And here is the agenda for today, I hope.
My Web ex controls don't come up half of it and I will hand the mic over to the Nordstrom.
Thank you. Wonderful. Thank you. Thank you. And, uh, welcome everybody. Uh, as participants continue to come in, we're going to get started.
Um, it's my extreme pleasure to introduce our speaker today and Dr Martin Jaffe who received his PhD in 978 in biophysics from the University of Toronto in Canada today.
He's a senior scientist at the sunny brook research institute, and a professor and the Department of medical imaging. And medical bio physics at the University of Toronto.
His research focus deals with, uh, digital imaging.
For a medical diagnosis, and the management of disease.
And his laboratory activities are very interesting. They deal with a range from developing image processing strategies for improving the quality of diagnostic images and methods for quantifying. The quality of the medical images themselves.
To developing and evaluating 3 D image synthesis.
In contrast, enhance imaging methods so you can see that he's doing a lot of very interesting work for this particular imaging community that we are. So, um, he has, uh.
Well, over 280 publications in this area, and it's my pleasure to turn the microphone over over to you. Now, Martin.
Well, thank you very much, Robert and good afternoon. Everybody, it's a, I appreciate being invited to give this presentation. I wish I could be there to see you all face to face and maybe that will happen sometime in the future.
But here I am in lovely Toronto and, uh.
Hello, wherever you are in the world, I'm going to share my screen, which is the most difficult thing in 1 of these presentations.
And if that works, we're on our way. So please, if someone would just confirm that it's.
Happened it's perfect. Thank you. Excellent. Okay. Thank you. So, uh, I'm going to talk about the ideas behind. Precision. Medicine is a very popular term now.
It's mostly applied to therapy, but I think the same concepts make equal sense when we look at detection and diagnosis of cancer. And because most of my work is in breast cancer today, I'll probably.
Be focusing on that, although I may give a few other examples.
I should say I do some work, uh, with people in the industry and here is here are my disclosures.
Um, but most of what I'll be speaking of today will have little little to do with those interactions.
This is sunny broke hits located at the edge of, uh.
Um, Central, uh, Toronto, it's a large, uh, health sciences center.
Uh,
it's been around since,
uh,
early and then 9,009 hundred's and,
uh,
it's,
it's 1 of those very spread out type campuses,
which makes it very nice because you can go for a walk in the afternoon and,
uh,
clear your mind or just or or or discuss,
uh,
ideas with colleagues.
Um, we have over 1200 beds in the clinical part of the facility 250,000 square feet of research space with, uh, focus in oncology, trauma, brain sciences and particularly recently.
Image guided intervention and and, um, high food for, uh, image guided therapy over 300 scientists and 1300 research staff.
So, today I really have 5 lectures.
Which, uh, uh, I'm going to attempt to give you some flavor from.
In the short period of time that we have, uh, available. So it's going to be a little superficial but I, I'm at least give you a sense of what we're doing. And if you're interested in any of these areas.
I'd be delighted to set up a separate session session with people who really are the experts in those areas in the lab.
Uh, and we can discuss things that much greater depth.
So, we'll talk a little bit about, um, stratification of breast cancer screening to improve performance.
I'll just mention the which I think you all know about already.
It it is, uh, NCI, uh, funded, um, again, a bit on micro simulation modeling that we use a lot in our work.
At a facility that we've created called the biometrics, uh, to make it easier to do multi disciplinary research involving, uh, imaging and particularly oncology and then I'll end with.
Some exciting work that we're doing and and trying to take some of the ideas of imaging.
And apply them in pathology to make it more quantitative.
Uh, to, for guidance of, uh, of of therapy.
Ah, to reduce over an under diagnosis and to really move towards precision medicine.
And a couple of the things we're focusing on right now are biomarker. Heterogeneity.
And creation of a pipeline that combines imaging with, uh, more traditional pathologic and genomic markers.
So, the 1st part, uh, stratified breaths screening the question is we know most, uh, um, uh, breast cancers are now found through.
A mammography, uh, screening is quite effective in, uh, and it's been shown to reduce the, uh, death rate from breast cancer, uh, when it's done with high quality and women attend regularly.
