Greetings and welcome to EHA Unplug, the official podcast
channel of the European Hematology Association EHA.
Hello. I am Isabella Rivera, a medical
writer for EHA. Today we're thrilled to have
Doctor Jan Moritz Mideka, a distinguished hematologist and
AI specialist. We're going to be talking today
about AI to accelerate clinical trials.
Welcome, Doctor Mideke, can you please introduce yourself?
Hi, yes, thanks for having me. I'm I'm Moritz Malika from the
University Hospital in Dresden. I'm working there as a
hematologist and have a research group on AI in hematology.
So we want to understand how you can accelerate clinical trials
by generating data with AI. Can you please explain first
what is synthetic data? Synthetic data is, as the name
says, data which is made-up, and the idea is that we face a big
problem with clinical trials in general, but especially in
hematology where a lot of the diseases are rare diseases.
For example, acute myeloid leukemia is a rare disease per
see and we already know that there are several subgroups with
targeted therapies. So the number of patients we can
include in clinical trials are even lower.
And therefore we need to think about new ways how we can
accelerate how we perform clinical trials in order to get
sufficient data to get the knowledge about novel therapies.
So what types of synthetic data are you generating that could be
used for clinical trials? So the idea is that you have
synthetic data which is reassembling the original cohort
in a a most perfect way, but without being able to get back
to the original data. So you're generating images.
This can be used used both for images but also for tabular
data. And what we did in recent study
and also other groups have shown is that you can reassemble real
clinical trial data in terms of baseline data, but also in terms
of outcome results based on cohorts of of real patients.
And the synthetic data is reassembling all the information
about the original patient, which is like age, sex, but also
molecular changes and then the outcome after a given
intervention. And how do you generate the
synthetic data? So for this we used guns,
Generative Adversarial Networks which is a known form of AI
which has been used for image generation, quite successful
already, but can also be used for tabular data.
And in our specific work, we use two types of these, so and flow
and C Top Gun. So these are types of deep
learning and it's generative modelling.
You have described 4 scenarios in which synthetic data
generation can be useful and I would like to walk through this
for the audience. So first, AI generated data
could help to prevent privacy concerns about clinical trial
date data of the patients. Exactly.
So the generated synthetic data can be shared without any
privacy issues. So we checked that there was no
single patient in the synthetic cohort which was exactly
matching the original patient. So there's no way that you can
go back to the original patient, which of course is the case if
you just have an anonymized data.
So this makes it really easy. You can just upload these data
and make them available also for other research group and see
public. So how does this compare to the
DI identification or anonymisation of patient data?
So anonymisation of these data isn't 100% sure.
And with the synthetic data it's 100% sure.
So there's no way that you can go back to the original patient
because see, there's no original data coming from one single
patient. So it's basically 100% sure that
you can't go back to the characteristics of one single
patient. So this is a big step forward
for this kind of data. Also, you would create new
patients and this would lead to either cohort augmentation, so
some synthetic patients among patients that are real or
completely a substitution and you have a complete new cohort.
How robust is this process? So for the generation, it's very
robust. We showed and also other groups,
Matteo de la Porta did this for MD's showed that it's
reassembling the whole Crestrix of the patients forgiven
disease. So it's not only age and sex in
terms of the distribution, but it also comes down to the
molecular changes, which is very important because we know that
there are certain relationships between certain mutations.
So for example, if you have an NPM 1 mutation, you are more
likely to also have inflate 3 mutation.
And these correlations are preserved in the synthetic data
set. And this holds also true for
correlation between age and certain cytogenetic
abnormalities like complex karyotypes with with higher age
and so on. So biologically important
correlations are preserved within this data.
And even more important, this also holds true for the
correlation to the outcome data. So known factors for poor or
good prognosis are also preserved in the synthetic data.
So if you have a patient with some complex karyotype, he also
has a worse prognosis in the synthetic group of patients.
And this is super important because if we want to use them
as a real control group, we have to make sure that this is also
like in the real world. So there's also A use for
exploratory analysis. So synthetically enlarged data
sets. What is the interest of this?
We know that there are several important subgroups when we stay
in the case of acute myeloid leukemia, certain molecular
subgroups which are recurrent but are rare.
And so for these group of patients, it's of course very
important to know how they fare with a certain therapy and with
a synthetic generation of data, you can substitute or enlarge
these rare subgroups. How reliable is this process?
We have all heard about hallucinations in AI.
Can this generative AI generate misleading data?
Well, of course, you always have to show it for a given group,
but for the examples I mentioned, this is very robust.
So there are some small differences, but in general this
is very robust in terms of how the baseline distribution of
characteristics is, but also for the outcome data.
