Hello, and welcome to this episode of the
Physics World weekly podcast,
which is sponsored
by Sun nuclear.
Our guest is the medical physicist,
Todd McNutt,
who's in conversation
with Physics World's Tammy Freeman.
They explore how an artificial
intelligence based tool
can help improve the quality of radiation
therapy plans for cancer treatments.
But first, a message from our sponsor.
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requires
balancing plan quality,
efficiency,
and clinical judgment.
Plan AI,
a sun nuclear product,
supports that process by using large scale,
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By providing
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plan AI helps reduce trial and error optimization,
improve consistency
across planners,
and support confident
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Integrated directly into the treatment planning workflow,
Plan
AI is designed to help teams deliver
high quality plans
efficiently,
consistently,
and with patient care at the center.
Artificial intelligence is a valuable tool used within
many areas of health care, from analyzing diagnostic
medical images
to empowering the individuals that plan cancer treatments
using radiation therapy.
Today, we're gonna be talking about Plan AI,
an AI powered plan quality improvement tool.
Plan AI uses an extensive database of thousands
of previous radiotherapy treatments
to predict the lowest possible radiation dose to
healthy tissues for each new patient.
Treatment planners then use this information
to define goals that streamline and automate the
creation of a best achievable plan.
In addition to discussing the benefits that Plan
AI brings for radiotherapy
patients and cancer treatment centers,
We'll also take a look at the platform's
evolution
from an idea developed by OncoSpace,
an academic collaboration at Johns Hopkins University,
to a clinical product offered today by Sun
nuclear, a US manufacturer
of radiation equipment and software.
I'm pleased to be joined by Todd McNutt,
associate professor of radiation oncology physics at Johns
Hopkins University
and the founder of OncoSpace.
Welcome to the podcast, Todd.
Why, thank you for having me.
So first of all, can you share the
story of how the OncoSpace
project
began back in 2007?
Sure. Yeah. We,
back in 2007
is when we really had,
initiatives to go electronic in the clinical environment.
And there were several groups
discussing how we could better use,
clinical data for discovery and knowledge generation.
And I was in charge with,
moving our clinic to an electronic platform,
And we had several meetings with with folks
at Johns Hopkins, and one in particular was
Alex Saleh,
who did, the Sloan Digital Sky Surveys SkyServer
project where he had built a large database
of galaxies and stars and cosmos. And it
became a huge,
research platform where
people could be junior astronomers
using this data. And
from that, there were other initiatives,
like the in health with Scott Seeger, and
and we had a lot of high level
meetings. And
I
taking from that, we really looked at going
electronic and moving
towards structured data collection for patients in the
clinical environment
and marrying that data with the treatment plans,
our radiation dose delivery
treatment plans in a database so that we
could really start looking at how our,
radiation dose distributions across the anatomy would affect
patient outcomes,
and things like that. And we just really
looked at it like that and and took
that opportunity to build it.
Well, that's really interesting that it started from
from the astronomy database and then moving on
to to
the idea of using this for radiotherapy.
So
moving further ahead, what inspired the transition from
this academic research
to founding the company OncoSpace Inc
in 2019,
which was then acquired by Sun Nuclear earlier
this year?
Mhmm. Yeah. So one of, one of the
earlier things we were able to do with
the with the database after we had populated
it with,
many patients
is we were
we had a project with, Russ Taylor and
some students
where we looked at,
how what
what features of patient's anatomy and target volumes
impact our ability to generate
a treatment plan to deliver dose that best
avoids
normal tissues while treating as best as possible
the target binds. And we came up with
a feature set that looked at the relationships
between
normal anatomy and targets and target complexity.
And our earlier work allowed us to sort
of predict
that the expected doses from,
from these shape relationship features, and it worked
quite well. And we published on that a
few times. And so at that point, we
really knew
we could tap into this data and and
offer up this prediction that could help do
treatment plans on a new patient. It was
sort of at that point, we thought of
it as personalized medicine. So
what for this specific patient, how good of
a treatment plan should I be able to
create? And for the first time, we were
able to see that.
And I I always thought that that was
something that was useful commercially, and we could
get it out to other clinics and allow
them to use it. But I didn't have,
like, the strength or the wherewithal to do
that back then. And what it I think
what it really took is my my friend
Praveen Sinha, who I'd known from my previous
work at, Philips and and doing, treatment planning
system development.
And he kinda came around and said, hey.