Uh, but mammography fails in some cases, particularly in when women have dense breasts. A lot of fiber glandular tissue because of something we call masking. The cancer is essentially hidden in the complex background structure of the fiber glandular tissue.
And for those women, it may make sense to recommend either alternative or supplemental screening using techniques, which are less susceptible to the degradation.
Um, the breast density causes in, um, in mammography, ultrasound contrast, enhanced mammography with exogenous contrast agents.
Uh, nuclear medicine approaches, et cetera.
And, uh, the upper, um.
Uh, slide part of the slide just shows 4 different classic categories of breast density ranging from the very fat fatty breast.
To the, uh, highly dense breast, um, the radiologists use for, uh, density categories. A. D. D.
To identify, uh, and in in part.
A breast density is really a double, uh, indicator of risk.
As breast density increases at a given age, women are at a higher background risk of developing breast cancer.
But as I mentioned also at a higher risk of that cancer, not being detected on the mammogram, and you can see this is work from from wanders in the Netherlands.
Uh, there they looked at the sensitivity of mammography and you can see it monotonically falls as you go from fatty or to denser breath.
And you can see the population distributions estimated to have those types of breasts.
Um, for at suggested at 1 point, uh, consideration that women in those upper 2 categories.
Be stratified to receive some sort of supplemental screening to improve accuracy.
But that would mean 50% of women would have to have additional examinations.
And I should point out overall, and this is from, um, uh, Canadian data that, uh, approximately 25% of breast cancers are not detected in mammography screening.
And if I can extrapolate those, those numbers to the US where there's 37Million mammograms done each year, that would equate to 52,000 miss cancers. And, of course, some cancer deaths, because of the late detection of those cancers.
So, it's really not practical to offer supplemental screening to half of the women.
Can we come up with a, a more.
Um, sensible approach that might actually be practical in the future.
So, what we did is we developed using the, um, uh, signal detection theory and looking at the.
Structural complexity of the breast caused by density as a form of noise.
We looked really at the signal to noise ratio of detection.
Of a lesion that was electronically or digitally implanted in a mammogram at various locations evaluated.
The, the chances that that cancer would be missed.
And came up with kind of a global masking metrics, or an index of masking.
Based on various radio features.
And, uh, which may also incorporate other features such as the woman's age.
The idea is to evaluate, um, on the mammogram for individual women.
Whether it was likely that if she did have a cancer, it might be missed in mammography.
And therefore, identify sub population, who might be where it might be recommended to have a supplemental exam.
And to do that work, you have to develop the model and you also have to test it and validate it using a large.
You require a large data bank of, of of images where, you know, which cancers were screen detected, found on mammography and which were found not found on mammography, non screen detected.
So, we use, uh, uh, a structure that we've created, I'll talk about in a minute call the biometrics.
Uh, which is just a big data warehouse repository for clinical images.
And clinical reports as our source of data, for the model building and validation we do.
And because there's so many reports, 1 has to go through, we've also developed some natural language processing, uh, techniques that we use to try and go through the records to determine the mode of detection screen detected, not screen detected, et cetera.
So, in the most recent study, we looked at 115, almost 116,000 records, uh, on patients.
To try to develop our set for model building and our set for model, uh, testing.
And I won't get it,
go into detail on this but and Martell,
my colleague who's a machine language person in our Institute and her student great cooling,
developed a natural language processing algorithm that would help us,
go through these records to identify the mode of detection.
And I think more and more of these kinds of tools are gonna be useful in the sort of research that we do.
And maybe you're using them already, um.
In some after we developed our initial model, we tested it initially using, uh, through a collaboration that we have with the University of Cambridge and doctors. Uh, Fiona Gilbert and Sarah Hickman there.
And they had cases that they described as being subtle.
Difficult to find cancers and ones where the cancers were more obvious, and we applied the masking index to those cases. And we found that in the case of the, in the subtle cases, the masking index was on average, 32 and a half percent higher.
So, it gave us some suggestion that what we were measuring within this index was related to what the radiologist was facing and trying to find cancers.
In mammograms, uh, we tested the performance further using a cohort of, uh, images from the, the University of Virginia and our colleague who was there at the time. She's now at Rochester. Dr Jennifer Harvey.
This is like an curve where what we're looking at is the on the ordinate is the fraction of the.
