Circulation November 15, 2022 Issue

This week, please join authors Qiang Zhang and Matthew
Burrage as well as Senior Associate Editor Victoria Delgado as
they discuss the article "Artificial Intelligence for
Contrast-free MRI: Scar Assessment in Myocardial Infarction Using
Deep Learning-Based Virtual Native Enhancement."


Dr. Carolyn Lam:


Welcome to Circulation On the Run, your weekly podcast summary
and backstage pass to the journal and its editors. We're your
cohosts. I'm Dr. Carolyn Lam, associate editor from the National
Heart Center and Duke National University of Singapore.


Dr. Peder Myhre:


And I'm Dr. Peder Myhre from University of Akershus University
Hospital in Norway.


Dr. Carolyn Lam:


Peder, today's feature discussion is on AI for contrast-free MRI.
Isn't that so cool, using AI to perhaps understand what we could
see only with contrast, but now in a contrast-free manner. Now I
know that sound a bit confusing, but I hope very, very enticing,
because everyone's going to have to wait for a little while
before we get to that interesting feature discussion. And for
now, let's talk about some of the papers we have in today's
issue, shall we?


Dr. Peder Myhre:


Yes, Carolyn, I can't wait for the feature discussion, but we're
going to start with some of the other papers in this week's
issue, and we're going to start in the world of preclinical
science with a paper looking at human cardiac reprogramming,
because Carolyn, direct cardiac reprogramming of fibroblasts into
cardiomyocytes has emerged as one of the promising strategies to
remuscularize the injured myocardium. Yet it is still
insufficient to generate functional induced cardiomyocytes from
human fibroblasts using conventional reprogramming cocktails and
underlying molecular mechanisms are not really well understood.


Transcriptional factors often act in concert and form tightly
controlled networks featuring with common targets among different
transcriptional factors. Therefore, missing one component during
heart development could lead to heart function defects and
congenital heart disease. And in this study by corresponding
author Yang Zhou from the University of Alabama at Birmingham,
the authors perform transcriptomic comparison between human
induced cardiomyocytes and functional cardiomyocytes to assess
additional factors that govern transcriptional activation of gene
programs associated with sarcomere contractility.


Dr. Carolyn Lam:


Wow. Really nicely explained. Thanks, Peder. So what did they
find?


Dr. Peder Myhre:


So Carolyn, through these computational analysis of
transcriptomic data, the authors identified TBX20 as the most
under expressed transcription factor in human induced
cardiomyocytes compared to endogenous cardiomyocytes. They also
demonstrated that TBX20 enhances human cardiac reprogramming and
improves contractility and mitochondrial function in the
reprogrammed cardiomyocytes.


Dr. Carolyn Lam:


Nice. Could you summarize the clinical implications, please?


Dr. Peder Myhre:


Yes. So the clinical implications are that enhancing the
efficiency and quality of direct cardiac reprogramming for human
fibroblast is a critical step in the clinical translation of this
technology, and better understanding of this synergistic
regulation of key cardiac transcription factors during
reprogramming will provide new insights into the genetic basis in
normal and diseased hearts. Well, Carolyn, please tell me about
your next paper.


Dr. Carolyn Lam:


Thanks, and we're moving now to kidney disease. Now end stage
renal disease is associated with a high risk of cardiovascular
events, but what about mild to moderate kidney dysfunction? Is it
causally related to coronary heart disease and stroke? Well,
today's authors give us a clue, and it's from corresponding
author Dr. Di Angelantonio from University of Cambridge and
colleagues who took a very unique combined approach to answer
this question.


They first conducted observational analyses using individual
level data from four huge population based data sources, namely
the emerging risk factors collaboration, Epic CVD, Jillion
Veteran Program and UK Biobank. Can you imagine this comprised
almost 650,000 participants with no history of cardiovascular
disease or diabetes at baseline, yielding almost 43,000 and
15,700 incident coronary heart disease and stroke events
respectively during a 6.8 million person years of follow up.


So huge observational study, which they then followed with a
Mendelian randomization analyses using a genetic risk score of
218 variants for GFR and involving participants in Epic CVD
Million Veterans Program and the UK Biobank.


Dr. Peder Myhre:


Wow, Carolyn, this is a topic that I think many of us have really
been wondering and thinking about. The mild to moderate kidney
dysfunction, what does it really mean? And what a beautiful study
to answer this. So what did they find?


Dr. Carolyn Lam:


First, there was a U-shaped association of creatinine-based GFR
with coronary heart disease and stroke with higher risk in
participants with GFR values below 60 or more than 105 mills per
minute per 1.73 meters squared. Mendelian randomization analyses
for coronary heart disease showed an association among
participants with GFR below 60, but not for those with GFR above
105.