You know, are you ready to do a
start up with this? And we had talked
about it several times before, and he became
available. And I think that was sort of
the
entrepreneurial
spirit injection to the whole
program. And,
it's just the timing was right for both
of us, and I had a team here
already ready to go.
And so, yeah, we we just went ahead
and did it. And with his knowledge of
doing startups already and my knowledge of what,
we wanted to achieve, it was just the
perfect,
perfect timing and perfect group to do it
with.
Okay. So so this is all led to
sort of the the commercialization
of this Plan AI tool.
Now now this this platform,
it incorporates both predictive planning and peer review.
So first of all,
can you describe what predictive planning means in
the context of radiation therapy?
Yeah. Sure. So predictive planning just, you know,
the idea is is for a given patient,
if I can predict
the expected dose that I should be able
to achieve for that patient,
then
that makes
an easier problem to solve
for when you're doing treatment planning. Treatment planning
involves
a a a dosimetrist
specifying
dosimetric objectives
to the system,
asking it to then optimize the delivery of
radiation
to meet these objectives that the dosimeter is
specifying.
And common practice now is nobody really knows
what the right objectives are to to even
put in the system. So it's a it's
a a trial and error process trying to
push the dose down here, and you might
be trying to overachieve in one area and
underachieve in other areas, and you don't really
know it because there's so many things going
on. And so by giving a prediction from
from these spatial relationships in this prior data,
it gives a nice
rational set of objectives to that optimization,
and that allows
the the optimization algorithm that's already there in
the planning systems to go towards a good
solution and a more much more solvable problem.
So that's the idea of predictive planning is
is basically
giving
the right objective to those planning systems.
On the peer review side,
that's peer review is when we look, physician
a peer physician looks
at every treatment plan that goes through to
evaluate it for quality and safety.
And in that regard,
it's very fast, and people don't really know
how good a plan you can generate. So
is this a good plan or not?
Depends on the patient's anatomy. It's very hard
to decipher that in a three d sense.
And so by providing a predicted
dose,
and dose objectives and dose goals like this,
it allows a very quick review
to see and highlight,
is this a high quality plan or not?
And so that's the peer review setting. So
it's just two different settings. One's a dosimeter
just using it in a in in a
planning sense. The other is a peer physician
evaluating the quality
of a plan that's been generated.
Okay. So so just, basically, these objectives, they're
to sort of maximize the dose delivered to
the target, the tumor that you're trying to
destroy,
minimize any radiation to everywhere else, particularly sort
of vital organs that might be nearby.
And then
the,
the peer review
process is a sort of a a second
check to make sure that this has all
worked
okay.
And then sort of what what types of
anomalies can
AI detect, for example, in
the prescriptions,
contours,
treatment plans? What what sort of things does
it find?
Yeah. And yeah. So with, anomaly detection more
focuses in on peer review, but also can
be used as a preplanning evaluation step. But,
you know, one of the peer review is
a very hurried process. It's you're you know,
you have a peer trying to look at
all these plans coming through, so it's very
fast paced. And evaluating contours can be a
little bit of a challenge. So we've done
some work, in the past looking at simple
things like
is, is a contour
is it missing slices missing contoured slices and
things like that? Does it,
does it have discontinuities in it? So those
are very basic and maybe not AI,
but can sort of evaluate the quality of
a contour, which is, you know, the identification
of anatomy on a on, like, a CT
scan.
But other work that you can do with
more AI is
look at all the rectums that are in
the system. And if your contour maybe goes
too far into the sigmoid,
then we can detect, hey. This rectum has
an anomaly. It doesn't look like all the
other rectums.
So have you
miscontoured that or contoured it beyond where it
should be? And these are the kinds of
things that physicians might look at in peer
review, and this can then highlight and bring
attention to things that might be anomalous
so that they don't get overlooked.
Prescriptions also are a a bit of a
challenge.
Prescriptions is as much as,
you know, they are the physician's sort of
realm and responsibility, but they're one of the
hardest things to double check because a peer
peer physician doesn't really know everything about that
patient. What is the dose
per fraction and how many fractions do you
wanna deliver?
And depending on the disease site, the location
of the tumor,
the diagnosis, and the intent,
you know, whether it's curative or palliative intent,
these prescriptions
are kind of under protocol. And so AI
can highlight when prescriptions are anomalies or different
than normal,
in which case they deserve a double check
or a second look. There have been instances
where somebody's
prescribed
six hundred centigrade per fraction for five fractions,
but they really meant five hundred degree
for six fractions, for example. And those kinds
of anomalies can be detected from a safety
perspective.