Cancers which are, uh, the interval catches the ones that would be missed on screening that are actually picked up and identified in this. So, called masked.
Group and would be sent off to supplemental screening.
Versus the overall fraction of the, um, uh, examinations.
That would have to be sent over so it's kind of an efficiency.
Ideally, you would capture you would only be sending women over.
Whose cancers would be missed, um, on a mammography screening and you can see in this example at this point on the operating curve.
We can catch half of the.
Uh, interval cancerous and send them for, say, supplemental screening with ultrasound.
By recruiting 20%, rather than the 50% needed in the Carla.
Model, so it's, it's more efficient by at least a factor of 2 we're hoping to do, uh, better than that in the future.
Of course, we can change the operating point to catch more of these MIS cancers, but at the cost of sending more women who don't wouldn't have MIS cancers over into the other group.
On the buyer ads performance, using the buyer ads categories are shown.
Uh, comparatively just by these, uh, black, uh, indicators.
And if there's,
uh,
we have a continuous scale,
whereas by rights,
because we actually use a volumetric density measurement as the density part of the of the of the measure,
uh,
as opposed to by reds,
which is a,
a 4 point scale.
So, in a a screening trial assimilated, a screening trial, it might look something like this where.
20 or 25% of the women would be identified as having a high probability of masking. They would get additional, uh, imaging using 1 of these techniques.
You might find you might drop the, uh, interval cancer rate from 1.4 per 1002.7 per 1000 uh, uh, in in this group.
And then, of course, there's going to be some cancers that you're not going to catch in the, uh, who will be put into the loe masking profitability. But overall, at the end, you find that you've dropped, uh, the.
Interval cancer rate by a, by about a factor of 2.
And improve the detect ability of cancers this, by the way is that a 2 year screening interval, which is done here? Typically done here in Canada.
Although why you could easily adapt this to a an annual, uh, screening.
So the next step for us, we're just beginning this work. We're going to do kind of the next step of testing in an intervention trial called ribs in Italy in parallel with, uh, genero. She has a large screening cohort. There.
Their standard is 2 year screening.
They will apply a breast cancer risk model, and also, as their standard measure volumetric, breast density, we're going to add the masking risk index, uh, measurement to their trial.
So, we're just kind of stepping into their trial with, uh, with this 1. um.
Additional measure the plan is their plan is that women who have elevated risk is determined by the breast cancer risk model.
We'll go from biannual to annual screening women with elevated breast density.
Will get supplemental breast ultrasound and women with both will get annual screening plus the ultrasound.
And what we'll do is to compare the stratification.
Recommendation that would occur if you just use density, or if you used our masking algorithm.
I'll just mentioned, uh, very briefly.
Ah, because I think it is an important to trial. It's an attempt to put some science into evaluation of a new technology. Typically many of these technologies just get thrown out there.
And, um, don't really know how much impact they're going to have.
In this case, we're doing a randomized trial to try to see.
And the questions a little different, it's not just can we find cancers, but can the use of it this new modality reduce the number of advanced or aggressive cancers in a screening population.
Compared to the standard 2 dimensional mammography, the number involved has just been reduced to 130,000. it was originally 165,000 women 45 to 74 who are randomized between the 2 dimensional or.
Uh, Thomas synthesis, 5 screens with follow up, but I think of interest is the fact that, uh, there is collection of tissue in anyone who gets biopsy an optional provision of tissue.
Uh, blood and buckle sells for biomarker studies. Some of which are to, to be determined.
But, of course, would have to be all have to be be approved. And I think this offer is something that goes beyond.
My discussion here today, but something I've always thought would be valuable, which is.
To have kind of a Prescreen, uh, using a, um, some sort of a, a blood.
Or saliva based measure in this case, perhaps.
A multi cancer detection algorithm that's based on selfie.
A DNA, and perhaps a methylation profile as a potential for a screen.
So, I think this is exciting. It's other people's going to be other people's work, but I think very interesting.
Currently 115 sites, operational 60,000 women have been recruited with a broad ethnic and racial, uh, representation and they've been very good. Uh, uh, overall quite willing to provide these optional, um, uh, samples.
1 role that we do as imaging scientists as we've developed a remote cloud based program to try to make sure that the performance of both modalities in this trial are are high.
