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Introducing Using AI for Early Detection of Pancreatic Cancer from Tomorrow's Cure. Follow the show: Tomorrow's Cure Early detection is critical for improving cancer survival rates, yet pancreatic cancer remains challenging to detect. A recent breakthrough from Mayo Clinic researchers offers new hope. Artificial intelligence models demonstrate the potential to detect pancreatic cancer earlier and with remarkable accuracy. Learn more about this life-changing innovation in early cancer detection. Featured experts include Ajit Goenka, M.D., radiologist and professor of radiology at Mayo Clinic’s Comprehensive Cancer Center and Suresh Chari, M.D., professor, Department of Gastroenterology, Hepatology, and Nutrition in the Division of Internal Medicine at MD Anderson Cancer Center. Get the latest health information from Mayo Clinic's experts, subscribe to Mayo Clinic’s newsletter for free today: https://mayocl.in/3EcNPNc DISCLAIMER: Please note, this is an independent podcast episode n...
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- [00:03]
Kathy Werzer
No one wants to hear the words you have cancer, but if you do, you want to hear it was caught in its earliest stages. Some cancers, like pancreatic cancer, are often found when it's too late.
- [00:15]
Dr. Ajit Goenka
Early detection is really our best hope, but also our biggest challenge when it comes to pancreas cancer.
- [00:21]
Kathy Werzer
How are researchers working to detect pancreatic cancer earlier when there are treatment options available? In this episode of Tomorrow's Cure, we'll hear about how physician scientists have developed an AI algorithm that can help doctors detect pancreatic cancer up to a year before symptoms appear. Today, we're speaking with two leaders in the field of cancer imaging and AI. I'm so happy that Dr. Ajit Goenka is joining us. He's a radiologist and professor of radiology at Mayo Clinic. His work integrates AI and advanced molecular imaging techniques to improve early diagnosis in many types of gastrointestinal cancer cancers, including pancreatic cancer.
- [01:02]
Dr. Ajit Goenka
The idea is that we know that the cancer has started many years ago from the biological studies that have been done by many of our colleagues. The only thing we did not know is that why is it that we cannot see the difference? And we also know that the limitation was due to the limitations of the human eyesight. So how do we advance that, Aidan, what is it that can do better than the human eyesight? And the answer is AI.
- [01:27]
Kathy Werzer
Also joining us is Dr. Suresh Chari. He is a professor in the Department of gastroenterology, Hepatology and Nutrition in the Division of Internal Medicine at the University of Texas MD Anderson Cancer Center. He is studying the role that diabetes plays as a potential early symptom of pancreatic cancer.
- [01:47]
Dr. Suresh Chari
It's a biomarker of pancreatic cancer. So for us, you need to react to this right away. We can't afford to wait for a physician to make a diagnosis whenever he or she chooses to call the patient. Diabetes.
- [01:59]
Kathy Werzer
And that is ahead in this episode of Tomorrow's Cure, a podcast from Mayo Clinic that brings the future of medicine to the present. I'm Kathy Werzer. Thanks for being with us. I am so pleased that both of you are with us today. Thank you so much for taking the time.
- [02:17]
Dr. Suresh Chari
Thank you for having us.
- [02:18]
Dr. Ajit Goenka
Thank you.
- [02:19]
Kathy Werzer
You know, getting a diagnosis of pancreatic cancer is so very hard. The prognosis often is not good. How do you explain to patients why it's so hard to survive this cancer? I'm going to begin with Dr. Chari.
- [02:34]
Dr. Suresh Chari
It's a very peculiar phenomenon that the symptoms that you can directly Attribute to pancreatic cancer show up in the last three months before diagnosis. We have plotted the duration of symptoms in like 1000 patients with pancreatic cancer and try to see what is the duration of these self reported symptoms. And you will see that 85% are all talking about something happening in the last three months. And a few more, we'll talk about symptoms that started like four, five, six months back. And symptoms is equal to advanced disease. So if you're showing up with symptoms, that means you have higher stage of.