Okay. So it's just picking out things that
look a bit unusual, and then it can
highlight to the the people that are working
on the system, make sure everything's okay.
Right. But unusual is a
a difficult word. Right? Because unusual is it's
a pretty broad base of inputs to determine
what's unusual.
Yeah.
So, I mean, the Plan AI models,
these are constructed
and trained
using the OncoSpace data lake. Now this is
that knowledge database of all the previous treatments.
Can you tell us a bit more about
this data lake?
Yeah. So in the early days, when when
we first started, we developed a large SQL
database. We wanted all of these
shape relationship features and dosimetry features and everything
directly queryable from a from a from a
database. And we had originally designed it in
SQL,
and the SQL link the SQL language is,
you know, very nice for being able to
query out data and manage and sift through
the data.
And then,
but when, the company was formed, we recognized
that there was,
you know, some age to that and some
linkages to machine learning algorithms and stuff that
weren't quite as clean as some of the
more modern technology that was available.
So in the,
in the the Plan AI now data model
or data lake, there we extracted all of
the different shape relationship features and shape complexity
features
and put them into a parquet,
database up in the cloud.
And in that cloud now, it's linked to
a lot of the machine learning tools available
in the Azure platform. So it even it
made it made a data lake much more
amenable to applying some of the machine learning
algorithms to it. We still maintain the, SQL
data lake here at at Johns Hopkins
where we primarily focus that, our efforts on
toxicity predictions and looking at spatial patterns of
dose and things like that. So it's taken
a couple of different forms, but on the,
yeah, on the plan AI side, it's,
I would say, streamlined for the specific task
of those prediction.
Okay. And what does the model training process
look like?
Yeah. So,
so with the the model, that was another
thing that, you know, in our earlier work,
we had
sort of a simplistic or model of doing
the dose prediction where we knew we could
do a a pretty good job back then.
Julie Shade, who is actually,
the student here, data scientist that had worked
with me in the in the past, She
joined the company
and,
really took a whole another look at, the
model and the training the model and came
up with a nice,
random forest ensemble model. And
there's a lot of things. So the first
step you have to do is really good
data curation, and
we had a lot more data to look
at and a lot more,
anatomical regions to look at. So one of
the first things we need to do was
curate the data by standardizing the org normal,
a normal anatomy naming.
So there's a task task group two, t
g two sixty three from the APM defines
a
standardized structure naming model, and not all the
data has that.
So so she wrote some fancy,
kind of learned
tools
that can, do automatic name mapping, and it
also does automatic target,
identifications
because treatment plans can have multiple targets, two,
three, and four targets within a given treatment
plan. And
that level of curation really helped,
process large much larger amounts
of, data for the model.
And then once we had all the shape
relationship features and shape complexity features and all
the doses,
we could then train the models by anatomical
region. So we have a pelvis model that
includes male and female, so prostate,
GYN,
treatments. We have a thoracic model that includes
lung and esophagus.
We have an abdomen model. And so for
each of these models,
we build or for each of these anatomical
regions, we build a model for each
organ at risk to predict the doses,
based on that feature set.
Then and that's with the random forest algorithm.
And the the shade relationship features actually really
do a nice job of separating out
similar patients and separating
them out through the trees.
Then we just do a training.
Do we do a five fold,
internal validation model during the training to make
sure that the predictions are good on the
internal dataset.
And then after that, we've done an external
validation,
at,
multiple institutions,
including both Johns Hopkins and Montefiore.
I guess one other I guess one other
quick aspect about that,
training is is whenever we drive optimization with
a predictive
planning,
we always wanna be pushing towards what we
call a best achievable dose.
So the prediction if you do a regular
machine learning, you would predict sort of an
expected dose or an average dose across patients.
You never wanna drive a treatment plan towards
the average dose because then every plan you
generate will be happy being the average. We
don't wanna do that. So we predict both
the expected dose, which would be sort of
like the average for patients,
and we predict a fifth percentile level, which
is what we would call the best achievable
dose. So we drive plan optimization to the
best achievable,
hoping that we end up somewhere between
the average and that best achievable or the
expected.
So our model's trained for both both of
those predictions.
Okay.
So another thing to think about is that
when you're implementing
more any new technology in the clinic,
it's important that it fits in with an
existing treatment workflow workflow.
So
what does clinic ready mean
for these AI tools?
Right.
So there's a couple of things that we
incorporated
in.
One is
we we've
built for an anatomical region, we built models
out of a very wide
range of treatment protocols.
And so
and then radiation therapy is pretty protocol driven.
