Okay, you're recording now so my name is honey on program director for imaging Informatics.
At the NCI center for Boeing, medical Informatics and information technology.
And together with my colleague Dr Robert knows from, we run these, um, imaging and Informatics committee webinars on a monthly basis. We are on the summer.
For for a couple of months and happy to be back.
And have a couple of great presentations for you today, the future presentation is by.
Uh, Dr, Sandra from Harvard Medical School.
And just a.
Brief fire of Dr Carter, he's associate professor in the Department of pathology at Brigham, women's hospital at Harvard Medical School. He practices Neuro pathology with a focus on diagnosing and characterizing adult and pediatric brain tumors.
sandra's research laboratory develops and implements methodologies to image tissue.
Rejected from cancer patients, extracting molecular information that shit's light.
And how cancers form, and then develop their most aggressive features with these tools his group explores the biological properties of cancer tissues. The molecular systems tumor cells, use to evade immune.
System and the ways that cancers and immune cells assemble into dynamic ecosystems. The switch imaging data is being made available in the form of interactive tumor atlas's as part of the National Cancer institutes.
Human tumor Atlas network, the cancer moonshot initiative. H.
For short is a consortium, which is assembly to the maps of human.
Pre cancers and cancers, which can be brows and analyzed by users.
In research and clinical practice to advance our understanding of cancer.
And to find new treatments, so, with that, it's my pleasure to bring you doctor.
Please take it away. I'll stop sharing now. Okay.
Okay, Eva, thank you.
You're mute.
Okay. Okay.
Now you can hear me. Now you can't see my screen. Is that right? Yes.
Okay, we're going to get both of these work again.
Okay, how about now you can hear me and see the screen. Perfect. Thank you.
Excellent. So yeah, so 1st of all Thank you very much for the opportunity to speak. I'm really indebted for the invitation.
So, today, I'll tell you a little bit about, uh, our work in multiplex tissue imaging and cancer, and in particular with with a focus on what we're doing in the human tumor Atlas network, which is a rather remarkable community.
That we're very fortunate to be a part of I need to start by saying that a lot of the work that I'll present today, essentially, all of it is done in close collaboration with Peter, at the lab assistants, pharmacology at Harvard, medical school.
I'm at the Brigham and we,
we work in this environment called the LSP,
the lab of systems pharmacology is a convergence space 1 of these spaces where multidisciplinary activities can happen software,
engineers,
data,
scientists,
doctors with all this on colleges,
et cetera,
come together and and work closely,
so it's a really important part of our of,
I think of the work that we're able to do together.
And I just want to also 1st, start by thanking our team, which is again really wonderful, diverse group of folks that are are really working hard on some really challenging problems together. And I'll point out.
Who contribute to who contributed to our part of the work through the talk.
As I mentioned, were part of the human tumor Atlas network, the H10 and the goal of this is to deeply characterize human tissues and try to extract as much information as we can use in new and improved methodologies.
They're sort of intended to be to imaging what the was to sequencing what we hope to do with, with, with tissue imaging technologies.
Is to really dig deep into samples, enumerate the cell types that are present try to find the biological programs that are at work. And, um, even score with cells are communicating with 1 another try to understand these in larger spatial neighborhoods.
The way we can understand our own neighborhoods that we live in and try to place these in the context of the tissue. The disease, the patient, um.
Et cetera, so there's, there's a lot to do and, um.
The, the H10 is focused on a whole wide range of cancers, including Pre cancers, as they're developing from, from essentially normal cells to mildly abnormal dysplastic cells on towards invasion.
And our center is focused on Pre cancer of the Pre cancer studies of melanoma and to do that because these are small lesions they can't be associated and study with a lot of genomic technologies we have available for other
tumors.
We really are focusing on the tissue imaging, which is our strength, and it's forcing us to think of new and creative approaches. I just want to point out.
This is the marker paper that describes what the H10 is up to. That was published last year. So, I'm a pathologist, we use glass slides. We reviewed them under the microscope we look at, and we come up with our diagnosis this way. And this is an activity we've been doing for about 100 years over 100 years.
Uh, very productively, but there are obviously, some very clear limitations we practice pathology, which is review of images from the frozen section room on the patient's on the table.
We get these kind of artifact written sections to some more more appealing, permanent section histology, as you can see here and then we layer in.
Genomic information, but it's painfully apparent that we still are not extracting as much information as we need to out of the, the images, and more importantly out of the tissues that are available.
And this is kind of driving a lot of advances in digital pathology on. But also the multiplex tissue image in methods that we're, we're working on.
