Yale Interventional Oncology Lab

September 30, 2020

Information

Yale Cancer Center Grand Rounds | September 29, 2020

David Madoff, MD, Julius Chapiro, MD, PhD, Jessica Santana, MS, and Tal Zeevi, MS

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00:00Thank you, thank you and welcome
00:03everyone to Cancer Center,
00:05grand rounds and really pleased with how
00:09we continue to innovate on formats in
00:12this new Zoom World of of the pandemic.
00:16And I think today's session is going
00:19to be interesting given the nature
00:22of of four speakers and various
00:25levels of their their training and
00:28professional development and are.
00:30Cancer Center in our University and
00:33health care system and you know,
00:35really focused on.
00:37The great work that's going on in
00:40radiology and biomedical imaging
00:42and the science associated with it,
00:45most notably in Interventional Oncology.
00:49Please introduce let us say the
00:52the leader for today's session.
00:54Doctor David Madoff is,
00:56as you may recall from last week,
00:59doctor Madoff is the Co director
01:01of Interventional Oncology of the
01:03Interventional and college research
01:05lab vice chair for clinical research,
01:08and the section chief for
01:10Interventional radiology.
01:11An Department of radiology
01:12and biomedical imaging,
01:14and David really has throughout his career
01:17have been an innovator in this space.
01:20And with his joining the faculty
01:22at Yale now a couple of years ago,
01:25I guess has really built out not only the
01:29capabilities in our clinical operations,
01:31but expanding research which
01:32is really exciting.
01:34And so David I turn it over to you to
01:37sort of introduce the other speakers
01:39and share with us all this great work.
01:44OK, thanks Charlie.
01:45So I'd like to again, thank you for giving
01:48me the opportunity to speak in Yelp,
01:50Cancer Center grand rounds.
01:51As you saw it last week I was part
01:54of a session on the multidisciplinary
01:56management of colorectal liver metastases.
01:58Today's session, as you stated it will
02:00be focused on Interventional oncology.
02:02What it is in some of the exciting
02:04research being done in our lab at Yale.
02:07Therefore, this program will focus on
02:09work being done by our trainees and
02:11not really on my own personal work.
02:13I will just be introducing the topics.
02:15And our vision and goals for the
02:18Interventional Oncology Program at Yale.
02:20So this session, as you may recall,
02:23was originally planned for March 10th
02:25and it was just after the kovid pandemic.
02:28So interesting, Lee,
02:29It's actually given us more time
02:32to make our data even more mature.
02:34So for the audience for today.
02:37So I'm really pleased. I think about that.
02:40So I'm going to start and then
02:42will be followed by Julia Shapiro,
02:45who happens to be the Co director
02:48of the intervention college.
02:49Live with me.
02:50He's actually an assistant professor
02:52of radiology and biomedical imaging,
02:55while actually,
02:56still,
02:56and I are resident so that shows you
02:59the level of achievement that some
03:02of our colleagues really have and
03:04then will be followed by Jessica
03:07Santana and tells Evie who are both
03:09graduate students in our lab and
03:12the work will all be talking about
03:14liver cancer and Interventional
03:16oncology related activities.
03:18So let me just share my screen.
03:27OK, so. Really,
03:31what is Interventional oncology?
03:32Well intermixed oncology is a
03:34subspecialty of Interventional
03:36radiology that utilizes minimally
03:37invasive image guided procedures
03:39to both diagnose and treat patients
03:41with various forms of cancer.
03:43The benefits of primary intervention
03:45oncology treatments or its
03:47immediate tumoricidal effects.
03:48As you I'm sure are aware,
03:51there minimally invasive,
03:52resulting in cost reductions
03:54and time Efficacy,
03:55as well as having minimal systemic side
03:58effects leading to an overall improved.
04:00Quality of life.
04:01The goal has been to make the case
04:05for us becoming the 4th pillar of
04:07cancer care and over the years
04:10I believe we have and this has
04:12been shown actually in many ways.
04:15These include having IO therapies
04:17incorporated into multiple NCCN
04:19guidelines which include you know
04:21colorectal metastases include
04:22HP be an even in renal cancer,
04:24having trials to assess the role of
04:27Percat Aneus management of cancer,
04:29and what I believe is most important.
04:32Is that,
04:33I believe now become a valued participator
04:36in most if not all of tumor boards.
04:40So what does it take to
04:42become a pillar of oncology?
04:44Clearly each clinical discipline needs
04:45to have a strong foundation in basic,
04:48translational and clinical research.
04:49We do have some work to do in this regard,
04:53but I must say I was really
04:55thrilled to see when I was in 2016
04:58as an invited faculty of Asco GI.
05:00My discipline next to my name was listed
05:02actually as an Interventional Oncologist,
05:04not an interventional radiologist,
05:06so I was really happy at that time,
05:08and I thought we actually made it.
05:12However,
05:12I really do believe that in terms of research
05:15and education of referring physicians,
05:17I think we're nearly there
05:18and on the right track,
05:20but I don't know if we've come there totally.
05:24So here you can see a long list of
05:28procedures that we do, and I owe.
05:31These can range from image guided biopsy,
05:34primary tumor therapy,
05:35palliative procedures,
05:36central venous access,
05:38an managing of complications that
05:40are either a result of cancer itself
05:44or related to cancer treatments.
05:46So I just wanted to highlight some
05:49challenging biopsies as this is one of
05:51the most important procedures that we do.
05:53Clearly it is difficult for Oncologist to
05:55treat patients in the absence of a diagnosis.
05:58That said,
05:59cases such as this one should be
06:01easily doable for any experienced
06:03interventional list,
06:03and as you can see,
06:05this patient has confirmed
06:08metastatic adenocarcinoma.
06:09So here is the second case,
06:11which I would say is much more
06:13challenging due to lack of any
06:15imaging finding on CT or ultrasound
06:16and you can see here it's closed
06:19proximity to the heart.
06:20Fortunately we were able to get
06:22adequate tissue sampling and help
06:24this patient get the appropriate
06:26chemotherapy that they needed to be on.
06:29So I just wanted to discuss a couple
06:31of primary intra interventional
06:32oncology tumor therapies,
06:34both of which will be discussed in
06:36research by our trainees later in
06:38the session here we have two more
06:40guided ablation or tumor
06:42image guided tumor ablation,
06:43so the goals of Ablation is to eradicate
06:46all valuable malignant cells and stem
06:48spare normal surrounding tissues,
06:50treat tissues with unfavorable location
06:51or pattern of distribution for resection
06:54and or have multiple comorbidities.
06:56These are the most often used in
06:58patients with low volume disease and
07:00required to bulking are typically
07:01done in an outpatient setting setting
07:04and these procedures are repeatable.
07:06So if we can see here we have
07:08radiofrequency ablation and microwave
07:10which are heat based therapies.
07:12We have Cryo Ablation which is a coal
07:14based therapy and we actually have
07:16irreversible electroporation which
07:17is really to electrocute the tumors.
07:19Believe it or not,
07:21by changing the ionic potentials
07:24between the between the membranes.