We know what technique we're gonna use. We
know what our clinic our dose goals are
for different structures and things like that from
a clinical
perspective and how we wanna treat.
What we don't know is the patient specific
part of that.
And
so
one
strategy that other vendors and things have used
is that for a given single protocol,
we train some kind of a model to
treat that one protocol.
And then that means that anybody using it
has to train for every single protocol they
may or may not wanna use.
And that was that's very complex and very
hard to implement in a clinical setting.
So our models, we have a for any
for each anatomical region, we have a very
large variety of different types of patients and
treatments in the data because it's a large
dataset,
and the protocols fall under
it. So we made sure that you could
write any protocol for treatment that you wanted,
and the same prediction model will be able
to predict for that protocol. So that means
any user
can put in however they really wanna do
things and the predictions work. They don't have
to retrain anything. They don't have to do
any so that's one aspect of being clinic
ready. It's ready to go out of the
box,
and there's a library of protocols to start
with, and then you can change protocols as
you need for your own clinic.
The other part of it being clinic ready
is, I would say,
aligning with the way,
the planning currently
happens.
We currently use dose volume
based objectives. So this is
it's called a dose volume histogram, but we
when treatment plans are performed and optimized, we're
manipulating these little dose objectives on the points
to drive the optimization, and that's exactly what
we predict.
So the
the users are using their treatment planning system.
We're merely just putting the objectives in there
so they're not changing the whole paradigm of
how they operate. They're not just running an
automated plan and then not knowing what to
do with it afterwards.
They're being able to run this automation and
be left in a position where they can
do exactly what they normally do after that.
So both of those, I would say, are
the clinic ready part of it. Yeah.
Okay. And and then can you describe some
of the clinical validation studies that were performed
for Plan AI and what they found?
Yeah. So, obviously,
we have to do external validation, and we
you know, I guess they're not too challenging
to do, but we looked back at
recent treatment plans that we had
and found clinical plans that have had the,
you know, evaluation
of the physicians in clinical practice.
And we did this at both, Montfiore and,
at Johns Hopkins, and we I don't even
remember how many
plans we did, but we ran a whole
bunch of predictive plans blind from the clinical
plans that were done and then did evaluation,
between those predictive plans that were were run-in
the in the clinical ones.
And
for almost all of the cases, the doses
were improved. I mean, it was very obvious
that,
you know, there are certain
parts of the volumes that
the the dosimeter is doing their trial and
error planning, not knowing how good they could
do,
left ignored. Right? And so we
improved there pretty well.
And so it's it was pretty clear that
both
plan quality and plan efficiency,
were improved. The predictive plans,
can be run with very few modifications,
to the treatment objectives
and very few iterations of the optimization
where the clinical plans just take this trial
and error process to make it take much
longer.
And then in one instance,
we also had, blind clinician reviews.
So we had three physicians blindly review,
the because this was for the prostate,
and,
two out of three,
physicians picked the predictive plan, and most of
them all three predicted
picked the, predicted plan over the clinical plans
in that study.
Okay. And then how does the Plan AI
tool integrate with existing treatment planning systems?
Right. Yeah. Actually, maybe I'm a little proud
of this, but back when I was at,
working for Philips and on the Pinnacle treatment
planning system, one of the early things we
did was write,
a scripting interface for,
access to our to control the planning system.
And I think, we were the first to
do that years ago, but the other vendors,
RaySearch and, and Variant Eclipse, they've all followed
suit and provided these
scripting interfaces to their treatment planning systems.
And so the way we've designed the protocols
in the plan AI tool is it allows
us to define prescriptions,
the beams to be used, the different phases
of treatment, which we might call a beam
set or a plan,
and then all of the objectives and as
well as the clinical goals. And we define
those in a protocol.
And then for both of the two
to their treatment planning vendors,
we through that scripting interface, each element of
that protocol can be executed through a script.
So
so from the planning system that the dosimetrists
are using,
they can request
this protocol to be executed in their in
their system,
and it will go through. It'll create the
plan. It creates each of the beam sets,
creates all the beams, sets the prescriptions,
and then it will load all the objectives
into the system and have it ready for
them to optimize
like that.
And, yeah, it works out quite well. And
we were, you know, smart not to make
commands that are too challenging. They're very they're
very simple set of commands
that, that the planning system vendors provide,
open access to.
Okay. And then
my next question, I guess we've touched on
this a little with the idea of being
clinic ready, but,
did you encounter any particular challenges
bringing big data and AI into the clinical
radiation oncology setting?