And traditionally we have, and then we have single color markers with individual antibodies on, on, on on tissues. And again, this is very useful what we call him.
You know, here's the chemistry where we can detective a protein is expressed or not expressed and diagnostics that has a lot of relevance to us.
But you can also imagine how this is also not quantitative kind of limited and does not give you as deep a quantitative assessment of the tissue as we'd like.
And because of that new tools have been developed that allow us on a single section of tissue to interrogate and then address and name.
Individual cell States,
and also the cell types,
the cell types,
and the states that are in and study their positions next to 1,
another really build in a more a more rich,
uh,
Google Maps type of,
uh,
deeper appreciation of of the cell types that are there.
A whole range of different methods have been developed to do this. The, the, the key point about all of this is that they are optical methods and their mass methods, and I'm not going to get into the details of any single 1 of them.
Um, just to say that each has.
Benefits and limitations no method is the perfect method. And the real challenge comes with understanding the data. And that's a problem that all of these methods share, not only just understanding the data.
How to manage it as you all know where to put it another problem,
how to,
uh,
annotate it and then also how to try to get integrated insights out of this,
which we view as a real critical activity between pathology and computation going back and forth into tissues and working closely with colleagues in a multidisciplinary manner that's always vastly under appreciate how hard that is to do.
The method that we use is called in a flourescence. It was developed by Jerry Lynn, working with Peter soccer. It's a very simple method is the reason we like it. We use 3 antibodies that are conjugated to floor floors, different floor floors.
They're placed on tissue sections of form 1, fixed paraffin, embedded tissues, which are.
The standard tissue that we use in pathology, these sections are placed on glass slides again. What we do in pathology, we add a nuclear stain, then we do for champ channel imaging, 3 antibodies and a nuclear stain.
We then bleach after acquiring the image we bleach the signal. We don't strip. We and we do it again.
3, new antibodies, same DNA stain for channel imaging bleach, and we do multiple cycles of this, anywhere from 8 to 20, depending on the question and the characteristics of the tissue. We then take these individual images.
We stitch them together, we registered different channels and then we start to do high dimensional analysis, using the result in the resulting data.
A key part of this is that we're able to do whole slide imaging and whole slide analysis and I'll get to at the end of the talk it to why we think that is important. And I just want to show you a couple of images of what this type of data looks like just to get an appreciation.
These are really old now about 5 years. But I'd like to show you them because they have these gutters between the different images.
These images are against it together and then,
you know,
there are multiple markers on the left is a council on the right is a piece of lung cancer,
and you get to see how complicated these tissues are cells are densely packed together,
which makes identify individual cells difficult but you also get the seal,
how rich,
and potentially exciting this kind of data can be,
because there's just a whole lot of information there to extract and these Google pixel images of single sections of tissue.
I want to highlight here that we have.
A number of manuscripts now on bio archive in some in somewhere in the publication process of revision and rejection, uh, the, the upper ones are, um.
Imaging Informatics manuscripts, and the lower 3 are are are more scientific principles scientific insights. I'm gonna mentioned probably the 1st, 4 of them in the talk and not all of these, but I just wanted to show you that.
A lot of our work is up on by archived or archive and please please if there's anything that's.
That that's strikes or interest please go look at the manuscript but also please feel free to reach out to me. And I'm always delighted to talk about the work that we're, we're doing a 1st, start with some of the technical work that we're doing in terms of just the computational pipelines.
And I'm a pathologist that work closely with the group to to do this. I don't do it myself, but but I'm always delighted to share the activities of the group and then after these pipeline, the pipeline discussion, I'll move to some of the scientific work.
Towards the end of the of the talk.
This is a very simple design philosophy.
We want to be able to analyze data,
but also visualize what we're doing and this kind of kind of develop our tools to go back and forth between the process to keep people like me,
the pathologist in in the loop and it's a really important part of what we do the other important part is that everything is open source and and publicly available.
The main workforce pipeline that Denis Shapiro, and have developed is called MC Micro.
It's a modular pipeline developed in the next full language that allows us to swap in modules and swap out modules to,
to,
to perform certain functions and to build customized pipelines to address specific projects.
So, for instance.
We either image whole slides or cma's we get the raw images that are done flat corrected. And as I mentioned stitched and aligned, we develop these whole slide images, which are, um, in it's only 2 files.
We can do Ray tissue micro records as needed we then move on to segmentation and then quantification and as you can see here, I sort of mentioned these levels of data.