07:26So ablation of liver tumors,
07:28which is in this was its initial indication,
07:31as shown here in.
07:33This is 1 case of a patient with
07:36HTC that needed ablation as a
07:38bridge to transplant.
07:40As we can see, 2 1/2 years later,
07:43no tumor recurrence or any residual disease.
07:47Next was the case of an isolated
07:49colorectal liver metastasis.
07:50Who here you see a pet positive
07:53or hypermetabolic lesion in the
07:54right lobe of the liver who was
07:57successfully treated with Ablation
07:58and his team are free at one year?
08:01Follow up.
08:03This is a patient who,
08:05instead of having a liver lesion,
08:07has a lung nodule.
08:08Here we can see that based on
08:10having heat based thermal ablation,
08:12the patient did very well at seven months.
08:15As you can see, there's no residual
08:17tumor on the image Ng, and there was no
08:21local recurrence seen at three years.
08:24We can also treat bone metastasis.
08:26This is a patient with metastatic
08:28breast carcinoma with a focal right
08:30femoral and right hip pain limiting
08:32mobility for whatever reason,
08:34she refused radiation therapy
08:35and was treated with heat based
08:38thermal ablation and Samantha
08:39plasty is shown here and she had
08:42an immediate improvement in right
08:43femoral pain from 8 to 10 out of 10.
08:49This is a very interesting use of
08:52thermal ablation in this case,
08:53with cryoablation.
08:54This patient had metastatic Rectal
08:56Squamous Cell Carcinoma who had
08:58severe intractable 10 out of 10 pain,
09:00secondary to inoperable tumor recurrence
09:02infringing on the sacral nerve roots.
09:04She was successfully treated with
09:06partial cryo ablation of the bowel
09:08wall and you can see right here the
09:10low density ice she had immediate
09:13or should say complete resolution
09:15of her pain within about 3 days.
09:17So she had actually no.
09:19Team up until a year,
09:21at which time she actually died
09:23and then Lastly we were able
09:26to treat this liver tumor here.
09:28That was a budding the left
09:31hepatic bile duct.
09:32So we used Ayari again,
09:34that's basically electrocution,
09:35which is technically a non thermal ablation.
09:38So about nine months later the patient
09:41has no residual disease or recurrence.
09:45We also have more regional therapies
09:47such as transarterial embolization.
09:48In these cases are typically reserved
09:50for patients with higher tumor burdens
09:52or are in neoadjuvant settings,
09:54or in cases where resection or
09:55ablation is in a location considered
09:58terribly difficult or dangerous.
10:00He We see a case of a patient with HIV,
10:03cirrhosis and a 4.8 centimeters segment.
10:068 HTC Embolization was performed
10:07to maintain the patient on the
10:10transplant waiting list.
10:11We see completing the Krosis,
10:13and she alternately underwent a transplant.
10:15This is a patient with compensated HTP,
10:18sarot sis, Anna Sala,
10:20Terry HTC and segment 8 and a platelet
10:23count of 57 who also needs treatment.
10:25There's a bridge to transplant.
10:27This tumor is shown here is in
10:29a very difficult location,
10:31and she successfully underwent
10:33embolization with no residual tumor,
10:35and this patient also underwent
10:36liver transplantation.
10:40Here we're able to see a patient that was
10:43treated in their 90s who has a large HTC.
10:45In this case, the ACC actually
10:47ruptured through the capsule,
10:48and as you probably are all aware,
10:50this is associated with poor, if not dismal,
10:53prognosis were able to successfully
10:54treat it with radio embolization,
10:56which was done as an outpatient,
10:58and this can be seen here.
10:59She had complete response and lived
11:01up to five after four years later,
11:03and then Lastly.
11:04This is a case of a patient that has BI lo
11:07or HTC with extensive portal vein tumor.
11:10Rambus, so typically this patient would
11:12have less than six months to live,
11:14but as expected, this is a relatively
11:17young patient with the family,
11:19small young children, etc.
11:20So we try to offer procedure to help this
11:23patient an we did Chemoembolization,
11:25and Fortunately we were successful in
11:28prolonging his life for about 16 months.
11:32So to wrap up,
11:32I just want to give you my five year of
11:35vision for a modern academic evidence
11:37based intervention on college program.
11:39First we want to increase the IO
11:41clinical research programs throughout
11:42the smilow cancer hospital and
11:44kiss centers throughout the area.
11:46Further,
11:46it will be important to improve the
11:48education for the community provider of
11:50the procedures that we offer and which
11:52patients are more likely to benefit.
11:54We also need better integration of care
11:56into current and future oncology workflows.
11:58We will be working on initiating some new
12:01clinical trials related to liver oncology.
12:03As discussed last week
12:04on liver generation was,
12:06which was dragging one study,
12:07but we also are going to be
12:09involved in the leap 012 study,
12:11which is going to be an immunotherapy
12:14chemo embolization plus or minus
12:15immunotherapy that we're going to be
12:17initiating or starting very soon.
12:19And then it's also going to be
12:21critical to have faculty who are
12:23sub specialized to be able to
12:25provide this level of advanced air.
12:28We also plan to make image guided
12:30biopsies more readily available and
12:32hoping to improve turn around time.
12:34There have been ongoing discussions
12:36with Doctor Chen Lu as you all
12:39know is the chair of pathology.
12:41To work on a by an image guided
12:44by over Pozza Tori.
12:46As will be seen from the next speakers,
12:48we are currently working on advanced
12:51preclinical and Translational
12:52research with a focus on molecular
12:54imaging immuno oncology.
12:54We're also very active in machine
12:57learning and artificial intelligence as
12:59it relates to liver cancer and based
13:01on these interesting topics we hope
13:03to be able to achieve and expand our funding.
13:05And Lastly we have a newly
13:07established IR residency program.
13:09In this we would like to develop an
13:12Interventional Oncology Fellowship and
13:13we are looking into the possibility of
13:16applying for a T32 training grants.
13:18So with that,
13:19these are visions and goals I'd like
13:22to then introduce my Co director
13:24for the Interventional Oncology
13:26Research Lab at Yale,
13:28who will be discussing
13:29quantitative biomarkers,
13:30molecular imaging in artificial intelligence
13:32to guide the therapy of liver cancer.
13:35So thank you for your attention.
13:46Alright, thank you very much everyone.
13:48I'm really excited to be presenting this
13:50topic on behalf of our lab and this is
13:53more of a vision presentation of what we've
13:55accomplished and a couple of thoughts
13:57that we put together over the last years.
13:59None of this would have been possible without
14:01really vast infrastructure of collaborators,
14:03which I'll be talking about
14:05a little bit later.
14:06I want to say that I very excited about.
14:09David joining the group here and really being
14:11our new leader and Interventional Ecology,
14:14and we all completely subscribed to
14:16the vision that she just provided.
14:18So as you know,
14:20I'm going to be focusing on the liver cancer,
14:23and primarily I'm going to start
14:25off with a BC else staging system.