So, that any difference between them is really resulted as a result of their inherent performance differences and not just the variability of how how well they're being used in in the field.
I'll just mention the biometrics very quickly, because it may be interesting to you. It's essentially just a large data warehouse.
Uh, that integrates information from different sources within our large.
Cancer facility for the purposes of doing research, and it was started by myself and Dr Kathy Pritchard who's a well known medical oncologist who's been involved in a lot of the important therapy trials.
So, we have a large center. We have a lot of data. Our vision is that anyone who walks into our institution.
Should have the opportunity to become a research partner and make, uh, if they want make their data available to be used for approve research studies. And, of course, in some cases that requires.
Well, it will always require inform consent. There's some uses of data that don't, but we try to get prospective consent.
Right up front from these individuals.
Um,
but the,
I,
the problem,
what we're trying to address is the fact that in many health institutions,
the data that would be useful for researchers start on different computer systems that don't talk to each other and researches often get trapped into using Excel spreadsheets to store.
Their data.
It's not a very safe, reliable or.
Uh, consistent way of of doing things so we're trying to.
Create a more uniform platform to facilitate research in this way.
And, uh, essentially what we have there, and we've been doing this for some years is, uh, images, diagnostic reports.
Tissue samples and slides, uh, information on treatment and information on outcomes and patient demographics.
So, our researchers have found is very useful.
And pulling together the various pieces of information, they need.
Uh, to do a number of studies and health services research, and, um, looking at the effectiveness of various, uh, therapies and in my case, in looking at, uh, imaging, uh, type, uh, interventions.
So, I won't spend a lot of time here these, these diagrams all tend to look the same, but basically many data sources. We've just done by the way.
Made it a collaboration with Microsoft who's going to help in terms of, uh, facilitating the extraction of data from individual hospital sources? So That'll take a lot of the load off.
Our shoulders, it's web based, and it has, um, um, uh, tools to try to help ensure accuracy of data entry and consistency.
Privacy and security features, which have been reviewed externally.
Uh, we have a group who work to make sure that the data definitions are consistent.
Which is very important and I think importantly we have a tool that lets you identify the availability of an adequate number of individuals with certain conditions to create cohorts.
Uh, to drive research projects.
And then, uh, our work is done on D, identified.
Data from what we call pseudo patients, they have names, but they're not the correct names. They have birth dates that have been altered enough. So you can identify the individual.
We built a tissue specimen tracking system so that we can link.
Information in the biometrics to.
Um, uh, slides and specimens in our bio bank.
Uh, and, uh, particularly digitize his, the pathology slides.
And I'm going to skip, I think, just in the interest of time, and I'd be happy if any, if you're interested in this, we can talk about it, uh, in a future session. Perhaps.
So, I'm just going to jump, um.
To, uh, very quickly, just mention the fact that that in my lab, we use micro simulation modeling extensively to inform.
Our research, and also to to try to.
Understand issues around policy and particularly the, uh, cancer screening policies.
This is just something that we did using an sponsored model. This is the Wisconsin Harvard model to look at different intervals times to start screening intervals for screening, et cetera on breast cancer mortality.
The sense that group has published much work in this area, but we find these models very useful.
Uh, more recently, there's been a model developed in Canada with the help of the.
Um, assistant model W, people, uh, called and is, uh, nice in that. It actually provide some additional flexibility and you can consider things like breast cancer subtypes.
And other other interventions, so it's fairly flexible in terms of modifying the conditions under which you're watching the evolution of breast cancer and the effect of.
An intervention like screening or a therapy.
This is just, uh, from Cisco, just showing the effect of changing the interval and the ages associated with screening. You may have seen these kind of graphs before.
Ah, but 1 of the things that we did recently is we applied the model and this was the opposite model to try to understand the effective.
Colvin and the interruption of screening that occurred during Colvin in Canada was approximately 4 months. But then even now we're not screening at the volumes that we did Pre pandemic and what we're seeing.
Uh, what we're predicting is.
An increase in the number of women who die from breast cancer.
Uh, but in addition a change in the in the, um, stage at which breast cancer.
Is, um, detected as as shown here and that, uh, distribution really depends a lot on the interruption.
Uh, that takes place and the rat, the profile of the ramping back to normal screening volumes.