And I'll, I'll briefly mentioned these in the other slide, but essentially we go from the raw images all the way through.
To to special feature tables, which are, which are essentially what people will see in single seller in a sequencing data.
But this is special feature tables on a single cell level for the flourescence intensity of the markers that we're using in our tissue imaging. I also want to point out that we also do quality control as well, which is another important part of the of the pipeline.
We've developed and we have in review and also on biomarker in archive, a set of metadata standards for this type of work. We've divided the.
These into different data levels, as I mentioned, and 1 of the important parts of the of what we're trying to do here is to set up the standards for reporting these types of experiments. Both the, the data that should be reported for bio specimen.
For the characterization method, and also for the for the imaging, and we feel it needs to be critical parts of any any experiment that's shared in a repository for certain. But also in in journals.
And we're also trying to decide which levels of data to be shared. Whether the raw data is the most important to share.
Whether the full resolution Pre processing or post artifact.
Detection can be shared and obviously speech spatial feature tables and for the data exploration data sets and, and, uh, analysis should be shared as well.
So, we're, we're in the process of laying out these data levels and working with the community to make this hopefully into a viable standard that will be widely used and implemented by it by others.
And we're trying to work to figure out how to make this a living standard that that gets adopted by others. I'm glad to talk to anyone in the group that's interested in this.
As I mentioned, we have a lot of artifacts in any tissue imaging data, and we develop tools like bacon, particularly about silencer to remove anything that's anti antibody aggregates or false and tissue. We can really compromise the data and the analysis.
So so this is an important step that we were taking very seriously and Greg should have something on silenter out in bio archive toward maybe next month, or the or the month after that.
But it's a really neat approach for extracting the artifacts and focusing on the relevant data.
We're also working on sharing how to share data and not just the raw data, but actually the images in a navigated form.
So,
a lot of the Shapiro developments Micro,
a few members of our group,
and the next slide,
I'll show,
you have also developed and they called Minerva and we're building this sort of ecosystem of tools from the pipeline to the image display to,
to helping channel these into the into the H10 data repository data portal.
That's being. That's been developed and and managed by the data coordinating center at and his colleagues. Adam Taylor and others.
So we're working with them to to sort of unify these these tools. 1 of the important tools is what we call Minerva and it's inspired by.
By looking at images in museums sort of like these.
Excuse me he's not rated atlas's of artwork where, where you can take a guided tour of a piece of art and look at what a curator or, uh, another, any other expert has to say about the work.
And and in pathology,
we do kind of something,
maybe slightly analogous where,
where the,
um,
the,
the more senior pathologist is driving the slides basically at the microscope and other or junior thousands or trainees or,
um.
Of a picture, a little thumbnail is, um, feel to walk through and the guided tour, um, by a digital, uh, docent is a, is, um, is a useful thing.
We're also trying to build in new tools,
more API,
based type of tools like this 1 scope the screen developed by Robert Kruger and hospital group at the visual computing group at Harvard sees where you can scan the image interact with it and then get computed data
back,
so that you have this sort of interactive engagement again,
between the analysis,
and,
and the visual review kind of intended for people like pathologist,
but it's all balances,
et cetera that want to really get deep into the data.
And and and understand it in in real time. So we have a bunch of tools and development like that as well. So, what I'd like to do now is pivot to 1 of the stories that we're working on into H10. This is actually it's called Donna.
It's, um.
It's focused on colorectal cancer,
and it has to do it's an effort to take a single specimen shared across the H10 and have different groups,
apply their own visual,
um,
imaging method,
um,
mostly energy methods to to the,
to the tissue,
collect the data.
And then do a joint analysis.
As a Pre step to that and this is what I'll tell you about today,
we've taken the sections of that sample that we have and we've done a deep analysis to start to better understand the samples so that we can then layer on the information from our colleagues in a,
in a structure in a well developed structure where we understand the the sample in a,
in a very detailed way.
So what we've done in this case, we've taken 1, colorectal cancer from from the, the biobank at Vanderbilt, and we've taken that 1 SEC. 1 sample, we've literally sectioned at 10,106 plus now sections of it.
And what we've done is we've taken an agency of every 5th section and multiplex imaging of of every of every neighboring section.
So, every 5th section as well, but the neighbor today, and we've kind of built 4th generation is 3 dimensional views of that data and also the ability to compare the data across these neighboring, but not not exactly. Serial sections.
You can explore that data through some of our web based tools. It's quite a lot of information. It's 1.6 terabytes of raw data 2.5 of stitch data.