14:28The most recent one was released in 2018,
14:30and even in that more recent addition to
14:33it we have seen a clear separation between
14:36intermediate station advanced stage disease,
14:38so they exist.
14:39Apparently in two separate planets,
14:41however,
14:41really in clinical practice and in signs,
14:44this is very transitional area where both
14:46seemed to be interacting quite extensively.
14:48We saw a lot of clinical trials
14:51focusing an overlap of those therapies,
14:53and this is exactly the realm where
14:55most patients are being diagnosed
14:57and also the realm where you know
14:59we think there is most room of
15:02improvement for patient outcomes,
15:04so systemic therapies for HTC then
15:06and now I borrowed the slides for
15:08from Agusan Abou Alfa, who's?
15:10Medical Oncologist at Memorial Sloan
15:12and this is how the market look like.
15:15Five years ago we just had one drug
15:17and this it's not even a complete view.
15:20This is where we are right now.
15:22So with him just five years,
15:24we have so many drugs being approved and
15:26most of 'em actually are very dedicated,
15:29specific targeted molecules
15:30and checkpoint inhibitors.
15:31So this flood of novel agents
15:33actually probably means the left
15:34ship of systemic therapy in the CL.
15:36See staging system and what we will
15:38witness is a greater overlap between local,
15:41regional and.
15:42Systemic therapies of immunotherapy.
15:44And this is where we come from.
15:47We focused now on the biomolecular mechanisms
15:49in the tumor micro environment behind
15:51those therapies and their combination.
15:53So we focus on two of the well known
15:56hallmarks of cancer that are particularly
15:58relevant for HTC specifically on its
16:00ability to avoid immune destruction
16:02and the deregulation of seller
16:04energetic into metabolic phenotype.
16:05So let's have a look at the HTC cell,
16:09so we now it has a very pronounced
16:11Warburg effect,
16:12and this is specifically a cell
16:14that is extremely dependent on
16:16the glucose metabolism.
16:17And so called the Arabic and
16:19anaerobic glycolysis, and well,
16:21the glucose is being taken up with that sell.
16:24It produces lactate.
16:25It is that essentially is
16:27D Cup from the Krebs cycle,
16:29and this lacked.
16:30It is then being flooded directly
16:32into the extra seller micro environment.
16:34And because of that there was
16:36dramatic acid ification of the
16:38surrounding tumor micro environment.
16:39And what does that really mean for us?
16:42The extracellular pH and liver cancer,
16:44especially aggressive age species
16:45known to be very, very low.
16:47And now finding say that this is a very
16:50important exclusion mechanism for four.
16:52Immune cells and the mechanism
16:54of immune cell exhaustion.
16:56So this has been previously reported
16:58and we now know more than ever that
17:01actually tumor infiltrating T lymphocytes
17:03depend extremely on extra seller acidity
17:06and competitive glucose deprivation,
17:07and that the reversal of this hostile
17:10tumor micro environment can actually
17:12improve their infiltration into the tumor.
17:14An immune response.
17:16So to sum it up,
17:18loads for seller pH is a marker of
17:21tumor aggressiveness and the presence
17:22of Protons and lactate really protects
17:25cancer against an immune response.
17:27It promotes new angiogenesis.
17:28The lactic acidosis plays in multifaceted
17:31role that is not fully understood for
17:33all variable immune cell subgroups,
17:35and the high lactic levels are
17:37actually strong predictors of
17:38metastatic disease and poor outcome.
17:40So where does all this fit in with taste?
17:43So theoretically this is an embolus therapy
17:46that changes the tumor micro environment.
17:49And what happens is that in theory
17:51tastes me potentially exacerbate
17:52a hostile tumor micro environment
17:54that attenuates treatment efficacy.
17:55Let's look at it.
17:57I mean,
17:58it induces a hypoxic injury that potentially
18:00may exacerbate the low pH even further.
18:02It potentially stimulates brand
18:04eugenic signaling and the embolization
18:06even might prevent delected
18:07transport away from the tumor.
18:08So postdates tumor marker environment
18:10was thought to be immune inhibitory,
18:12but is that really true?
18:15I mean,
18:15let's look at HTC in general,
18:17where it stands in terms of Genomic
18:20somatic mutations as opposed
18:21to Melanoma and lung cancer.
18:23Really, HTC is somewhere in the middle,
18:25so the idea of the Immuno and inoculation
18:28with locoregional therapies with
18:29initially discarded because of that.
18:31So,
18:32however,
18:32let's have a look at the pathology
18:34of a tumor treated with taste.
18:36This is a patient with HTC.
18:39Histology was taken 11 days post locoregional
18:41therapies that was transplantation.
18:42What we see we see in necrotic
18:45tumor packed with in this
18:46particular case drug eluting beads.
18:48And surrounding that we see a very
18:51dense immune infiltrate and if
18:52we zoom in We really almost see
18:53a secondary immune follicles and
18:55macrophages and T cells really
18:57accumulating the transitional zone.
18:58But they can't really penetrate
19:00into the necrotic areas of the tumor
19:02where all the good end to Jenison.
19:04Why is that?
19:04So our mission was at this point
19:07to really look at the underlying
19:09mechanisms and how do systemic therapies.
19:11How does in the immune system and
19:14local regional therapies interact?
19:15So we wanted to develop imaging
19:17instruments for noninvasive functional
19:19characterization monitoring of the
19:20tumor micro environment in the setting
19:22of the immune response and taste,
19:24and that was the mission of the
19:26lab that we essentially star
19:27started several years ago.
19:29So the one of the first projects that
19:31I'm going to talk about is recently
19:34published paper clinical Cancer Research.
19:36That focused on the development of an
19:38extracellular pH probe that demonstrates
19:40extracellular pH noninvasively
19:42using spectroscopic methodology,
19:43and I'm actually really appreciate it.
19:46'cause that partnered in design and
19:48work on the on this novel mechanism
19:51with Daniel Komen and if I meet
19:54higher from the Mr Research Center
19:56who have been really instrumental
19:58and great partners in developing.
20:01And we used to model of a rabbit liver tumor,
20:04which is so far the only moderate size animal
20:07model that has a faithful reproduction.
20:09Reproduction of an HTC tumor
20:11environment and the has a high
20:13clinical relevance because we can
20:14do the embolization also image those
20:16animals in real size clinical scanners.
20:19We do have a 3T MRI system for that
20:21and also cutting edge IR suite which
20:24we use in the white Rick with the help
20:27and support of Alston is this group.
20:29So this is an immuno competent host.
20:32Anna hyper glycolytic metabolic phenotype.
20:34An essentially those tumors have
20:35been characterized previously,
20:37is very reminiscent of the actual
20:39tumor marker environment of HTC.
20:40So what is a good way to image pH so
20:43their sensor jeans that you can use
20:46their luminescent probes that you can
20:48translate yourselves with reporters?
20:50But what we in radiology aim for is
20:52really the noninvasive measurement
20:54affects your seller pH and this is
20:56where our msit based spectroscopic
20:58birds methodology comes into place.
21:01This has been established previously
21:02in brain tumors by Daniel.