Yeah. I, that's
twofold.
You know, interestingly enough,
the it's not the challenges aren't really technical.
The challenges
are are more human related. Right?
I guess probably one of
the more systemic
challenges
is on, you know, the use of of
medical data for training and things like that.
There's always,
data security.
The IRBs are always, you know, questionable about
how you're using data. And it's another nice
thing about our system is the feature set
that we need to generate from the treatment
plans are really just these, like, mathematical features
of shape relationships and don't involve a lot
of identifiable information.
But you have to get through that whenever
you're using any kind of medical data with,
you know, review boards and things like that.
On the,
I guess, implementation
side from a clinical side,
you know, there's a healthy skepticism
of AI performance.
In our in radiation therapy, there's been a
lot of AI on auto segmentation.
So contouring tools from images and things like
that. And, you know, early efforts were not
so good or not as good, and people
had to modify things. So there's there's always
this good healthy skepticism.
But I think once you show to them
that, it works and it works well and
they still have control over it after it,
that can be overcome.
I have seen and and I I would
say probably with some
of the older planners and dosimetres, there is
some fear of job security. People are always
a little bit worried about
AI taking over what they do.
There's an art. Right? I mean,
treatment planning and radiation therapy is you know,
we're they're medical professionals. They're they're they're designing
this treatment plan to care for a patient,
and there's a lot of pride
and art in that. And if you automate
that,
that's
taking away some of that art and some
of that pride.
And
it's,
I think it's a little bit challenged to
overcome. And and people say, you know, your
job's not gonna be taken over by AI.
That's gonna be taken over by the people
that embrace AI and use
it. Right? And and that's that's true to
some extent. And and I guess there's other
things.
Yeah. I mean, I think that's a little
bit to overcome.
But maybe, you know, if you're if you're
automating the monotony
and give and and and show them that,
really, no. We're helping you get to this
point where where you can apply your art
more often
and and and more meaningfully
rather than spending all of your practice trial
and error trying to get to something that
may or may not be able to be
achieved. Well, hey. No. We'll put you right
there where you can achieve it. Now you
can advance it from that point forward, and
I think that can help a lot. Yeah.
Yeah. I guess so. It's it's,
working
together or using AI as a tool rather
than replacing what you do, a tool to
help you
in what you're doing. So Yeah. And you
don't want a paradigm shift. You don't wanna
make it this totally different paradigm on how
something gets done. Wanna, you know, give them
put them put them in the same place
that they're already working would help them. Yep.
Yeah. So so looking ahead, where do you
see predictive modeling in AI in oncology,
say,
five years from now, and how could it
impact patient outcomes?
Yeah. So, you know, right now, I think
there's still some more advancement to go,
more body sites and, you know, broader ranges
of of,
of data to improve some of the models
to get more
specific. So I think one of the areas
is growing the current models to the next
you know, to to fill out the rest
of the body sites, things like that.
Another thing that I always think about is,
you know, once you get going really with
the concept
is getting multiple centers in sort of a
consortium
adding to this pool of of knowledge and
data so that it truly becomes
sort of the prior visions of a learning
health system.
You know, right now, there's been a lot
of data collected. We built a model. You
can train the model, and you can do
the predictions.
But, really, you want that data to advance
and learn. So you so having multiple centers
contribute back to that knowledge base as they
advance or as they change,
can
be
can be of a huge value, and and
a community environment like that should be able
to work. I see no reason why it
can't work, and it it should be great.
In terms of sort of patient outcomes
and things like that, we do a lot
of work here.
One of the things with normal anatomy is
we we tend to look at
where the dose goes in this ball of
anatomy,
and it assumes that every
location in that in that anatomy is equally
sensitive to radiation
and
equally important to the function of that anatomy.
And that's sort of because it's complex. So
we we we try to make it simple
and think about it that way. And
the a lot of the work we've done
here is to look at how a spatial
pattern of dose on a complex piece of
anatomy
impacts toxicities and outcomes.
So if, you know, if I have a
parotid gland and I'm irradiating
stem cell region too heavily,
maybe I destroy the ability for recovery down
the road. But if I'm just irradiating
sort of the acinar cells responsible
you know, the outer acinar cells that produce
saliva,
then maybe it's not so bad because it
can recover in a year post treatment. And
so looking at how that pattern of dose
impacts,
the toxicities is something that's coming.
I think that I would call that discovery
mode and trying to use AI to advance
our own knowledge of how radiation impacts the
anatomy.