Now, we've done more so, but there are, at least 12Million cells, probably more like 20Million cells since we've collected another round of data on this sample.
As I mentioned,
we have a 3 D reconstruction,
and we've also done some validation with single sequencing and the manuscript describing this work is available on by archive and you can click on these link size of dot Org to explore both the,
and the multiplex data.
What we've tried to do now with this and multiplex data together is is try to develop articulate our view of how 1 can start to interact with this data and in pathology.
Obviously, we even have 100 years of crowdsourced work of various groups and millions of publications. That tell us what the important features are of cancer.
So we don't want to disregard this just take the high dimensional data and run off and work with this data apps. Totally independent of of the morphology that we think contributes a lot.
So we've thought of 2 different ways of articulating this 1 is that there's a bottom up approach where you start with multiplex data,
you enumerate and map the cell types and states,
you can identify cell interactions and do a lot of different high dimensional analysis using that information,
but it's largely separate from and it's largely and it's best done when 1 has a lot of samples in the case of multiplex imaging where we're still at the stage where we have fewer samples.
We argue that it's also good to have another approach, which is the top down approach where the pathologist is in the loop. Where are you? You look at the journey, you define the morphological regions that are important.
And then you drive the analysis based out of those important morphological features, and I'll try to give you a hint of or a sense of what we're trying to do with that as we, um.
You know, in the next few slides so 1 of the things that we've done is we've annotated regions of interest in this 1 sample. And there's, there's normal areas, like this, and other parts of the tumor that are glandular and other parts of to me that look very different.
They're solid sheets of tumor and other tumors. That kind of have this is kind of characteristic where they're glandular.
But they're kind of all this kind of extra cellular material that the tumor sits in and we train a classifier on the multiplex data from these annotated H E, regions corresponded to these regions. And then we went often.
And the prediction is using the classifier and showed that indeed the classifier trained only on the, on the marker data was able to pull back regions that look morphologically very similar to normal pullback. Normal glandular solid. I'm using this, et cetera.
But what we found is that some regions came back really well, predictive with high confidence. Predictions. Other reasons were were less clear. There was less certainty in the classification. That's shown here. Bye.
Bye. Hi, Shannon entropy shown in red, for a number of different regions. And then when we inspected those reasons, what we found is that those regions actually were not just molecularly heterogeneous and difficult to classify.
But morphologically, they represent the regions of morphologic transition from normal to more typical type of glandular architecture from Houston solid. And from glandular solid.
So, what this showed is that a lot of features could be that there's a strong link between the, the morphologic features on H. E. and the, um.
And the, uh, the, the molecular information that that's available, but there are are these regions of transitions and now we're in a, in a position to start to understand and address and investigate.
What are the molecular drivers of these plastic States.
We also took another kind of top down approach again by the histology, and we found another feature in these samples called tumor butting. And if you look at the sample, these are our regions of interest marked here.
There are also these borders between the tumor.
And the Bal wall,
basically,
and we found 3 different very 3,
very different regions of invasion between the tumor,
and the normal tissues,
and 1 of them in invasive margin a,
we found these tumor cells that are they look like they're individually infiltrating into the into the into the viral wall,
and these are called tumor buds typically defined as less than 4 cell a cluster of less than 4 cells.
So even individual cells or 2 or 3 or 4 cells that are infiltrating into the, into the, into the non tumor region, let's say so, there are a little hard to see on, but there are a lot easier to see on.
With multiplex data these are cytokeratins things. You can see the individual cells here, but there, they're not that many of these in a sample. And in fact, if you go through all 25 of our sections, there are only about 3 thousands of these cells. That are present. And out of out of 12Million cells, that's 1 at 4,000.
so this is a very small population that you can't really see in single cell sequencing data. So, if you want to study these cells, imaging is really a critical way to go forward with this, either, by antibody based imaging or spatial transcript, atomic methods.
And what we found is, we looked at these samples is that they're known that these bugs go undergo this transition into what's called an empty like state where they become more and more invasive.
They proliferate proliferate, less and less like, epithelium and more like cells.
But what hasn't been known is that this is not a sharp cut off but actually, that is kind of a gradient of transitions. And what we're showing here in these plots, is that.
If you take that tumor and by computational methods, describe everything from individual cells, all the way to larger and larger clusters of a 1000 cells.
So, we have these these tumor cells that are are grouped by their, their cluster size, the size of the community of cells. That were then that they're actually gradients, the individual cells are very lowly proliferative, but there's a gradient as you increase the size of cluster.