- [03:14]
Kathy Werzer
Disease, which is why we're talking about developments to try to find this disease earlier. Say, Dr. Goenka, I was doing some research on you and you had said up to 40% of small pancreas cancers may not show up on standard imaging. And I don't know why I was surprised by that. But why is that?
- [03:34]
Dr. Ajit Goenka
I think there are multiple reasons for that. I would say the most important reason is that these small cancers, they are very difficult to detect on imaging. So the challenge with these small cancers is that they look exactly similar to what the rest of the pancreas would look like. And unless you are deeply suspicious and you're looking very carefully, you are likely to overlook that. The second challenge is that as Dr. Chari mentioned, majority of the patients, especially in the earlier stages of the disease, they have no symptoms. When somebody is reading a CT scan, let's say of the abdomen, there are about 20 different organs that you are looking at. Since there is really not much of a reason for you to expect that the patient would also have something in the pancreas. Oftentimes these entities get overlooked when it.
- [04:25]
Kathy Werzer
Comes to then screening, screening methods and maybe recommendations. It doesn't sound like asymptomatic adults should be screened for pancreatic cancer because nothing would be found then. Correct.
- [04:38]
Dr. Suresh Chari
So you bring up an important point that the United States preventive services task force, which uspstf, they're the ultimate recommending body for who should be screened and what should be screened for, and so on and so forth. And they give a grade for the evidence for screening for different cancers and pancreatic cancer gets a failing grade. And they list three reasons for why pancreatic cancer should not be screened for. One is it's too low an incident. So we talked about pancreatic cancer's mortality, but it is not a common cancer. So it ends up being an uncommon cancer that's deadly. So it ends up becoming a common cause of death due to cancer, but not a common cancer in itself. Second is you look for it, you can't find it, which is what Dr. Goyer just pointed out, that there is uncertainty about whether it can be found early. And third is the so what Question. You find it early, but can you make a difference in people's lives? And for all these three reasons, the second two are uncertain. The first one is real. It's an uncommon cancer. But Dr. Gordka, ourselves are trying to break the second thing that, oh, you cannot find it. We are like, oh, no. We have additional tools, and we will find it. But whether it'll make a difference or not, that needs to be tested.
- [06:05]
Dr. Ajit Goenka
I think what we have discussed so far can be summarized into a single sentence, that early detection is really our best home, but also our biggest challenge when it comes to pancreas cancer. And so that is what motivates folks like me in our specialties, because we are really that, you know, the ultimate frontier in the battle against the early detection of pancreas cancer. So how do we find it early and at a time when cure is still on the table? And that is where I think some of the work that we have shown has demonstrated immense promise in terms of. Yes, that we can detect it at a stage that may allow our colleagues like Dr. Chari and our surgical oncologists to go in there and take it out at a stage where we can really make a difference to their outcomes.
- [06:55]
Kathy Werzer
Both of you are working on one of the toughest cancers. What's behind your interest in pancreatic cancer?
- [07:02]
Dr. Suresh Chari
I trained in India before I moved to the United States. And in India, pancreatic cancer is extremely uncommon. It's one of the lowest incidence of pancreatic cancer in the world. So I didn't have much of an experience dealing with pancreatic cancer. So while I'm doing my fellowship, my mentor gives me a project that involves testing a biomarker that had previously been published in the New England Journal of Medicine as a marker of diabetes in pancreatic cancer. The investigators have proposed that that was a reason why people develop diabetes in the setting of pancreatic cancer. That molecule and the end of it. We found out that while the marker had nothing to do with pancreatic cancer or diabetes, the connection between diabetes and pancreatic cancer was very striking. More than half the patients with pancreatic cancer had diabetes, and 80% of those were recent onset. So it got me thinking, what is this? Why am I seeing this unusual connection between diabetes and pancreatic cancer? The question is, if you have diabetes for the first time, are you at higher risk of pancreatic cancer.