21:04And we have translated this
21:06essentially to deliver.
21:07This is NMR Spectra,
21:08scopic method that measures the
21:10redundant deviation of shifts and
21:12temperature in the region of the tumor,
21:14and we can generate the 3D extracellular
21:16pH map that provides us with essentially
21:18a very accurate characterization
21:19of the tumor micro environment.
21:21So if we look at the tumor in by
21:24itself and measure it as compared
21:26to the surrounding liver tissue,
21:28we see that the tumor in by itself
21:30has much lower pH and baseline in an
21:33untreated fashion and the surrounding liver,
21:35and that's important.
21:36And we know that this is probably
21:38due to the overt Warburg effect and
21:41overexpression of glued one and lamb two
21:43as indicators for the micro environment.
21:45Now, what does taste actually do with it?
21:48And this is where the surprising
21:50result from our study came in.
21:51We looked at the effects of
21:53embolization with little one day,
21:55one week,
21:56and two weeks after we actually
21:58treat those tumors and measured the
21:59pH and as opposed to most of the
22:02assumptions that taste is going to
22:04actually exacerbate the The Anti.
22:06Yeah, I mean the logic, the immune,
22:09evasive tumor micro environment,
22:11it actually did the opposite.
22:13So what we see is a normalization
22:15of the tumor pH towards almost
22:18delivered background levels.
22:19Overtime after taste.
22:20And that is an important finding
22:22because that gives us an opportunity
22:25to use local regional therapy in
22:27preparation for successful anti
22:29checkpoint checkpoint inhibition
22:30therapy and we actually demonstrated
22:32this also with a more direct and
22:35exacerbated model. Where we.
22:37Eustace and actually injected bicarbonate
22:39directly with taste and what we
22:41demonstrated here is that on baseline
22:43you have an acidic tumor with taste.
22:45You have a minor improvement of pH towards
22:48normal over overnight essentially,
22:49but if you add bicarb you can
22:52immediately improve the pH almost
22:53essentially to normal and yellow and
22:55green means higher levels than blue.
22:58If we now look at, for example,
23:00H Lady are expression at baseline,
23:02we see immune cell exclusion were really see
23:05all the immune cells in the tumor periphery.
23:08And with with taste alone,
23:09immediately at least one day after therapy,
23:12you don't really see a
23:13lot of immune invasion,
23:14but with taste and bicarb,
23:16essentially improving and elevating a pH,
23:18you see massive intratumoral
23:19infiltration of immune cells,
23:21and that's an important finding that
23:22we made here in actually an important
23:25other thought that we should be thinking
23:27about as a group generally in oncology,
23:29and what kind of agents we use.
23:32So when we use a key mobilization with oil,
23:34which is Lupito, we achieved,
23:36achieves seemed to be achieving
23:38very different effects.
23:39Compared to,
23:39for example,
23:40using an cuisine beads or Lumi beads,
23:42which is another flavor of beads
23:44and that is important for us to
23:46understand because through the
23:48uncle logic community taste was
23:49taste for long period of time,
23:51but in reality it is not because we
23:53know that different embolic particles
23:54will induce very different in a logical
23:57effects in very different changes
23:59to the tumor marker environment.
24:00And that is something that we
24:02really need to study,
24:04especially if we want to combine the
24:06local regional therapies with systemic
24:07therapies and checkpoint inhibition.
24:09Now another level of investigation
24:11that we took to token published
24:14recently in radiology,
24:15again close collaboration with
24:17Daniel Komen Fahmideh Hyder but
24:20also other groups like rip Ocala's
24:22group and also want to mention Ruth,
24:25Montgomery,
24:25and Joshi,
24:26who helped us generate this research.
24:29Here we wanted to demonstrate
24:31that we can actually use molecular
24:33dedicated inmar probes nanoprobes
24:35labeled antibodies to visualize
24:37the immune system surrounding those
24:39tumors in vivo and noninvasively.
24:42So what we did is we use both iron
24:44oxide particles and the direct
24:46injection of gadolinium labeled
24:48antibodies to visualize those tumors
24:50and put those animals with those
24:53implanted tumors into them are what
24:55we demonstrated was that we were
24:57able to clearly delineate microfusion
24:59immune cell infiltration and macrophage
25:01and immune cell accumulation in
25:03the periphery of those tumors.
25:05On T2 MRI sequences,
25:07and that was proven in histologically
25:09with Prussian blue staining,
25:11which stands for iron.
25:12And we saw that the spines,
25:15the iron oxide particles really
25:17deposited and CD11B positive macrophages
25:19and we confirmed that later with
25:21CD 68 staining and the same thing
25:24is true for our induction injection
25:26of gadolinium labeled antibodies.
25:27You probably heard about this from
25:29the molecular imaging using pet,
25:31but here we have a much Marsh
25:33higher focal resolution where we
25:35label antibodies with gadolinium
25:37and then inject them
25:38directly into those tumors and through
25:41the artery, and we can see that there is
25:44very specific accumulation and staining.
25:46Of immune cells and perforate the tumor
25:48that we can now really locali characterize,
25:51and that's that's a big step forward
25:53because not only can we now image the tumor
25:56marker environment from a pH perspective,
25:58we can also images from a presence
26:01and functionality of immune cells.
26:03So for the first part of the conclusions
26:05are their appearance of, for example,
26:07more than five targeted agents to
26:09treat HTC will cause a left shift
26:12of systemic therapy in the BC else,
26:14and we really must do some heavy lifting.
26:16It's up to art actually to put
26:18to put the collaboration between
26:19the Interventional in college and
26:21Immuno Oncology on the right track.
26:23From a science perspective we must
26:25also work more on non invasive
26:27imaging modalities and we did so with
26:29the extracellular pH that revealed
26:31that ace actually is an inducer.
26:33Profound changes to tumor marker
26:35environment that may help immunotherapy
26:37to be more effective,
26:38and pH is key for us as marker,
26:42but the changes in pH depends on
26:44the embolic materials that we use.
26:47We also now know that C test would
26:49lipiodol may achieve a partial reversal
26:52of the immune evasive tumor marker
26:54environment on itself two weeks after taste,
26:57and that we can use them are
27:00instruments to actually detect immune
27:02cells directly surrounding the tumor.
27:04So in summary,
27:05the for the first part of my conclusion
27:07is that local regional therapy
27:09and intervention alone college.
27:10You must be established in combination
27:12with each other.
27:13We have to have a synergy between
27:15the two iOS interventional and Immuno
27:17Oncology and it's up to us to work on that.
27:20And that generates another bigger
27:21problem that we really workup on very
27:23closely with Jim Duncan and collaboration
27:25with that group and biomedical engineering.
27:27And this is the overwhelming growth
27:29of data and will be talking about
27:31this little bit later.
27:32Tall is going to be presenting on that,
27:35so we know that the data that we
27:37have specifically in imaging and.
27:39Cancer in general explode so we
27:41have more than 20 times more data
27:43today than we have in 2013,
27:45and so that is a huge challenge.