However, especially when the outcome events get lower in the
training group, this leads to a lower robustness of the model.
So there is still open questions where we have to work on.
And the last case you mentioned where this kind of synthetic
data can be used is in model training or benchmarking.
Can you explain this further? So the idea is that we also have
to generate data for rare subgroups and for that we can
use synthetic data to enlarge the group of patients basically.
For in clinical trials this generated patients are called
now synthetic patients. Can you summarize what the
advantages to use this kind of synthetic patients?
We have to point out that so far synthetic patients have not been
used within clinical trials. And I believe that there's still
a lot of work to do also from a regulatory aspect to show that
this is really feasible. On the other hand, we know that
the generation works and the problem is so big that we have
to think of new ways how we can accelerate clinical testing.
So for this example, we used only intensively treated acute
myeloid leukemia patients almost with our targeted therapy.
So this has to be shown with targeted therapies.
This has to be shown with real life data and with certain
intervention. And there's a lot of work to do
before we can really say, OK, so we one group of patients within
a clinical trial for in the end leading to an approval or
identification of certain efficacy of a of a given drug.
So really is work in progress, how closely does these cohorts
match real world cohorts? OK, that's a difficult question
because it hasn't been shown yet.
So for the moment you don't know really how well and it's being
tested. We know how well it matches for
a given group of patients we studied or other groups studied
and there it is working well. However, we have to, yeah, prove
this for real world cohorts and and certain interventions.
So the idea would be to use these cohorts mostly in the
control group in a clinical trial at the beginning at least.
Yes, I, I, I think this is the most realistic and and closest
scenario that we use this as a control group, an enlarged
control group, yes, with a certain intervention.
Which would be interesting because then you don't have to
assign patients to a control group.
So you mentioned the regulatory considerations.
We have some tools at the moment, like GDPR or HIPAA.
Are they enough to address the potential issues that this kind
of synthetic patients would bring?
Well, I think this requires complete new thinking about the
approval process and the, the way we look at clinical trials.
We need a way to prove that this is really working.
And and I envision this in the 1st place to go alongside
clinical trials. And this is also what we are
currently doing. So we're going back to clinical
trials, we already did and we are going to see how many
patients we can substitute by synthetic patients to get the
results we already know. So for me this is an important
step to show how we can incorporate A synthetic patients
in real clinical trials. And I think this step has to be
done in close contact with the authorities that we together
define criteria where we say, OK, this is working and we can
move on further. So if you had a crystal ball and
could see what the future holds in five years time, do you think
it's going to be used a lot? Yeah, what five years is close
in in clinical testing, but I, I know that the way we did
clinical trials in rare disease like acute myeloid leukemia in
the past is over. So there won't be clinical
trials with like 1000 patient randomized with a certain drug
because in the meanwhile other drugs are coming up we we gain
further knowledge. So this is getting harder and
harder to perform these large trials.
So we have to adopt new methods. And I think synthetic data is 1
very promising way. But there are several steps
before we can really say, OK, that this is working and
enhancing our clinical testing. Are there other uses of this
data that you are now developing for prognostic capabilities in
hematological malignancies? So coming back to images,
there's another interesting field where we can enhance the
amount of images for rare subgroups of patients.
So basically doing the same thing for small subgroups within
clinical trials. We can also do this for images
of of bone marrow smears, for example.
This is already, it's been used. Well, I wouldn't say it's been
used, but it's like we and others have shown that this is
working and that you can generate synthetic images based
on real bone marrow smears and that you can't really tell which
is a synthetic or a real bone marrow smear picture.
And these have been used to. Train.
Well, it hasn't been used, but that's the idea.
So the idea is that you can generate more data and then
increase model development. So in the future you think that
you will go beyond the control group, so you would use the
synthetic data also in the test group.
The improvement of the techniques is so fast that we
can really like simulate what is going on within a single cancer
cell with a certain intervention.
And with that, I believe that in, I don't know how many years,
but in the future we will be able to also simulate what will
happen to a certain group of patients when we give the drug
X. Yes, I'm I'm a biologist by
training and I find difficult to imagine that you will be able to
reproduce this complex system in its totality, but I look forward
to it. It would be fantastic.
Yeah, agree on that. But like five years back, you
probably would have said that in predicting protein structure,
which is now working almost perfectly.
So yeah. I think it's really promising
and I really hope this works because it would help enormously
in accelerating clinical trials and treating the other patients.
So thank you very much, Doctor Mideke, for sharing your
insights and your experience with us.
Thank you to everybody for listening.
And if you like this episode, don't forget to like it and
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episode. Thank you.