- [08:15]
Kathy Werzer
Right.
- [08:16]
Dr. Suresh Chari
I went to the colleagues at Mayo at that time I was at Mayo and I asked the colleagues who were working on diabetes whether there was any group out there that was collecting a cohort of patients who had new diabetes. And sure enough, there was one. Starting from 1954 onwards, they were keeping track of every patient who had diabetes, knew diabetes in the population in Olmsted County. So I said, this is ready tailor made for me to look at what happens in these patients. And sure enough, almost 1% of patients over the age of 50 who had developed diabetes for the first time had pancreatic cancer in the next three years. And so this was back in 20 plus years ago.
- [09:00]
Kathy Werzer
So it was your curiosity that spurred you on, Dr. Goenk, I'm going to ask you the same question. What spurred you to get involved in the research in this specific field of study?
- [09:12]
Dr. Ajit Goenka
Well, my story is relatively shorter and it can be summarized in two words and that's daughter trial. Because everything that I know about pancreas cancer was something that was taught to me by him when he was here at Mayo and by the inspirational body of work that he had been doing for decades.
- [09:32]
Kathy Werzer
I like that. Say, I want to take a step back if I could, please. There are listeners who might want to know who's at risk for pancreatic cancer. You mentioned diabetes as a potential risk factor.
- [09:44]
Dr. Suresh Chari
Yeah. So let me clarify. The risk word can be confusing. So there are factors in your life that predispose you to future development of pancreatic cancer. And there are factors that are signals that indicate an underlying pancreatic cancer, that is you already have cancer. And this is a signal of that. If you take risk factors for pancreatic acid, long standing diabetes is one, smoking is another, obesity and family history of pancreatic cancer.
- [10:17]
Kathy Werzer
As you know, the incidence of pancreatic cancer is increasing, especially among people 55 and younger. Are there theories, gentlemen, as to why it's becoming more common? Dr. Goenka, what are you hearing?
- [10:29]
Dr. Ajit Goenka
Well, I think we certainly have a rise in the incidence and the prevalence of the risk factors that Dr. Chari very eloquently highlighted, which is smoking and other lifestyle related conditions. So I think that is likely contributing to it. And the challenge is that there is a lot about the disease that we still don't know.
- [10:53]
Dr. Suresh Chari
We talked about risk factors for pancreatic cancer. The strongest risk factor is age. The connection between aging and pancreatic cancer is the strongest among all the connections we have. And I think that there's a question.
- [11:07]
Kathy Werzer
Mark about why talk about the AI algorithm. Now, I'm curious, Dr. Goenka, when you were developing this and talking it through with Dr. Chari. Dr. Chari, what did you think when you heard about this, the use of AI as a potential tool?
- [11:23]
Dr. Suresh Chari
There's a history behind the study that Dr. Goenka did. So we had 150 patients who had a scan in the past three years, five years, and we collected all those things. Dr. Goenka and his colleagues participated in looking at it visually to try and see if we could see anything. And we picked up a few patients that had signals in the pancreas, but were not picked up at the time of the CT scan because they were being done for something else. That's when Dr. Goenka stepped in and he said, let me try and see if I can find what you guys. What we couldn't see with the eye. And then he and his colleagues developed AI algorithms to try and see if what could not be seen with the eye could be seen by AI. And that was remarkable in the sense that he saw every single cancer that was not seen with the eye, and he could tell you exactly where the cancer would appear. When the patients showed up for his clinical diagnosis, to me, that was like, we have solved the find problem. We can find it. I still believe that we have solved it. It has to be proven prospectively. But so excited to see his work.
- [12:41]
Kathy Werzer
Dr. Goenka, when you discovered this, when you started to see these patterns, what was the aha moment for you? When did you say, oh, my gosh, I think we're onto something?