This is for breast cancer, and we did this in collaboration with a group interested in colorectal cancer.
There's also an model and the lung model and a few others.
Uh, that are, uh, available uh, I'll just skip over that 1 and move into the last part of my top because I see time is.
Flying here, but I wanted to give you a flavor of some exciting work we're doing in.
We've created a pathology lab. I'm a physicist by training. I really knew nothing about pathology.
I'd probably still don't know much about it, but I have good people who do molecular biologists and pathologists in the lab, uh, to, um, to straighten me out. But the idea behind it is to take the tools from.
Imaging, which are quantitative, and we found them useful in characterizing images and to apply them to make pathology more quantitative.
1 of our our concept is really summarize in this slide where we go from.
In vivo imaging, which is low special for special resolution, relatively speaking, but gives you good sampling and a, a fairly.
Uh, complete coverage spatially to histone pathology.
Fusing information between the 2 realms, if we, when we can, and then looking at individual areas.
Uh, by using, um, multiplex protein analysis of of biomarkers, and in some cases, doing a genomic type analysis on, uh, specific regions of interest.
In my lab, we've developed, uh, techniques for doing very large 5 by 7 inch type, um, pathology slides. This is of a whole section through the breast. You can see the breast. You can see where the cancer is.
And then we have a Google Earth type, uh, display system that allows us to blow these images up and to look at them in microscopic detail. So you can go from the macroscopic millimeter.
Type resolution to, uh, you know, micron.
Uh, level of resolution, uh, very quickly and and seamlessly.
Uh, this is the idea in terms of fusion. This is the work of James main prize in my lab.
Who's working with, uh, uh, images of the breast and basically, um.
Combining them fusing information between that, and our history of pathology.
Platform, so the color components are, this is a, um, invasive cancer here in the breast, normal breast tissue, and just, uh, some skin for, for reference.
And these are, I didn't want to risk showing a movie, but you can rotate these things.
And, um, and basically get a a good 3 dimensional impression of the juxtaposition of disease and normal tissue structures.
So this is the direction we're going in and right now we're actually beginning a big, uh, project in prostate cancer.
Um.
Alison Chung, who's a molecular biologist in my lab has got very interested in looking at phenotypes of single cells and I'll show you how that works and looking at the, some of the spatial characteristics of those cells.
Uh, using a protein, a multiplexing.
Uh, strategy, so, um.
1 of the questions we're very interested in is heterogeneity and heterogeneity comes.
Uh, in part from, uh, you know, clonal evolution in cancer.
And when you look at cancers, when a lot of the measurements basically give you 1 number.
Characterizing the cancer, but when you look at a cancer, you find that there's a lot of spatial variability in the cancer.
And that will be important in terms of its response to traditional chemo and hormonal therapies. And also to immunotherapy, which is an increasing interest in my lab.
Just going to move a little bit more quickly here.
So the questions really relate which patients will respond.
To a particular type of immunotherapy can you.
Predict who's going to be responsive or nonresponsive where there's going to be resistance.
By looking at the information.
Uh, that is shown in terms of these spatial biomarker uh.
Uh, maps the, the platform we use is, uh, uh, called, it was developed by the research group, but in upstate New York.
Their global research labs and essentially what involved? It's a cyclical process where you take unstained tissue.
You measure its auto flourescence and correct for that you remove auto flourescence effects and then you label with antibodies that have attached to them, uh, Florida force and.
Which you could image with fluorescence,
then you bleach you,
you,
uh,
acquire digital images,
you bleached the floor for to inactivate it and then,
uh,
repeat with additional,
um,
additional antibodies.
So, you can, uh, image as many as 40 or 50 different antibodies on the very same tissue section, and therefore, look for a CO location.
And you can do single cell counting, you can do Boolean operations.
You could measure distances so you can now you now have images.
With a very rich information in them that are amenable to quantitative analysis.
We developed a segmentation program that uses segment, various segmentation markers for the nuclei for the cell membrane for the cancer cells.
And, uh, using machine learning to segment these, uh, any kind of tissue section.
Uh, image into the individual cell components, and then we can insert the fluorescent measurements of the intensity of the various biomarkers into and attribute them to that specific component.
So, this just kind of illustrates that, uh, process here.