The cells become more productive and same with markers of epithelium and a whole range of other markers are in this kind of gradient.
So, just to give you a sense that these are not just abrupt transitions, but really kind of long molecular gradients kind of the gradients that we see in let's say, developmental biology. We think we see this illustration.
We see this for all kinds of features, which is kind of a neat new insight. And then if you take the 3 dimensional.
Uh, struck the 3 dimensional view of this sample. What we find is that these are not individual cells, but actually these finger, like, projections that are coming off the main massive tumor.
So, they're not like, they look in these, these 2 dimensional cuts where you have individual cells, but they're actually clusters and fingers that are extending out and along those fingers are these gradings are manifesting in in, in, in the tissue.
So, it's giving us a different view of what's really happening in the samples. Another morphologic feature. That's important. When you look at tumors, such as colon cancer, sometimes you see these big nuisance pools, and you can see those over here.
Higher priority is kinda like.
Regions we call them pools because they look like little little kind of, maybe, oddly shaped collections of kind of viscous fluid.
But when we,
when we take the 3 dimensional view,
and we reconstruct the whole,
not the whole investment,
but a large piece of it,
what we see is that all of these pools,
the vast majority of them are actually interconnected with 1 another and not only are they interconnected,
but they kind of form these channels that go all the way to the lumens surface out to the bowel lumen.
So, there's a channel of connection, and these little holes, like little caves, that kind of fall down, down all the way down to the invasive margin.
So so these are more like music, Catherine's and tunnels, and they have pits and branches and an, extending all the way to the lumen again changing the way we perceive of the structure of these, these of these cancers.
What I'd like to do, and I think I may be running a little short on time, but I'll try to be swift in this. I just want to say that.
Not only do we see clusters in the stroma. We also see clusters in the pools and these clusters are also parts of finger, like, projections and they also have molecular gradients that go along those projections.
So that's pretty neat to see as well and another insight that the about about the invasive front, we also see a tremendous amount of heterogeneity between the interactions of immune cells. And here is a contract placito in 1 region.
1 margin. There's strong interactions between Kevin and positive cells that express the Damian checkpoint molecule PDL 1 and the interactions with with T, cells that express and that's very strong here. But but very little.
But present very little of the rest of the tumor. But other interactions between macrophages.
And T, cells are president to but this invasive margin in the middle really has has has probably a different mechanism of immunosuppression as opposed to Carrington and and macrophages immediate and interactions with T cells.
So there's a lot of heterogeneity across the margin. And what I'll do just to save time, and I apologize for going over. And I'll skip this is that let me see.
I'll just, I'll just mention very briefly. I'm sorry about that. Is that what this shows and I won't go into the logic behind it? Is that whole slide imaging is very important for actually characterize the assessment.
Well, if you were to just sample a few regions with sometimes done, either, by certain technologies, or by by what we call tissue microarrays, you'd have a very different appreciation for what the immune environment was.
If you sample in this region, or if you sampled in this region, and we're trying to make a strong argument that to understand specimens. Well, whole slide imaging analysis is important.
And the last point that I want to make, and that's the slide here, is that we're operating that different scales. We have high resolution imaging, sometimes on low resolution imaging and different questions are going to require different types of imaging here.
For instance, we see the interaction of a macrophage with a T cell and the macrophage. This is actually PDL 1 in, in blue. It's being polarized towards the T cell, which is inactive.
And we see actually, these polarization events individual cells are not bags of intensity. It can be average across them.
They actually there are there are rich structural features and and patterns within individual cells that we think are going to be important. This is just another example of that at a high resolution between a tumor cell and an immune cell.
Where this polarization of the molecule, which is really interesting and this is the last slide that I'll show is that sometimes these interactions happen at long distances.
So, for instance, here is a macrophage that's extending a long process out to neutralize a T cell and it has PDL 1 at surface the T cell has.
And this is this is a neutralizing interaction, but it's happening at a distance. So, high resolution imaging is going to be very important for understanding how cells communicate with 1, another, what the rich architecture the infrastructure of these tumors are.
And that's what this slide is intended to show you here, is that we're gonna have to do imaging.
At, um, the centimeter scale, but also do some analysis down to the down to the nanometer scale. And this is 1 of the challenges that lies ahead for all of us. So I just want to say, thank you. And, uh, and again, thank you for the invitation.
Glad to talk with anyone about any of the issues around pipelines or computation of these samples, or the biological questions that were really, you know, giving up to answer right now.
Thank you very, very much. Thank you. Dr. something. It's a great presentation.