Results were not materially different after adjustment for
traditional cardiovascular risk factors and the Mendelian
randomization results for stroke were nonsignificant but broadly
similar to those for coronary heart disease. So in summary, in
people without manifest cardiovascular disease or diabetes, mild
to moderate kidney dysfunction is causally related to the risk of
coronary heart disease, highlighting the potential value of
preventive approaches that preserve and modulate kidney function.


Dr. Peder Myhre:


Thank you, Carolyn, for such a great summary and an important
result from that study. I'm going to now take us back to the
world of preclinical science and talk about diabetic
cardiomyopathy and exercise. And we both know that patients with
diabetes are vulnerable to development of myocardial dysfunction,
and that exercise, our favorite thing, for maintaining
cardiovascular health, especially in patients with diabetes.


And despite a wealth of evidence supporting that cardiometabolic
benefits of exercise, the precise exercise responsive signals
that confer the beneficial effects of exercise in cardiomyocytes
to remain poorly defined. And previous studies have identified
fibroblast growth factor 21, FGF21, a peptide hormone with
pleiotropic benefits on cardiometabolic hemostasis as an exercise
responsive factor.


And in this study from Aimin Xu from the University of Hong Kong,
the authors investigated a six-week exercise intervention program
in FGF21 knockout mice and wild-type litter mates that all had
diabetic cardiomyopathy induced by high fat diet and injection of
streptozotocin.


Dr. Carolyn Lam:


Nice. So what did they find?


Dr. Peder Myhre:


Yeah, the authors found that exercise lowers circulating FGF21
levels, therefore remodeling the heart as an FGF21 sensitive
target organ. And the protective effects of exercise against
diabetic cardiomyopathy are therefore compromised in mice with
deficiency of FGF21. They also identified Sirtuin-3 as an obligor
downstream effector on FGF21, preserving mitochondrial integrity
and cardiac function. Finally, the authors demonstrated that
FGF21 induces Sirtuin-3 expression through AMPK-FOXO3 signaling
access.


Dr. Carolyn Lam:


So could you put that together for us better? So what are the
clinical implications?


Dr. Peder Myhre:


So the clinical implications from this paper is that circulating
FGF21 is a potential biomarker for assessment of exercise
efficacy in improving cardiac functions. And exercise is a potent
FGF21 sensitizer in cardiomyocyte and has the potential to
enhance the therapeutic benefits of FGF21 analogs in diabetic
cardiomyopathy, and selective activation of FGF21 signal in
cardiomyocytes may serve as exercise mimetics and represent a
promising targeted intervention for precise management of
diabetic cardiomyopathy.


Dr. Carolyn Lam:


Oh my goodness. That is fascinating. Thank you, Peder. Well let's
wrap up with what else there is in today's issue. There's an On
My Mind paper by Dr. Weir entitled, “The Emperor's New Clothes:
Aren't We Just Treating Grades of Heart Failure with Reduced
Ejection Fraction.”


Dr. Peder Myhre:


And there is a Research Letter by Dr. James Martin from Baylor
College of Medicine entitled “Gene Therapy Knockdown of Hippo
Signaling Resolves Arrhythmic Events in Pigs after Myocardial
Infarction.”


Dr. Carolyn Lam:


Very nice. Thanks, Peder. So wow, let's go onto a featured
discussion on AI for contrast-free MRI and a virtual native
enhancement here coming right up.


Dr. Peder Myhre:


Awesome.


Dr. Carolyn Lam:


Now we all know that myocardial scar is currently assessed
non-invasively using cardiac MRI with late gadolinium enhancement
as what we would call the imaging gold standard. Wouldn't it be
amazing to have a contrast-free approach, which could provide the
same information with many advantages such as a faster or cheaper
scan, and without contrast associated problems? Well guess what?
We're about to discuss that today in a feature publication in
today's issue, and I am so pleased to have the co first authors
with us today. They are Dr. Qiang Zhang and Dr. Matthew Burridge,
both from University of Oxford, and to discuss it as well, our
senior associate editor, Dr. Victoria Delgado from Barcelona. So
welcome, everyone.


Qiang Zhang, could I start with you and ask you, I understand
you're a machine learning expert, which means you're probably
smarter than all of us here. Could you maybe explain in simple
terms what made you and Dr. Burridge do the study?


Dr. Qiang Zhang:


First? Thank you so much, Carolyn and Victoria, for the
invitation. As you have mentioned, late gadolinium enhancement,
or LGE, has been the imaging gold standard in clinical practice
for myocardial catheterization including scar assessment for
patients with myocardial infarction. However, LGE requires the
injection for gadolinium contrast, and this is cautioned in some
patient groups and increases the scan time and cost.