27:47Anne, it's David already presented.
27:49We have different flavors
27:51of chemo embolization,
27:52embolization with drug alluding
27:53Beatson Radio mobilization.
27:54We have a variety of different
27:56methodologies in ways how we
27:58can introduce tumor necrosis,
28:00cold heat,
28:00and electrocution in a way we have
28:03five different and more coming
28:05up with systemic therapy agents,
28:07all of which pose different changes
28:09to the tumor micro environment.
28:11So we went overwhelming burden of
28:13complex data that actually may
28:14impede effective clinical practice.
28:16We need to address that.
28:18And so,
28:19how do we transform the data
28:21from burden to value?
28:22We know that we have an increase in data.
28:25We have very complex medical health records.
28:27We have genomics sequencing information.
28:29We have the availability of complex
28:31computer based algorithms and
28:32now the availability, availability of
28:34computational power at a lower cost.
28:36And that is the space where
28:38artificial intelligence now comes in.
28:39And this is essentially data driven.
28:41Learning that deep down can be
28:43explained as machine learning that
28:45recognizes trends and objects in pre
28:46labeled patterns or deep learning,
28:48and tall is going to be talking
28:50more about that where?
28:52We use those networks to actually
28:54learn from data without pre labeling
28:56the outcome and that may hopefully
28:58increase the workflow efficiency,
29:00improve our diagnostic accuracy really
29:02enabled predictive recommendations
29:03for us Taylor Personalized
29:04Therapeutic recommendations and help
29:06us introduce Precision Medicine,
29:07but caution is still very important and
29:10AI is being hyped and I think we need
29:13to approach it in a gradual fashion.
29:16Really see what is doable and what's not.
29:19So how can this advanced data
29:22analysis help us?
29:23It can improve the diagnosis from automation
29:25and introduction of novel biomarkers.
29:27It can also help us make therapeutic
29:30decisions and probably introduce a
29:32level of better personalized care.
29:34Hopefully we will be able to ultimately
29:36improve Inter procedural guidance,
29:38especially with now with the
29:40introduction of robotics into IR,
29:41and specifically follow up imaging.
29:43We hope that in the realm of tumor
29:46response and patient patient outcome
29:48prediction directly after the therapies,
29:50those technologies will help
29:52us put all this data together.
29:54Now,
29:55prior to introducing the other
29:56speakers and talking and giving
29:59specific abstract presentations.
30:00The two larger topics that I presented.
30:03I want to really thank the sources of
30:05funding inspiration mentoring in our
30:07community. Already mentioned Drake.
30:08I wanted to thank Jim sincerely and
30:11Todd and and David who joined the lab.
30:13Also great partners of mine are
30:15Ruth and Nick and I really,
30:17really appreciate those collaborations
30:18that are increasingly interdisciplinary,
30:19but at the same time I wanted to
30:21also point out that mean radiology
30:23have a very large biomedical
30:24imaging collaborative network,
30:26and I want to thank from Eden, Daniel,
30:29and and Larry and everyone mentioned here.
30:31I'll say I'll.
30:32From White Rick,
30:33who have been instrumental over the last
30:34five years in my personal development
30:36and also in the development of the lab,
30:38and I think that is the environment
30:39that we need and we need to highlight
30:42that to the Cancer Center and
30:43introduce it to to all of you.
30:45And I'm very happy to do so here.
30:47And,
30:47you know,
30:47at this point I would like to thank on behalf
30:50of the Interventional College lab which
30:51I'm privileged to Co direct with David and
30:53all of our sponsors and really say that,
30:55you know,
30:56there's one thing across all this.
30:57There is a single light of science,
30:59then to brighten it anyways,
31:00to brighten it everywhere.
31:01This is our group model by Isaac Asimov.
31:03And with that,
31:05I'd like to introduce two or
31:07students that work with us.
31:09First,
31:10Jessica Santana.
31:10She's going to be talking about
31:13taking the molecular imaging
31:15really to next level in our new
31:17animal models, and tall is going to be
31:20talking about his role as Biomedical
31:23Engineering graduate student within
31:25the bio medical imaging Sciences on
31:28focusing on predicting outcome and
31:30recurrence of HTC after intervention.
31:32And I'd like to.
31:34And now the stop sharing the
31:35screen and give it to Jessica.
31:46Hi all, I'm Jessica.
31:47I am a master level demonologist
31:50Anna graduate student at the
31:52Yale Interventional College Lab
31:54and I'm very excited today to
31:56be presenting my work, titled,
31:58Noninvasive molecular imaging
31:59allows characterization of the
32:01immune response following hepatic
32:03radiofrequency Ablation in a mouse model.
32:07Nothing to disclose,
32:08so giving you a brief overview
32:11on hepatocellular carcinoma.
32:12So as we know, this is a classical
32:15inflammation associated Carcinoma and
32:16now is the third most common cause
32:18of cancer related death worldwide.
32:21With the majority of patients being
32:23treated with locoregional therapy as
32:25they may alternative option over surgery.
32:27However, the problem with locoregional
32:29therapy is that a significant fraction
32:32of patients they tend to recur and the
32:35causes for recurrence can vary a lot.
32:37And it has been suggested, for example,
32:40that the immune response to radiofrequency
32:43ablation can play both both roles.
32:45An Protogenic Side effects as well as
32:48abscopal effects that in turn can positively
32:51impact the immune response to cancer.
32:54However,
32:54we have currently no instruments that
32:57would allows us to non invasively
33:00monitor such immune response.
33:02So the purpose of this work is to
33:04develop a noninvasive molecular imaging
33:07instruments to visualize such immune
33:09response to thermal injury following RFA.
33:14So our group has built a translation
33:17of mouse model of radiofrequency
33:19ablation as a platform to develop and
33:22validate mirbase dimona probes for in
33:24vivo imaging of the immune system.
33:27And based on our findings,
33:29we have observed that there is a after
33:33radiofrequency ablation and normal liver.
33:35There is a strong time dependent local
33:38infiltration of immune cells that
33:41orchestrates the tissue healing process
33:43and there is an overtime transitional
33:46zone of those immune cell infiltrate.
33:49So having a specific cell population
33:51locally present at the transitional
33:53zone between the necrotic area and
33:56the normal liver parenchyma at a
33:58specific time point serves us as
34:00an illegal platform to build and
34:03validate our dedicated immune probes.
34:06So guiding you through our
34:08experimental design.
34:09We have a bladed and normal liver of a mouse,
34:13and after characterizing a large
34:15infiltration of a specific cell
34:17population is specific time point.
34:19We have established a dedicated
34:21gadolinium labeled antibody that was
34:24delivered systemically to target
34:26a specific cell population at the
34:28chosen time Point Post Ablation.
34:31And a second imuna probe also used
34:33in this study,
34:34is the small iron oxide particles,
34:37and we know that RN particles they
34:39have been largely used in the clinical
34:41setting as the dark contrast agent and
34:44once they are delivered systemically,
34:46they are able to be phagocytosed by
34:49circulating phagocytes that once they
34:51migrate to the site of inflammation
34:54they can cause a local deposition
34:56of Iran and this study we have
34:58demonstrated that both gadolinium
35:00labeled antibodies inspire probes.