Don't see any questions in the chat. We have a couple of minutes for questions. I'll just start by asking. Are these data being used in the clinic or in support of any clinical studies?
Yeah, that's a great question. So right now in the clinical space, um.
Almost all the tools that are widely used or not, I would say clinical grade. So, even which we use, it's not it's not appropriate, I think, as a clinical aspect, just because it really hasn't been validated.
Nor is it, is it immediately applicable and can be introduced into clinical workflows? There are 5 or 6 color assets.
That are being used by by some groups, including our, our own at Brigham Scott group has a, um, immune profile initiative where they've profile now over 2000 samples, using 5 markers.
And that is sort of in the clinical, Translational realm. Where the data from those.
Those analysis are being used for clinical trial assignment, uh, over time I'm certain that this is going to be increasingly more common and as the asset is improve.
And we're trying to develop assets that we think we'll get out to 15 or 20 markers on a single slide that are compatible with clinical workflows.
I think you'll just be seeing more and more of this and ideally, we'll have assets that will.
Be deployable in the research arena, but that can also this nearly a similar asset can be applied in the clinical arena that we'll have. Um, hopefully rapid biomarker transfer between the 2 spaces.
Um, at least that's 1 of the hopes.
Okay, thank you. There's a question in the chat.
Completed I'm reading the correct question. Is it to the compute?
Cpr GPU and storage sit in the same location.
Yes, so so I think, um.
We, we should probably have you talk with our with our team to get the full answer, but we're, we're trying to migrate as many of the tools into the cloud. Both the, the.
Computational tools, and the data, um, sometimes they live in the same place. Sometimes they don't. But the goal is to to allow this all to live in, uh, in the cloud.
And so that people can bring their own tools, uh, to to the data. We're also trying to develop ways to share data that are not so expensive.
These are large datasets and, um, and we're playing with a few different models to to permit data sharing. Okay Thank you. And 1 last question, if you could answer this.
Very briefly please, uh, what is the advantage of this method over.
Oh, maybe yeah, it's a great question. So, um.
I think what I would say, there's the simplest difference 33 fold 1 is maybe is generally focused on much smaller regions of interest. It takes a long time to scan large regions because of that.
It gets back to that promo as mentioned before with whole slide analysis. Are are very useful also. Maybe sections have to be cut on gold slides, which is some costs with that.
But more importantly, if you have clinical trial samples, where you have slides, that are already cut that. You can't you can't go back to those. For instance. And, but there's a number of positives, maybe as well.
That's why there's a really rich ecosystem of tools available that people can choose from to apply to their specific question.
Great, thank you. Can I ask 1 question? Can you ask Sandra to answer that through Chad? Thanks because we have another presentation and, and also an update.
Happy to do it. Thank you. Taken down on the chat.
Please Thank you. Okay. Um, our, our next presenter, uh, we have a brief presentation on, um, the missing data comments, and the presenters Dr federal. Uh.
To federal office associate professor of radiology at Brigham women's hospital, and also the and technical lead on the imaging data comments project, uh, within.
It's my pleasure to have Andre present.
Today,
thank you,
thank you very much,
Kevin,
and thank you for inviting me to,
uh,
to be here to give this quick update about the status of NCAA you mentioned data comments and it's an interesting connection with what Sandra has presented because we are working very closely with Sandra and human network to bring some of the data that Sandra showcased and
talk to image data comments.
So, as a background, we released the pilot of emission data comments about a year ago. And since then we were working hard to add features to the system.
And to populate it was the data utilizing the public collections from as the source for the 1st, iteration of calculating the content of. So.
Uh, this is the portal of emission data comments through my quick presentation. I will just go through some of the features and the highlight.
Uh, the new things that happened, uh, in this past year, as we were working on bringing the production release to life. So at this point, you can see the summary here. We have 113 collections that are had been ingested to, uh, all of those have public access collections.
Uh, you do not need to log in to explore this data and perhaps 1 of the most exciting new collections that is available here.
Um, is the analysts national on spring, spring and trial, which was limited Texas for a very long time, and has been 1 of the most popular collections perhaps.
Uh, in, uh, so this collection now is, uh, publicly available, both and, uh.
We have a subset of clinical data, which is also publicly available.
Uh, you can get a quick demonstration of the visualization capabilities that, uh, accompany this functionality from the front page of the portal. Uh, and, uh.
Uh, as part of this work, we actually took the, uh, uh, vendor, specific representation of pathology slides in.
That were shared in the, and did the conversion into the adaptive file format.