On the other hand, pre-contrast CMR such as Sydney T1-T2 mapping,
a gadolinium-free alternative for myocardial catheterization. But
their clinical use has been hindered by confounding factors and a
lack of clear interpretation. So with our cross deceptor team at
Oxford, we developed an artificial intelligence, virtual native
enhancement technique VNE.


It can produce a sort of a virtual LGE image but without the need
for gadolinium contrast. And we have previously tested it in
patients with hypertrophic cardiomyopathy as published in this
journal last year. And in this new study together with Matt here,
we tested in patients with history of chronic or prior myocardial
infarction.


Dr. Carolyn Lam:


Oh wow. Cool. So audience, you heard it. Instead of LGE, we now
have VNE, virtual native enhancement. That's super cool. Thank
you. Matt, could I bring you in here? So tell us a little bit
more about the population you studied and what you both found.


Dr. Matthew Burrage:


Yeah, absolutely. And thank you so much for the invitation as
well. So as Chang has said, this was a single sensor study that
we performed at the University of Oxford and specifically
targeting assessing myocardial scar in patients with a history of
chronic or prior MI. So we had two sources for our population
data. Well, first we used our real world clinical service data
from our institution.


So we screened 11 years worth of patient data for presence of MI.
So patients were included. There was a evidence of a previous MI
based on an ischemic pattern of LGE, but we specifically excluded
patients who had an acute presentation, or if there were features
of acute MI on the CMR scan such as presence of myocardial edema
or microvascular obstruction. The reason for this is we wanted to
keep this as a clean population to avoid the potential
confounding effects of myocardial edema or MVO on native T1
values. And so we also excluded other myocardial pathologies such
as underlying cardiomyopathies and infiltrative diseases.


A second population dataset came from the OX Army study, which is
a single center prospective study of patients presenting with
acute MI. And for these patients we used their six month follow
up scan to again avoid the confounding effects of edema and
pathology. So overall we had a total of 912 patients who have
contributed over 4,000 image data sets. The patient
characteristics, 81% were male, they had a mean age of 64 years
and there were cardiovascular risk factors such as diabetes
melitis, hypertension, hypercholesterolemia in 20 to 40% of
patients, while just over half had a history of previous
revascularization.


We also separately applied the VNE technology to a pig model of
myocardial infarction, which was thanks to our collaborator,
Rohan Domakuma in the US. And so those were scans performed eight
to nine weeks after an induced MI in the LAD territory in a
series of pigs. And so this gave us the ability to provide a
direct comparison between LGE, VNE, and histopathology in this
model.


Dr. Carolyn Lam:


Wow. And results?


Dr. Matthew Burrage:


So what we found and the key results were firstly that VNE
provided significantly better image quality than LGE, and this
was on blinded analysis by five independent operators from our
test data sets. Secondly, the VNE correlated strongly with LGE in
terms of quantifying infarct size and the degree of
transmurality, so the extent of the MIs in our test data set. We
had pretty good overall accuracy of 84% for VNE in detecting scar
compared to LGE with no false positive VNE cases.


And finally there was also excellent visuospatial agreement with
the histopathology in the pig model of myocardial infarction. So
really this, we think, is a technology that provides clinicians
with images in a format that firstly they're familiar with, which
looks like LGE, provides essentially the same information as LGE,
but it can be achieved without the need for any gadolinium
contrast agents and can be acquired in a fraction of the time.


So it takes less than one second to generate the VNE image. So as
we've said before, we feel there's a lot of potential here for
this technology to potentially eliminate the need for gadolinium
contrast in a significant proportion of CMR scans, reduced scan
times and costs, increased clinical throughput and hopefully
improve the accessibility of CMR for patients in the near future.


Dr. Carolyn Lam:


Oh wow. That is tremendous. So first of all, congratulations to
both of you. Before I ask Victoria for some thoughts, could I
also just check with Qiang Zhang, because all AI algorithms need
to be externally validated or surely there's some catch to it, or
so-called limitations, or something else you may study. Could you
maybe round up by saying is there anything that clinicians should
not be applying it to or be aware of some limitations or?


Dr. Qiang Zhang:


Thank you, Carolyn. So a limitation of this study is that the
dataset that is used for developing the models, the majority of
them are patients around six month after the acute infarction. So
where the myocardial infarction is still evolving, which may
include residual edema and microvascular obstruction, and that is
difficult to assess using the current VNE model.


And also we found it challenging to assess small sub endocardial
infarction and actually to address those limitations, we are
working on improving the VNE models, training it on even larger
data sets and training it on LGE to detect small sub endocardial
function. And we will further develop it to detect, for example,
acute edema and a microvascular obstruction, and in the meantime
develop quality control driven AI models to inform the clinical
users of and unreliable results.