This is actually work from the G, but done in in our lab. And in our lab, we've developed the techniques of, um, uh, basically, uh, doing the, uh, conjugation of the antibodies.
Specifically onto onto the Florida force, so we have the capability of if somebody's interested in a particular marker, we can usually, uh, create a fluorescent version that we can, uh, use for imaging in the lab.
So you can also create from the fluorescent images you can create a pseudo R. H. E virtual H. E image.
Uh, which is useful, because most pathologists are pretty uncomfortable if they don't have an H. M. E image in front of them when you present some of the more sophisticated other marker images.
So, we're interested in heterogeneity of the traditional biomarkers and breasts. Some of them are shown here.
And you can see, for example, key 67, which is a proliferation marker is a present, but it's certainly not homogeneous, fully present in the tumor area.
You can see the same thing with, uh, the yellow, uh, um, uh, the, um, marker and green.
Uh, et cetera, so it gives us a picture of really the spatial, uh, relationship really on a molecular.
Basis almost of what's going on in, in within the cancer.
And we can look at different. We see a lot of heterogeneity in the quantity.
Uh, and the intensity of markers in these samples.
All right, I didn't realize this was animated um.
But very interestingly, um, it's very common to use immunal history chemistry to characterize.
Cancer samples as a surrogate for doing.
Molecular analysis, which is more costly and time consuming and, um.
With what we discovered is within, in our lab is within the, uh, history.
Chemical surrogates for the breast cancer subtypes.
We see a lot of heterogeneity. We've actually found that in in our hands, uh, it might make sense to.
Subdivide to produce additional granularity.
In terms of the, the subtypes of breast cancer using the multiplex Samuel fluorescence data. So here we have actually got 16.
Different categories, breast cancer based on the measurements of the estrogen.
Uh, receptor, uh, sort of the estrogen receptor, the.
Uh, her too, and whether key 67 for the, uh, proliferation marker is plus or minus.
And you can see, um.
Basically, the variability, these are the immuno history chemist, chemical, uh, groups of, uh, a number of different, uh, cancer samples and you can see.
Uh, the triple negative, they tend to be fairly homogeneous, not showing the markers, but you can see that for that. The, these different characteristic. Um.
We call them categories are represented.
Um, uh, within a single.
Uh, immuno, history, chemical, um, group.
So, I think getting that granularity may explain to some extent why, in some cases therapies aren't working, because you're really not dealing with a homogeneous SLI, responding set of cells.
Just more examples here just looking at different blocks from the same tumor.
Analyzing them and we can do, uh, measurements using something called the diversity index.
We can actually measure the amount of diversity.
Uh, in, uh, representation of of important markers, like.
And PR, in these in these cancers.
We're very interested in immunotherapy. We're collaborating with a really good group downtown Toronto at princess, Margaret hospital who are working on various clinical studies.
So, we're trying to use these, uh, the same approach to characterize these markers you can see listed, which are important markers of the immune environment.
And I can see, I'm almost out of time here, so I'm just going to kind of give you a.
Maybe another minute of a, um.
Perhaps a bit of a description of of what we're doing here, but for example, we can look at immune subsets. We can look at the presence of the important markers like.
And how it's represented as a percent of the immune cells, the a positive immune cells that you can see.
That, um, that many of these, uh, um, measurements differ.
Substantially these, by the way are not breast cancer. This is a variance sample of cancers of samples that we're getting from doctor lab at princess, Margaret hospital.
And you can see huge variability in the representation of these important immune markers in these samples as a percentage of the.
Uh, general, population of immune cells so this is probably going to tell us important things about the responsiveness.
Of these cancers, these different cancers, which from classic characterization pretty much look similar.
To the use of, uh, of the immune therapy.
And I'll just mention quickly that we are using a cluster analysis techniques, like, uh, uh, et cetera to look at the clustering characteristics of the markers within.
Examples of cancer we can see that there are quite different.
And we think this is going to be helpful in understanding which cancers will respond to, which therapies.
I think I'm, this may be a good, um, I, I think maybe this, this is the last slide I'm going to show you.
Which is just looking at before and after treatment and this is part of the inspired trial, which was a trial being done.
Uh, by our colleagues at princess, Margaret hospital, but embedded on that trial are our measurements that we're making in my lab.