35:01They were able to be imaged using a
35:05higher resolution Mr animal scanner.
35:08So in our first setting of experiments
35:11we have demonstrated that the
35:13ablation zone itself can be easily
35:15imaged on a 9.4 Tesla Burger.
35:17So here on your left you have
35:20the pictures taken seconds after
35:22ablation to show you or give you an
35:25idea of how the ablation site looks
35:27like an one week post Ablation,
35:30and we have delivered pure gadolinium
35:32systemically and we ran a T1
35:34weighted MRI sequence and we could
35:36see precisely the Ablation.
35:38Side and on your right we have an ex
35:40vivo confirmation of what we see on them,
35:42right?
35:44But you might have been wondering
35:46why exactly 7 days post ablation.
35:48So to the best of our knowledge,
35:51we know that our our thing doost
35:53thermal tissue injury,
35:54largely contributes to a strong time
35:57dependent innate immune response at
35:59the margins of the necrotic zone.
36:01And according to our findings,
36:03we have observed the largest
36:05accumulation of city 68 positive
36:07macrophages in the transitional zone,
36:09precisely seven days post ablation.
36:11So this is the time point we decided
36:15to base our experiments on.
36:18So we have decided to deliver systemically.
36:21Those aren't oxide particles.
36:23Seven days post Ablation and we
36:26have confirmed that there is a local
36:29deposition of those iron oxide
36:32particles exactly 24 hours after
36:34systemic delivery. At the transitional zone.
36:37So here we have an ex Vivo Prussian
36:39blue staining,
36:40although one week post ablation,
36:42mouse liver 24 hours after
36:44systemic delivery of Spions and
36:46when we image those animals,
36:48we could demonstrate any Viva local
36:50deposition of those phagocytes
36:51at the transitional zone.
36:53So here on your left you have a teacher
36:56weighted MRI of a one week post Ablation,
36:5924 hours after systemic delivery
37:01of Spions and as a Redpath
37:03correlation we confirm the ex vivo
37:05specifically position of the spine.
37:07At the transitional zone,
37:093 min inflorescence and
37:10Prussian blue staining.
37:14And As for our gadolinium based
37:17immuno probes, we have used a anti CD
37:1968 antibody tagged with gadolinium.
37:22So after XP will observing a massive
37:24infiltration of city 60 positive
37:26macrophages in the prohibition of
37:28zone we delivered gadolinium tagged
37:30with city 68 systemically and was
37:33able to see a specific position of
37:36those cells in the Prohibition of rim.
37:38So here on your left you have the Ablation.
37:43Cite the picture taking seconds after
37:46ablation showing the ablation site.
37:48And we also have 81 way to MRI when
37:51we post Ablation After 24 hours with
37:55gadolinium city 68 delivered systemically.
37:59And on your left you have the picture taken,
38:02ex Vivo confirming of what we're
38:05seeing on the MRI.
38:07And to confirm that this parable
38:10itional darkroom is seen precisely
38:12in animals receiving those city,
38:1568 tagged to 68 antibodies tagged
38:18with gadolinium.
38:19We also ran T1 weighted MRI with
38:23pure gadolinium.
38:24So on your lap to have the baseline
38:27and it comparison,
38:29we have a T1 weighted MRI with pure
38:33gallium injected and we have confirmed
38:35that only with animals receiving city
38:3868 tagged with get alignment we have
38:42this precise parable itional rim
38:44showing a local infiltration of CD
38:4716 positive macrophages and therefore
38:49in vivo visualization of those cells.
38:53We have also confirmed the ex vivo
38:55a specific labeling of immune cells
38:58using imaging mass atama tree.
39:00So he ran on your left.
39:02We have the T1 weighted MRI of one
39:04week post ablation after gadolinium
39:06labeled antibody administration,
39:08where you can see the rim of the
39:11local deposition of the infiltrating
39:13cells an on your right.
39:15We have the X visual confirmation with
39:17the image Ng Masama tree of local
39:20deposition of the sea to 68 macrophages.
39:23In the transitional zone.
39:26So as the main conclusions
39:28and findings of this study,
39:29it tells us that both spines and
39:31Catalina based molecular imaging
39:33allows for specific labeling
39:34of local immune infiltrate,
39:36and this is also a translation of
39:38study with the proof of principle
39:40for the visibility of the MRI imaging
39:42for of macrophages on a 9 point.
39:45For Tesla MRI scanners.
39:46And also tells us that noninvasive
39:49in vivo detection of the immune
39:51system can be achieved using
39:53dedicated immune probes.
39:55An ask for our future perspective
39:59and clinical application.
40:00We can have this as a useful tool to
40:03study and characterize the interplay
40:06between the tumor micro environment
40:08and the cell opinion selectivity in
40:11vivo and also gives us the possibility
40:13to integrate complimentary molecular
40:15MRI imaging of the immune system.
40:17And let's say the extrasolar pH
40:19liver cancer model for simultaneous
40:22characterization of this immuno
40:24metabolic cross dock.
40:25This also serves us as a platform to
40:28study strategies for local modulation
40:30of the immune microenvironment
40:32towards a moon are permissive
40:34phenotype and can also serves us as
40:37if they were gnostic immunotherapy.
40:40So I'd like to thank you for the
40:42opportunity to present this work
40:43as well as the yield bio medical
40:46imaging Department. And now I'll give
40:48you the word to my friend, tall.
40:58Hello everyone, my name is Charles
41:01early and I'm graduate student in
41:03the Biomedical Engineering program
41:05here at Yale and I'm a member of the
41:09Interventional Oncology lab since 2018
41:11and today I'm going to be presenting
41:13our project on deep learning use to
41:16predict disease recurrence of HTC,
41:18based on NMR imaging.
41:23So a little bit about ATC,
41:25so HTC is the primary tumor of delivered.
41:28It usually develops in the
41:30setting of chronic liver disease,
41:31and while the diagnosis of ATC
41:33could be made by imaging alone,
41:36sometimes a biopsy may be required
41:38to support this diagnosis.
41:40A little bit statistics every 40 seconds at
41:44patient diagnosed with ATC in 2020 alone,
41:48the death rate is approximately
41:51the 800,000 deaths worldwide.
41:53ATC may recur and there is no
41:56significant imaging biomarker or
41:59clinical biomarkers to reliably predict
42:02recurrence before location to treatment.
42:06One of the treatment is
42:08liver transplantation.
42:08It helps to decrease the
42:10chance of disease recurrence.
42:12However,
42:12we are all aware to the shortness of organs.
42:16Therefore, 2 criterias of
42:17allocations of levers are being used.
42:19One of them is the Milan criteria,
42:22which was presented in 1996 and
42:24the other one was presented by
42:26the University of California,
42:28San Francisco on 2001 basically
42:30extended this Milan criteria.
42:32Both of them are based on low
42:34level handcrafted features such as
42:36tumor size and number of tumors.