- [12:52]
Dr. Ajit Goenka
There really wasn't one particular point that I could identify and say that, you know, this is where we thought that, yes, this is doable, because when you think about it, it's a very intuitive idea. The idea is that we know that the cancer has started many years ago from the biological studies that have been done by many of our colleagues. The only thing we did not know is that why is it that we cannot see that difference? And we also know that the limitation was due to the limitations of the human eyesight. So how do we advance that? The answer was that you take a technique that can amplify the difference between a diseased tissue and a normal tissue, and you do that at scale, and what is it that can do that at scale, and what is it that can do better than the human eyesight? And the answer is AI. So then the question was that how do you systematically take stepwise approach where you not only answer that question, but you do it in a way that it would be potentially clinically translatable. And what that means is that your work should not just throw out some insight that has no practical relevance to the everyday problem. And that is another thing that I've learned from Dr. Chari, is that you have to think it through. You have to think that, okay, if this is a solution that we are finding out, we need to be able to structure that solution so that it would make a difference into the lives of the people who matter to us, which is our patients.
- [14:21]
Kathy Werzer
I'm Kathy Werzer. This is Tomorrow's Cure. We'll be back in a moment. Know someone who would love this podcast? Please share it with them. I'm Kathy Werzer. You're listening to Tomorrow's Cure, a podcast from Mayo Clinic that brings the future of medicine to the present. Back now to our conversation where we're talking about how AI can help with pancreatic cancer.
- [14:51]
Dr. Suresh Chari
To use AI, the machine has to be able to identify the pancreas and say, this is the pancreas. And when you have 20 different organs, first you have to teach it to identify the pancreas. They did the work on thousands of patients where manually, you had to outline the pancreas and say, this is the pancreas. Can you get it? Do you get it? And then after teaching it multiple times, multiple times, multiple times on different scanners, different techniques, you figure out that it's got it, it's got it. It's able to identify the pancreas by itself. Then you just show it what's wrong with the pancreas. This is what normal looks like, and this is what pancreas cancer looks like. And normal is not always normal. By the time you get to 70, you got all kinds of things in your pancreas that don't look normal. So you got to show it all these things and say, this is still normal. This is not cancer.
- [15:42]
Kathy Werzer
And I can imagine, Dr. Goenka, that your database has got to also have been large and probably. So it is workable, diverse, right?
- [15:54]
Dr. Ajit Goenka
Yeah, absolutely. So I think this is where your domain expertise comes into the picture, is that when you train AI, you have to make sure that you are thinking about the ultimate end goal here. If you take a very homogeneous data set and you expect your AI to be able to go out there and make predictions into everyday CT scans of the people who come from all walks of life, then you're really not having the right kind of expectations. What we did at the get go. Is that we made sure that we provided to the AI training module the most diverse data set that we could, which means diversity in terms of age, in terms of ethnicity, in terms of the machines, where the data was coming from, in terms of the data itself. How thick was the particular CT scan, how small was it, how noisy was it, was it done at the right radiation dose or not, and so on.
- [16:49]
Kathy Werzer
Can I ask a question about the diversity of the data set? And I asked that because black men specifically, I understand, have the highest incident rate of pancreatic cancer. And I'm wondering, is that adequately reflected in your database?
- [17:04]
Dr. Ajit Goenka
Yeah. So the challenge is that AI in a way, is a mirror to our society. And if you look at one of the challenges of AI is that it tends to perpetuate the disparities in healthcare that exist right now. And the reality, the unfortunate reality today is that when you look at the access to healthcare, it is not really reflective of the diversity of our patient population in the US we do know that people from certain racial groups, certain ethnicity, and certain socioeconomic factors, they tend to not have access to healthcare as much as some others. And unfortunately, that's a barrier that we cannot overcome unless we devise larger scale studies where we take data from those institutions that are actually located in those geographical areas where there is more representation of these populations, including the black men.