And that's the representation that we use. So, with this representation, we are able to utilize a standard interface to visualize the data. We can, uh, have, uh, shared infrastructure to the visualization. We can leverage.
They can web interface implementation from Google. And we have the richness of the metadata available in diagram to support, uh, exploration of this data.
So, if you go to the portal, and you search for, uh, slide microscopy, uh, under the modality, for example.
You will see these 2 collections.
Uh, that have the, uh, uh, digital pathology component.
Uh, include it, uh, so, uh.
Next I want to show you just the kind of go back to the announcement that we posted to our community forum last week.
Uh, once, uh, the production release was out, and if you go to this announcement, uh, which you can get from the front page, you will be able to kind of.
Find all of those relevant pieces. So, uh, what I wanted to highlight here is another, uh, big effort that, uh, we are working on is the development of use cases, uh, that highlight.
Uh, how to utilize data in the cloud and, uh, how to work with the tools that are available in the cloud for analysis of.
Uh, data in, so we have a growing number of use cases. We have 3 use cases in the domain.
And, for example, if you open this 1, um, it will open in Google call up, which allocates virtual machine in the cloud.
And populate that virtual machine with the content of repository and that's something that we presented just last week at.
Uh, tutorial so here you, uh.
Will be able to step over the.
Uh,
sales and generate segmentation of natural from a public dataset that is available from and then you can run another network to predict survival score for,
uh,
lung cancer patient.
So, this this example highlights how you can take existing tools in this particular case. It's an unit.
A network developed by colleagues that because that and the prognosis network developed by the group of few birds, uh, deploy those tools, uh, on the cloud cross load data from, uh, Google cloud, uh, bucket.
Uh, to the virtual machine and do the analysis and interestingly enough, you can do all of this for free this, uh, Google call up.
Workstations are provided by Google for the purposes of exploration of Google, cloud functionality, and sharing those notebooks.
If you go if you go back to the GitHub repository.
That, uh, we have in, I just see, we also have the use case.
Available,
so Here's a also call up notebook,
uh,
with the example of how to retrieve digital slide,
uh,
files in,
uh,
adult personality representation and,
uh,
utilize,
uh,
Google,
Google call up for classification of the tiles in in these slides.
Uh, so, let me go back to a post that has all of the highlights and see what else I can.
Mention in the time we have left, maybe there are any questions or clarification points if anyone in the audience has at this time.
Okay, uh, if not, I, uh, wanted to also mentioned that, uh, we, uh, put significant effort into, uh, outreach. Uh, we held a number of, uh, tutorials at, uh, such conferences.
And, uh, workshop, and actually, uh, in 1 of the links here in the announcement.
I will bring you to the recording of the session at the Simi that highlights. How.
Uh, how data comments and, uh, data sets were used by.
Uh, our users by users, so you can see some of the, uh, features that, uh.
Other users not just developers of found uh, interesting. Uh, we also had a publication, uh, about, uh, you mentioned the comments that is now, uh, available in, uh, cancer research. Uh, if you go to our documentation.
Again,
from the top of the portal,
you will find it on the front page of the documentation,
and it also is accompanied by a series of short videos that highlight some of the capabilities of and related,
uh,
cloud tools.
So, I think to conclude my, uh, brief update, I wanted to bring your attention to the opportunity we make available to early. Dr.
uh, we have free, uh, Google Cloud credits that are available to any researcher.
Uh, or anyone really who is interested in exploring the public data sets in, uh, for, uh, cancer research and, uh, for cloud based analysis. So, all you need to do is submit this form.
And all requests that provide all the details will be given 300 cloud credits allocation and this credit can be extended further. If you make a case, uh, we can definitely talk with you about that.
So I encourage you to use this opportunity. We already have a number of, uh, early adopters that have been onboarded.
We will not have these funds available forever, so the window of opportunity disappear. So, here encouraged to.
Take advantage with while it lasts, and you can also get to this application from, from, uh, from the IDC portal. Uh, we have this, uh, banner, which perhaps could be more prominent, but it's there.
Just click on free cloud credits, and it will bring you to the documentation page that you can use to apply. Uh.
For this allocation, so thank you for your attention and thank you for the opportunity to update the community.
Thank you Audrey.
Perhaps in relation to the previous topic each time, you could say a few words about plans for ingesting.
Imaging data from page 10 yes. Yes. We were very fortunate to have a productive collaboration with the team.
Uh, we have, uh, developments in multiple fronts, uh, for example, we have been implemented the capability in our.