Of these markers, and looking at the, um, um.
Basically, the representation of important immune markers, and seeing how they vary between the Pre treatment and post treatment.
Um, situations and how that correlates with the actual responsiveness of these cancers to, to therapy.
So these are the kind of tools I realize this has been a fairly superficial presentation, but I hope this is giving you some insight into how you can use imaging tools.
I hope in a in a valuable way.
To answering important questions in the treatment of of cancer.
And with that, I'd like to thank you for your attention acknowledge a large group of people of color people in my lab.
And collaborators at other institutions.
Uh, with whom I work, um, uh, thank you for your attention.
And just say that we, we have a lot of things that we have to think about very carefully.
Thanks very much. Wonderful. Wonderful. Thank you so much. Um, it's obvious that you have a lot of, uh, interest and a lot of research going on. Do we have.
Questions that we want to ask now, if you have a question, please unmute your line and ask the question.
Please well, you're unmuting. Let me ask this. Um, I'm very interested and curious in the fact that you're able to collect so many images from a single slide 30 or 40.
you said, um, biomarkers from a single slide? Yes.
Right, right now, what kind of I'm just curious what kind of timeframe does that take? I mean, how long is the slide? Good for it. This is what I'm trying to add. Well, you know, DNA is very stable. So if you're if you're looking at, um.
And the proteins sorry, I should been in this case, we're looking at at proteins, and they tend to be very stable and you can.
Process the slides this is not my area of expertise, but the people in in the lab.
Are very confident that they can process slides even the ones that have been around for a few years and retrieve the antigens.
So that you can actually measure them using these, these labeled antibodies.
So, I'd say years, I'm sure they'd be great over time, but.
So far that we haven't really had a problem with with samples that were kind of expired.
That's very interesting. Thank you.
So, yeah, I have a question now. This is great. Talk really fantastic stuff. Martin. So I'm interested in what you were talking about red path.
Uh, integrative analysis correlation.
And I wasn't are you, are you actually looking at tactic scenarios or are you looking at the.
Deep learning algorithms that use both to make predictions or if you talk a little bit more about how you're relating those to modalities.
What the imaging you mean and the.
And the pathology yeah, well, uh, you know, currently it's really a matter of, of, uh, of trying to fusion with, um, um.
Additional information and and and traditional information is it's challenging. It's Dr salts. Right?
Yes, hi. Yeah, yeah. Yeah. Yeah. Yeah, it's, it's, it's rather challenging. I mean, we use whatever we can, uh, but, uh, is 1 of the things we use we certainly do.
We use some, uh, fairly sophisticated, uh, computer techniques for trying to do alignment.
Of images in different domains, bearing in mind that that the resolution scale is is so so very different.
That the advantage of the whole mount is that we can.
Look at the tissue in pathology images that are starting to look a little bit like the mammograms and the images so that you can.
You know, create reference points for alignment, but it is a challenge.
I see. Okay. That's so so so then you, you actually are trying to.
Do a comparable spatial registration now, then when we can, and we've developed some interesting techniques for block face correlation we, we use believe it or not.
Uh, it's like a squid or octopus ink, uh, pasta.
Is a really good, um, uh, uh, as it is something that's very good to to use as a way of.
Going between, uh, pathology blocks.
And and some of the images you can basically put that.
Into into tissue, that's been freshly respected.
It will show up on it will show up on, on, on various kinds of imaging and give you sort of external that let you.
Align structures, you know, at the, at the macroscopic sense anyway.
Cool and if you have you thought about, or I've tried to to use Super resolution approaches to try to train the interpretation of the radiology, using the pathology.
We haven't got into that yet, but that's a very good suggestion. I mean, because we've got a group with that particular work with, uh.
Um, we have a team of deep learning folks and Super resolution is 1 of our particular interest. So that could be that could be a fun discussion. Well, that's a great idea. I'd love to talk with you about that.
Super super good things um, anything else, uh, anymore, any more questions.
Well, hearing none.
I guess it's appropriate to thank you very much for your presentation. Um, we've learned quite a bit and I'm sure people will go over the, uh, the recording from time to time to, uh, to learn more.
Yeah, and please if any if any of you, I realize that I could only give a very quick, superficial picture of things. So if any of you are interested in any of these areas, we can certainly get into it with, uh, much more detail. Um, if you want to set something up afterwards.