42:39Ann even dog.
42:40Using these criteria,
42:42we are seeing 15 to 20% of transplanted
42:45patient to occur within the 1st five years.
42:48So as I said,
42:50these two criterias location criterias
42:52has suffered from false positives,
42:54and our hypothesis is that there is more
42:57information in radiological images,
42:59specifically MRI that correlate
43:01with HTC recurrence.
43:02Then the naked eye could detect
43:04and the way we're going to try to
43:08test this hypothesis is by using
43:10deep learning algorithm to extract
43:12features from MRI images and try to
43:16use them to correlate to ATC re occurrence.
43:20And a little bit before I start
43:22talking about our methodology,
43:24I will talk about about data
43:26driven predictive modeling and
43:27a little bit of deep learning.
43:29So when we talk about data
43:31driven predictive model,
43:32we usually refer to two different variables,
43:34the Explanatory variable and explain
43:36part of our target and we want to
43:39take these explanatory variables
43:40to feed them into predicted model
43:42which will give up the outcome of
43:44our target building which could
43:46be recurrent or not.
43:47And this predictive models
43:49has different shapes.
43:50And algorithms which you may hurt.
43:52For example your networks this season.
43:55Trees as VM's.
43:56All in all,
43:57these algorithms that try to do the
43:59same thing they try to create or to
44:02estimate a mathematical function that
44:05correlated this input into doubt.
44:07And there are two main elements
44:09of this estimation process.
44:11One of them were trying to see
44:12what is the mathematical element
44:14interaction between our features
44:16and the mathematical operations.
44:18That's going to be incorporated
44:20within these mathematical function.
44:21For example,
44:22the multiplication of X1 and X2
44:24and the second thing we're trying
44:26to estimate the weight of these
44:28mathematical elements within this function,
44:31which could be seen here as better 0,
44:33better one, etc.
44:36And I'll give a short and very
44:38simplistic example of these
44:39data driven predicted model.
44:41So here we are,
44:42having having only one variable Explanatory
44:44Variable, which is the age and we are
44:47trying to predict the disease patient
44:49is positive or negative to the disease.
44:52So for example in the right
44:53side of the screen you can see
44:56this mathematical function.
44:57Why would be considered positive if the age
45:00is greater than better otherwise negative?
45:02And as we get more and
45:04more examples we could.
45:06Estimate this better.
45:07For example,
45:08here we are estimating better to be 60,
45:12but as more data comes we
45:15can update modifier.
45:17Wait and basically change our function.
45:21Ann everything is OK till we have
45:23a data example that prevent us from
45:25creating one to use this mathematical
45:27function to a separate between
45:29between these these two groups.
45:31So here we need to incorporate a new feature,
45:34for example wait when we are doing that.
45:36We can create a more complex function to
45:39separate these two groups and basically
45:41this is what we're going to try to do.
45:43Today we're going to try to find the
45:46features that will allow us to separate
45:49between the recurrence in Nonrecurring.
45:51When we're talking about deep
45:53learning where basically usually
45:55refer to neural networks,
45:56and you run networks is again an
45:59algorithm that allows us to approximate
46:02almost any mathematical function
46:03that correlate input into output,
46:06and it does that by finding
46:09interaction between features.
46:11Convolutional neural network allow
46:13us to search for petitive patterns
46:16within an image and then correlate
46:18them to the output variable so it
46:20tries to find high level features such
46:22as edges and the more deeper we go,
46:25the more complex the feature become
46:28and allow us to separate between the
46:31groups that were trying to suffer.
46:34A little bit of our data,
46:36so we had 120 patients 18 years old or older,
46:4088 minutes and 32 females.
46:42All of them were diagnosed with
46:45HTC between there is 2005 to 2018.
46:48So the patient went into MRI imaging,
46:52then were diagnosed with HTC and
46:54then got treatment 29 oblations,
46:5732 receptions and 5:59 presentations.
47:01An time went by, some of them recur,
47:04and some of them stop their follow which
47:07we considered to be non recurrences.
47:09To this time that I can call time
47:11to recurrence and this would be
47:13our explained variable,
47:14the variable that we're going
47:16to try to predict.
47:18With respect to our input data,
47:20we're going to use conference enhanced multi
47:22phase liver magnetic resonance imaging,
47:24MRI,
47:25and we're going to use three
47:27different phases that the arterial,
47:29the portal venous and the delay.
47:33So the question that we're trying
47:35to answer here is can we predict ATC
47:38recurrence using pretreatment MRI images?
47:40In other words, are they visual
47:42features in free treatment MRI that
47:44correlate with HTC disease recurrence?
47:46So to visualize that we're going
47:48to use the input MRI input data
47:50as an input data to freedom into
47:52a predictive model which will be
47:55convolution on your network and to
47:57predict whether the patient will
47:59reoccur within one year two years
48:01after six years after treatment.
48:05So here are the results.
48:07Here we can see the results
48:09for these 6 * 6 timeframe.
48:11So one year for occurrence,
48:13two years of free France up to 6,
48:16zero for occurrence and this figure
48:18present the relationship between
48:20the true positive rate to the
48:22false positive rate on our test
48:24cohort and seeing these 45% curve,
48:26which represents basically
48:27a random chance to predict,
48:29we can see that all of our
48:32curves is above that.
48:33Which means that our model
48:35has prediction power.
48:39Another analysis that we did we
48:41did was to try to use Kaplan Meier
48:44curves and our predictions to
48:46separate our cohort into risk groups.
48:49Soloist group to recurrence in
48:51high risk to recurrence and we
48:53got significant results for your
48:55current free survival for four
48:57out of the six times you can here,
48:59you can see the six different figures
49:03each for one time for each time frame.
49:06So to summarize,
49:08the current state of the art criteria
49:10based on low level handcrafted
49:12radiological features such as tumor size,
49:15number of tumors and this study
49:17showed that there are still unknown
49:20visual features in pretreatment.
49:22MRI does correlate with recurrence of ATC.
49:24Secondly,
49:25the current state of the art selection
49:28criteria suffers from false positives
49:30and we showed by incorporating
49:32machine learning based algorithms
49:33we could potentially improve the
49:35prediction of AGC recurrence.
49:37Little bit of limitations
49:40and future research.
49:41We used a single site cohort
49:44from Yale Hospital,
49:46which was small 120 patient,
49:48which has the limitation of generalize
49:51the results for other sites and
49:54therefore will need to increase
49:56our sample size or incorporating
49:59patient from different side.
50:01And maybe single data modality,
50:03maybe not enough,
50:05so in future research we recommend
50:07to test interaction between
50:09imaging and clinical data,
50:11even incorporating more imaging modalities.
50:15Thank you very much.
50:23So, so that was really great.
50:25Hope you all were able to
50:28see what exciting work word.
50:30You know, doing in this lab?
50:32Does anybody have any questions?
50:35And individuals can certainly
50:37submit their questions on chat,
50:39but we did receive one which
50:41David you took the Liberty of
50:44at least answering online.
50:45But let me for the benefit of all.