Digital pathology,
if you were to support multichannel residence emission,
and we have specific examples of the data that will be produced by each time that we used in our testing and actually we demonstrated that capability to team,
uh,
before it's public availability.
So, uh, we, we are, uh, quite confident that this functionality will, uh, be useful to support data. Uh, we also had a good chunk of communication with team to better understand how data is being identified.
Uh, we have regular meetings to understand the process.
And, uh, to prepare the submission to NSA security, which will be approval of, which will be needed before the data can be adjusted to. So all of those things are happening.
Uh, and, uh, we are doing everything we can to make this data available, uh, to the community in.
And another thing I want to mention, also a very important point is that we are working to utilize a dual personality representation for multichannel htm data.
So this will be coherent and consistent with what we do for radiology and annotation. So, we are following this data model approach, powered by diagram to support as well.
Thank you, there's a related question in the chat, um, from yoga, or the pathology slides store in native format at Tony.
Finds magnification or higher resolution.
The Pixel, the actual pixel data, which is included in, uh, dual personality files is exact the same as in the files. Uh, the header information is different.
So there is no loss in, uh, quality of the images, uh, due to its conversion to, uh, the compute representation.
Thank you another question is, are there any IDC use cases related to collecting annotations from pathologist and radiologist.
It's a great question right now there are no collections in that are accompanied by annotations.
Uh, it is a priority for us to add support for resolution notations in we work closely with Marcos Carmen, who is our technical lead for additional effort and this functionality is common.
We would be very interested to collaborate with the groups that organize activities around annotating public datasets. So, Brandon, you would like to discuss this to be very interesting.
Thank you and another question is, how does get clinical data.
For a patient, is it from or from another up to up to this point? Uh, all of the data that we have in is coming from.
So, clinical data as you probably know, exists in a variety of formats, and it's not harmonized across collections. This data is currently not available in.
We recognize that, uh, is very important. And, uh, we are looking into, uh, approach is how to bring this into the cloud and make it complicated with the image and data.
Uh, right now it's not available other than clinical data for which we share in query tables.
Great, thank you. And thanks Sandra for taking questions.
Um, in the chat box under, you may find some questions there. I think I read the last question. So thank you both both presenters and we'll move to the last section of the, the webinar, which is updates and announcements.
Typically, we provide updates on, we just heard the IDC updates. So, um, I'd like to invite my colleague Justin Kerry to give a brief update on please.
Thanksgiving and just share my screen.
Um, so just a couple of quick updates this month. Um, we along with presented, uh, and, uh, some see, Mimi. Um, so we're just highlighting the links to those talks and Andre thanks for the heads up on the video.
That's great. Now, to post a link to our video somewhere as well from the SIM call. Uh, and then, um, we also wanted to highlight, uh, some new tech research that came out using the.
Uh, the, uh, endometrial cancer H, pathology slides, um, Dr, David venue's group but you did some, uh, machine learning to.
Predict endometrial, cancer, subtypes and molecular features from Histopathology images.
So, you get the link to the article there.
Um, we also wanted to let folks know that the underlying software that powers the radiology portion of has had its API guide updated.
So, if you're a programmer data scientists, looking to access our data sets without going through the website, there's a whole bunch of additional API endpoints available through the MBA software. Now that you might find of interest.
And then the last thing is, um, the, uh.
Or think open source, packs, software recently, released the plugin. So you can easily install this.
Once you have or think running on your server and this allows you to directly import data sets from into the packs and then be able to interact with it, using that functionality.
And then I know that also shortly after this, uh.
Um, shortly after this came out, uh.
This was also integrated into the, uh, open Susa, medical imaging. Um.
Distribution, so if you're taking advantage of the.
The specialized open Susa, Linux, um, server, uh, that that now also includes this package of, uh, or thanks server with the plug in.
So, average day thanks. Great. Thank you.
Testing.
Okay, I just wanted to remind everyone that you can find more information on the.
Webinars wiki page listed here and also recordings would be available, uh, a short time after the event.
Uh, our next call or next, uh, webinar will be on November. 1st.
I'm happy to be hosting our colleagues from the United clinical center will be presenting on the resource or air. Uh, Dr, Stephanie Harmon and various.
Uh, please join us on November. 1st, and, uh, later announce the program for December. 6th with that. I want to.
Thank the, the speakers for excellent presentations and all of you who were able to attend Thank you so much and have a safe.
Afternoon and see you next month, thank you.
Thank you. Thank you. Bye. Bye.
Thank you.