That's a good opportunity. Thank you. Thank you. Thank you.
Okay, Julia, I guess it back to you.
Thank you please let me know if you can see the agenda slide.
Not any more that's it.
Okay, um, next is an update, a brief update on, uh, new things from the imaging data comments.
1 of the most exciting updates that I would like to share with you, is that the imaging data comments data, which originally came from are now available through the Google public data set program.
And therefore they have become available to an even larger.
Audience the Google public data set program in itself contains more than 200 public data sets, and they are listed on the Google cloud marketplace. And for your convenience, I have put the.
You are else here, but I will also put the URL to the imaging data comments.
In the chat as well as the link to the Google public, uh, data sets program.
The big advantage of having the.
Imaging data comments, data in the Google public dataset program is that they are now, uh, that users no longer have to set up a billing account.
To work with the data, so we encourage everybody to go and just try it out.
And if you have questions, uh, you can put them into the chat. I know 1 of our team leads from the imaging data comments team, belong about is on the call. So if you have very specific questions, he might be able to answer them.
Thank you and, uh, the, the next topic on the agenda is.
An update from the cancer imaging archive. Justin. Would you like to share your screen?
Yeah, thanks. Okay.
So a couple of quick updates from our side, I think we had mentioned previously the National screening trial.
Uh, data sets now available, this is totally open access, 11, terabytes of imaging and pathology data, um, from this, uh, large trial of approximately 26,000 subjects.
Then another update on the software side of things, the NDA software that we use to manage all of our dot com data had a new update recently and the, the most, um.
Interesting thing from the user side of it is the data retriever software that you use to download the data is, um.
Now provides a feature where you can do a check some verification to make sure that all the data that you download matches exactly. With, uh, what we have on the server side.
And there's a screenshot here, just showing we can find it under the file menu to turn that option on. If you'd like to use it.
Then we also wanted to highlight a new dataset that was published as a pathology collection called nascent prostate cancer heterogeneity drives evolution of resistance to intense hormonal therapy with the nickname of any
prostate.
So, this, this was just released in the middle of November and has.
Uh, publication that goes along with it.
Then another thing on the software side, we've.
I've been collaborating with some folks that project.
And they put together a Python notebook tutorial that describes how to quickly and easily load data into the, the tool kit to do model development and deployment.
And then the last thing we had actually 3 different sessions that we were either had featured in,
or were discussing datasets John freidman and Fred prior did a presentation about,
uh.
Uh, anonymization and data privacy um, and then there were 2 deep learning lab, uh, classes that were, um, part of that series this year. There were more of a hands on type of thing.
Um, where you're working directly with, uh, with, uh, Python notebooks.
Um, and so 1 of them was the collaboration with the imaging data Commons uh, Andre, and I presented, um, a number of examples about how to, uh, import data from into a deep learning model.
Um, and then there was another 1 that was focused specifically on the cancer genome Atlas Glioblastoma dataset to integrate both the genomic data along with the imaging data.
Um, with the genomic data, coming from the NCAA genomic data comments.
And if you follow this link, it will actually take you to, uh, a number of different notebooks that we're all prepared and posted on GitHub for, um, for the planning lab sessions.
Happy to answer any questions. If there any Thank you, Justin.
And that leaves just a few announcements for today's call,
uh,
as always invitations to the webinar are distributed using the and email list and are announced on the.
Website, uh, you can also always find the information on the NCI wiki and here is the link to it on the wiki. You can also find the link in to the Webex. Uh.
If presenters agree to have the presentation recorded, you can also find the recordings of previous webinars on.
This wiki page,
uh,
our next call would have been January 3rd,
2022,
but because this is more or less the 1st day,
uh,
the 1st work day after the new year,
we decided to cancel it.
So, uh, we hope to see you again on February 7th for our next call, and we will announce the agenda.
In advance, so, you know what's coming.
And this leaves me and everybody to wish you happy holidays.
And I'll hand the mic back over to talk to Nordstrom.
Thank you Julie and happy holidays to you. It's a very nice slide.
Happy holidays to everybody. Thank you for your attendance today. Um, it was a very informative discussion and we'll see you in February.
Thank you. Bye. Bye.