50:49Let me just toss this out so
50:52Joseph Cam asked, you know,
50:54essentially, how do you decide?
50:56On the specific procedure be RFA,
50:59thermal abrasion,
51:00ablation, cryoablation,
51:01and and also relative to contraindications.
51:04How do you make those choices?
51:08Yeah, so so. I guess I gave a
51:11pretty long email for a chat,
51:14but are more common for chat, but.
51:17Each ablation modality has its pros and cons,
51:21and clearly my goal for today was
51:24not to go into my own personal
51:27research and discuss a lot of this
51:30stuff in depth because I really
51:32wanted to highlight our trainees.
51:35But basically you know,
51:37for liver it's pretty much understood that
51:40heat ablation is actually what is preferred,
51:43and that's because over the years well,
51:46first of all it's very effective.
51:49But over the years,
51:51there's been concerns with Cryo Ablation on
51:54what's called something called Cryo shock.
51:57There's questions about fracturing the liver.
51:59There's no way of really cauterizing
52:02any bleed that you can do with microwave
52:06or RFA and a whole bunch of others.
52:09So it's really the complications
52:12in this in this setting that makes
52:16it less ideal than four.
52:18For the Heat base,
52:20now the one positive thing is we
52:23shown in some of the in one of the
52:27cases is that you do get the ice
52:30ball which has low density ice and
52:33that actually allows the operator
52:36to really sculpt the margins where
52:38you want to treat in other organs.
52:41Systems like the lung and kidney
52:44is really a dealers choice so that
52:47there have been papers on both.
52:50In terms of using cryoablation or
52:52heat ablation for those that we were
52:55just involved in a study that was
52:57recently published in the Journal
53:00of thoracic oncology,
53:01which was a prospective clinical
53:04trial called solstice that.
53:06Did show benefits of cold,
53:08you know,
53:09for for lung nodules and then I
53:12already as I kind of explained,
53:15it's kind of a niche application.
53:18Mostly used in pancreas,
53:20but it's in an area where it's
53:23difficult to treat.
53:25Areas which have vital structures
53:27in your body,
53:29so that's the I guess summary
53:31of that so thanks.
53:35So let me ask a follow up question 'cause
53:38you great deal of the work is really
53:42advancing understanding the nature of
53:44the immune infiltrate post procedure.
53:47And Moreover, how to address how to
53:50make the environment more hospitable?
53:52Two immune cells, Sony aspid,
53:54be both with regard to the imaging
53:58techniques monitoring techniques and
54:00perhaps the introduction of bicarbonate.
54:03How much you leverage all of this to think
54:07about combining these approaches with
54:09ongoing immunotherapy immuno therapies
54:12that are under development in HTC?
54:16Right, I mean that's a really
54:18important question that we're
54:19actually trying to answer ourselves.
54:21As David mentioned,
54:22I mean, we're right now.
54:23Actually submitting one or an
54:25application after the other
54:26because we want to investigate
54:28exactly those points we generated.
54:30This preliminary data.
54:30Now we want to take it to the
54:33clinical perspective and do also
54:35trials and immediate future.
54:36So I think there is a couple of
54:39limitations that we first need to address,
54:41and for the pH Image Ng,
54:43this is certainly the contrast agent,
54:45which is not done yet.
54:47Applied and in the human scenario,
54:49so we need to make sure that we
54:51can do that safely, you know.
54:53Secondly,
54:54I think you know the imaging of the
54:56immune system is a very hot topic and
54:58is being hotly discussed and worked
55:00up on both by the nuclear medicine
55:03community as well as you know us,
55:05and I think that both approaches
55:06have their merits in their drawbacks,
55:08and I think that this will
55:10definitely bring a breakthrough
55:12in terms of imaging biomarkers.
55:13As we know in HC see only 20% of
55:16the patients at Prof. Roundabout.
55:18Respond to immunotherapy.
55:19But we treat a lot of them with it,
55:21and so we want to make sure we choose
55:24their right patients for the right therapy.
55:26And that's the whole goal.
55:28As for you,
55:29As for the As for the real
55:30application of bicarb,
55:32that is an approach that could be
55:34translated into clinical trial very easily.
55:36I mean,
55:36bicarbonate is not a harmful substance.
55:38It could be used in just used
55:40in a clinical trial with taste,
55:42and I think we need to really
55:44think about using using that as
55:46an addition and an additional.
55:48Arm,
55:48possibly in one of the clinical
55:50trials that David is bringing on
55:52board to use taste in combination
55:54with the immuno therapies.
55:55And I think that that would
55:57be a very simple initial fix.
55:59To do that.
56:00I mean there is very little Harmon.
56:02Probably you know some major
56:03benefit that we could get from that.
56:06So we need clinical trials in this
56:07respect and we need more translational
56:09contrast agent and more research.
56:11And I think we do have an
56:14amazing infrastructure with RMR
56:15Research Center here and the Pet
56:17Center on the other hand 2.
56:19To actually do that kind of research.
56:21And so let me as a follow up.
56:23So let me ask, given what you're
56:25describing in the nature of the immune.
56:27Response, immune environment,
56:29fear after procedures such as these.
56:34Desert potentially suggests that any
56:36efforts right now to combine a checkpoint
56:40inhibitor with tastes or other ablation
56:43procedure may not be yet optimized.
56:46Turn to really achieve what we
56:48hope they would achieve because of
56:50the nature of the environment absolutely,
56:52and this is exactly what I showed.
56:55If you remember one of the slides
56:57had different embolic agents,
56:59and so we're using various symbolic agents,
57:01various agents.
57:02If we inject into the tumor,
57:04that may be pro or anti inflammatory.
57:06We then also combine taste and
57:08checkpoint inhibition in tumors that
57:10may be hot or immunologically cold.
57:12So essentially you know a lot of
57:14these trials will probably have
57:16very non significant results
57:18and probably will never achieve.
57:20Yeah, it is certain level of clinical
57:22translation that we hope for.
57:24Just because we don't know what we're doing.
57:27Essentially so when we combine Interventional
57:28in college and Immuno Oncology,
57:30we just go about it and say one size
57:33fits all and that is something that I
57:35feel like is is the major key point
57:38that we need to address and one of
57:40these issues is that in Interventional
57:42Ecology Community would just did not
57:44have the academic culture and you
57:46know research environment so far and
57:48this is across multiple institutions
57:50across our entire community.
57:51To actually tackle those topics,
57:53and I think now we as we more
57:55understand that our therapies are
57:57necessary in our increasingly
57:58combined with systemic therapy,
58:00we need to investigate that,
58:01and I think that we're going to
58:04have major breakthroughs in the
58:05next three to five years. Thank you.
58:08Well, I know where at the top of the hour,
58:11and I want to thank Jessica and tile
58:13and Julius and David for really
58:15a remarkably stimulating body
58:17of work and Bradley bring to our
58:19attention what can be accomplished in
58:21Interventional Onkologie So you know,
58:23thank you for this continued education
58:25for the great work you're doing
58:27and everyone on line. Thank you.