Dr. Carolyn Lam:


Wow, thank you. So Victoria, now I'm dying to hear your thoughts.
How do you think this fits in the landscape of all AI imaging
now?


Dr. Victoria Delgado:


I think that it's an excellent development and I congratulate the
others for the article and the proof of concept that we can move
away from the late enhancement and the use of gadolinium
enhancement. I think that this is a major step forward because as
Matt said, they are going to decrease very much the time of
scanning and the post processing because is automatically done as
far as I understand. So even if you can interpret yourself the
amount of so-called virtual enhancement, the system gives you a
value for that extension of the virtual in non-gadolinium
enhancement. So that reduces very much the variability that can
be in each observer if that is done automatically.


But my question to them is also if that can be influenced by the
type of scanner that you use, for example on echocardiography,
that's much more my field of interest, it depends very much
sometimes how the images are processed of which are the vendors
that we have used to acquire the images. Is this a limitation for
your software? Can you foresee there some variability or is
completely independent?


Dr. Qiang Zhang:


Thank you, Victoria. So we are aware of actually the difference
of the data produced by different scan of vendors and the
advantage of AI-driven methods is that it is data driven. So we
plan to incorporate dataset from other vendors so that the trend
that VNE models can work with like multiple scanner vendors. This
actually will be done alongside the ongoing standardization
program of T1 mapping in our group, which is the underpinned
technology for VNE. And this is led by Professor Stephan Pitchnik
and Vanessa Farrera. And we actually hope the VNE technology as
AI driven methods could contribute to a solution to the CMO
standardization between the scanner vendor.


Dr. Victoria Delgado:


And another question, if I may follow in this CMR, it has been
proposed as a very valuable imaging technique to assess infarct
size and to see the efficacy of some therapies to reduce the
myocardial infarction size. How do you think that this new
methods will impact in future trials and the way we have been
interpreting the previous trials, like for example, the one that
you use for the validation?


Dr. Matthew Burrage:


Yeah, thanks Victoria. It's a really, really excellent question.
I think there's a lot of potential for the new VNE technology to
also become a clinical endpoint in some of these trials in terms
of reduction in infarct size, because the information that we get
is more or less the same as we get from the LGE. So there's lots
of potential that we can, again, use this as a biomarker in
trials for looking at reduction in infarct size and reperfusion
therapies. But it has the benefit that it can be done quicker and
without gadolinium contrast.


Dr. Victoria Delgado:


This is amazing guideline and really I would have a lot of
questions for them as well. And knowing the literature, for
example, in the Scenic center in Madrid that they have been
scanning the evolution of myocardial infarction from 0.02 weeks
to see how this would translate with your technique. That will be
amazing to understand how this can be done.


Dr. Carolyn Lam:


Oh wow, there you go. New research idea right there. Well how
about if we end with a very quick question for each of the first
authors. So maybe Matt, you could start, I mean is this ready for
primetime and clinical use? And if it's not, what needs to be
done to get there? In other words, where are you headed as the
next step?


Dr. Matthew Burrage:


So again, thank you, Carolyn, that's a really excellent question
and I think the next step before this becomes ready for primetime
clinical use is validating this technology really across the
spectrum of other myocardial pathologies. So the next work that
we are developing this on is in patients with acute myocardial
infarction, and then extending this to sort of acute inflammatory
conditions like myocarditis, other non-ischemic cardiomyopathies,
things like amyloidosis as well.


So this will be the next step into rollout and we are looking to
track things like VNE burden and how that relates to clinical
outcomes, similar to the previous LGE papers have done across
different myocardial pathologies, but then ultimately aiming
towards clinical rollout within the next few years.


Dr. Qiang Zhang:


Yeah, I think pretty much what Matt has said, we're going to
develop the deep learning methods and test it further on pretty
much the whole spectrum of commonly encountered diseases, and
then more complex pathologies such as acute pathologies like
edema, microvascular obstruction, and then we test on large
population study like UK Biobank and other prospective clinical
trials. And of course the most importantly is to roll out for
real world clinical use. And as Matt said, we are aiming to do
this within the next two to five years.


Dr. Carolyn Lam:


Wow, this is amazing. Both Victoria and I said thank you,
congratulations on this landmark piece of work. Thank you for
publishing it in circulation. Audience, thank you for joining us
today from Greg, Peder, myself. You've been listening to
Circulation on the Run, and don't forget to tune in again next
week.


Dr. Greg Hundley:


This program is copyright of the American Heart Association 2022.
The opinions expressed by speakers in this podcast are their own
and not necessarily those of the editors or of the American Heart
Association. For more, please visit ahajournals.org.