But I do see down the road that,
some of these same concepts where we predict
those, we could predict normal tissue outcomes
and things like that and feed that into
the planning process to help reduce
that at the patient level.
So those those, I think, are,
some important advancements.
Yeah.
And do you have any advice
for clinics interested in adopting this AI driven
planning?
Yeah.
Yeah. In general,
you know, introduce it as an assistant. Don't
introduce it as a solution,
you know, to get you want the the
current people that know what they're doing
to be able to use their knowledge even
better and more efficiently.
And then,
you know, we wanna make their jobs easier
and show them that it also improves that
quality.
And I think once once that overall value
is realized and sort of
in the clinical environment where they they see
that, wow, you know, I can go right
at this predicted solution. And in peer review,
we can see, hey. This plan is at
the level of quality that we expect, that
we predict. We're no longer guessing in our
minds. We're not arguing back and forth.
Like, a lot of times with with the
dosimeters, you know, they're they've got a plan.
They've worked really hard getting this dose down,
and the physicians
looks at it and goes, oh, certainly, you
could do better, not knowing what they've done.
Right? And so here, it just gives them
that that confidence that they're right there. And
so once they have that,
you know, once they realize that and they
kinda remove those initial doubts and start using
it,
then
you really can start bringing more value from
it.
You could start using it for adaptive planning
and and other things
because
you've gained that level of constant confidence and
you're not spinning your wheels on it. And
and then you can use it to leapfrog
some of the other technologies and buy people
time.
I guess one of the quotes I would
say that I've I've often said is, you
know, why don't we automate the easy easier
redundant things that you can spend your quality
time on the more difficult and challenging cases?
Because that's where
your your talent might be even more needed
rather than doing the redundant over and over
repeat things that maybe are a lot easier
easier more easily done.
Okay. So what what's been the most rewarding
part of this journey for you personally?
Yeah.
I mean, on the predictive planning side I
mean, on the research side, I love the
big data and all the all the knowledge
and discovery we can try to do with
it. But some of that, yeah, it's still
a little far fetched. But from the predictive
planning side,
you know, having my prior experience,
building treatment planning systems
at Phillips and ADAC, you know, we built
a this intensity modulated radiotherapy planning system. And
back then, you know, people were worried. Oh,
the the system is just gonna optimize the
delivery of this treatment plan,
and and and our jobs are gonna go
away and whatever. And,
but the biggest problem we always had is
nobody knew what the objective was. Nobody knew
how to tell the system, this is the
dose I expect to receive.
Now go optimize to get it for me
because
you
you couldn't know what you could do.
And so for any given patient, you could
ask for too much, ask for too little,
and you get the results. So we never
actually knew that. And for the first time,
I I argue we actually know what our
objective is in our treatment planning.
And bringing that,
you know, forward and being able to provide
that to the rest of the world as
a as a tool to use in clinics,
it affects everybody.
And,
so now I can say that. I kind
of rounded out that that intensity mod you
know, that optimization, that plan optimization.
We've rounded it out and finally told people
what they can achieve. Now we just have
to get everybody using it.
Excellent.
So if you could leave our listeners with
one key takeaway,
what would it be?
You know, AI is all the AI tools.
Right? It's,
for the most part, it's all trained on
historical data, prior data. Right? So, really,
it's kind of just telling you what you
can do from what you've been able to
do in the past.
And if you can remember, you're just you're
just have a sophisticated system of of recalling
what you've done before,
showing you what you can do now on
a on an individual patient basis.
And bringing that to light in a useful
way
is just it's helpful. It's and and people
need to
see that and believe that and and appreciate
that. And,
you know, I think it also
in a hurried clinic, in a busy clinic,
you have dosimetres with different levels of experience,
and it really does kind of
level the playing field or bring
a more uniform quality
across
whatever environment you're in, whether, you know, you're
in different countries around the world or different
environments or different busyness of the clinics or
different, you know, experience level of the treatment
planner.
This tool really
brings all that to a uniform state, and
gives the best quality every everywhere you go.
Excellent. Well,
thank you, Todd, for speaking with us today.
Thank you for having me. It's,
it's hard to talk about all this stuff,
but it's great. Thank you so much.
That was Todd McNutt of Johns Hopkins University
and the founder of OncoSpace.
He was speaking to Physics World's Tammy Freeman.
Thanks to both of them for a fascinating
conversation.
And a special thanks to Sun nuclear
for sponsoring this episode.
I'm Hamish Johnston, and our producer is Fred
Iles.
We'll be back again next week.