your own images. If there are stored.
In cloud buckets, and as we are speaking of viewers in IDC, I just, uh, once again, 1, to remind you there is a 3rd viewer, the slim viewer,
which is used for slide microscopy and you can access this viewer on the.
Yeah.
Study and the series level, and you see the screenshots here viewer gets pulled up.
It has also been used for multiplex microscopy, which adds additional capabilities to visualize those very interesting images. And the viewer has.
Been described in this nature of communications publications publication.
That is cited down below, I will add, uh, some of these links into the chat so that you can look at these viewers and the new analysis data set on your on your own time.
And I hand, uh, the Mike over to Justin, who is going to give us an update on the cancel imaging archive.
Um, yeah, so just a couple of quick announcements from our side this month uh, the, we had a new dataset come out that was a preoperative and recurrence for patients undergoing reception of colorectal liver metastasis.
It's a based data set with 197 subjects of colorectal liver cancer, which includes segmentations. Um.
And, uh, clinical data, and then we had a couple of different, um, annotation data sets that became available.
This is part of a larger project that the cancer imaging program has sponsored to get a NCI clinical trial network datasets, annotated with tumor locations.
So these include segmentations and see points, um, the 2 trials that we added this for this month, were, um, the 883 1 and area 953 for, um, the 1st 1 is, uh. um the first one is uh
Looking at, um, Hodgkin, lymphoma subjects, 165 patients and the other 1 was well, 239 patients.
We also wanted to give a shout out to the childhood cancer data initiative, annual symposium.
Um, we've been partnering with the folks that managed the data catalog since that, got up and running, and are listed as 1 of the available, uh, data resources in that catalog. Um, so you can go.
Go to their website and find a list of all of the different pediatric imaging data sets that are available on the cancer imaging archive a number of which actually overlap with the previous project.
I mentioned for the tumor annotation of clinical trials. So, we purposefully selected several pediatric data sets.
Um, in order to help address the, the gap of available data in that area.
Um, and then we also published a new pathology dataset, uh, this one's pathology of prostate biopsy with biochemical recurrence outcomes.
So, this is, uh, uh, digitized 3 D pathology.
Um, and, uh, then the last thing is, um, just to give folks a heads up about the virtual workshop coming up on the medical image, the identification, um,
a number of folks from the team and many other leading experts in the field, uh, participated in the the task work group.
Led by, and, um, so if you're at all interested in the identification, we hope you'll join us in May that workshop.
Thank you, Justin I, I put the link to for registration through the workshop in the chat.
Box, and I think we also had a slide on this, but yes.
Okay, sorry thunder question for Justin.
Sure, please. Do you off the top of your head? Know how many and Corp trials are now.
Uh, I don't have an exact number, but I think it's around 20.
Okay, thank you. Please. Go ahead.
Okay, uh, is Justin already mentioned in also post the link we will have medical imaging data identification workshop. Again. The link is in the chat. You can go ahead. The registration is open.
You can already register for this event and you can block the.
Uh, times on your calendar, it's virtual, so should be easy to attend for everybody.
Please join us then, and it will be shared by David.
Yes, yeah and, uh, last, but not least just a few announcement.
Announcements as always a invitation to this webinar goes out via a Google group but of course, it's also being placed on the events website of the.
Data science, uh, the, and I should go for data science or canceled golf website. Um.
Previous, uh, recordings and slides if available from previous webinars are posted on the NCI Vicky and our next call is scheduled for me. 1st.
And came back to you, thank you. Thanks, Lee and Justin and, um, I'd like to thank all the.
Folks who attended this interesting webinar and what's the well, of course Dr team liner from the clinic for giving a fantastic presentation. Uh, thank you so much. And, um.
Looking forward to seeing you at the next round. We haven't determined that the speaker from May. 1st, but we have a couple of potential candidates in mind and once we get the confirmation, we will make the announcement.
Thank you thank you.