- [17:58]
Dr. Suresh Chari
We are doing another study in another part of the country where the whole racial ethnic mix is turned upside down. We have 65% minorities in there. And so we will end up coming up with ways to image people, minority populations, by going to institutions that have a high proportion of minorities and be able to answer this question, whether the findings in this study are applicable to those in the minority communities.
- [18:32]
Dr. Ajit Goenka
Since the time that we have published our results, there are fortunately many international institutions that have come to us saying that we are keen to participate in taking this work forward by contributing our data sets. So we have institutions from Brazil, we have institutions from India, we have institutions from Germany that have come forward and said, we have data sets that we would like to contribute. If you want to validate your models and if you want to train them and refine them further.
- [18:59]
Kathy Werzer
You know what jumped out to me as I was doing research on this was that the AI algorithm can visually detect these cancers that are not visible to the eye at a median of almost 438 days before a clinical diagnosis. And I got to thinking about this. Is that relatively fast? Would that give maybe a Runway for physicians to maybe adequately treat their patients? Were you excited when you. When you discovered this, Dr. Goenka?
- [19:31]
Dr. Ajit Goenka
Absolutely. Because, you know, one of the challenges with cancer is that because of the different chemicals that are secreted by the cancer, it tends to go into the tissues of the body, like the muscles, the fat. And there is a lot of wastage, what we call the muscle wasting, the soft tissue wasting. And a lot of these people tend to lose weight, which in a way contributes to their suboptimal outcomes, which means that they cannot really tolerate the therapy. So to be able to pick up at that stage now, of course, even earlier is better. But if we are able to pick up even at that stage, I think the word cure would still be on the table. And the reason for that is because the expectation is that at that stage, the cancer has not spread, the cancer has not resulted in the patient becoming extremely cactic, whereby they would not be able to withstand any other kind of treatment. So, absolutely, it is the kind of hope that we are strongly interested in.
- [20:25]
Kathy Werzer
Dr. Chari, what do you think?
- [20:27]
Dr. Suresh Chari
Yes, we have given this a lot of thought because while we are doing this kind of research, we have to understand two concepts. One is we have to learn to walk before we run. And walking back in time is something we have not done ever in pancreatic cancer. So first step is, don't start asking for diagnosis when it is not even invasive. Cancer is not precancerous. It's not possible. So then we ask the question, okay, we are trying to find cancer. So this time, you mentioned 468 days, called lead time. Lead time is the interval between the time you start to think of screening and when the clinical diagnosis would appear. So that lead time, what is the lead time that is clinically meaningful, that is, you could make a difference to the patient. And in this setting, where we are trying to diagnose pancreatic cancer earlier, the challenge we have been facing is that half the patients have stage four at the time of diagnosis. And what we want to do is reduce that to 25%. Initially, we want to reduce the number of stage four patients that our early detection strategy would pick up. That would automatically shift everybody else to an earlier stage. Right now, only 7% of pancreatic cancers are confined to the pancreas at the time of diagnosis. We want to triple that number. We came up with a number of four months. If you diagnose all pancreatic cancers four months before they were clinically diagnosed, we would significantly shift the stage to a earlier stage. Not everybody will be stage one. Definitely not. But more would be. And as we learn to walk back, we can walk back even further. And when we looked at diabetes, for example, we found that 70% of people had at least a four months lead time.
- [22:33]
Kathy Werzer
That's significant.
- [22:34]
Dr. Suresh Chari
Yeah, but it has to be acted upon immediately. And that's where the AI assisted CT will come in handy. Because if you do a CT at six months and it's negative, and you wait another six months to do another CT too late, it'll be diagnosis time by then.
- [22:53]
Kathy Werzer
I'm curious. And Dr. Goenka, what's your next move with this tool? Do you need more screening trials?
- [23:00]
Dr. Ajit Goenka
If you look at how long it takes for any kind of medical discovery to translate to clinical practice, on an average, it takes about 14 years for a medical discovery to translate to clinical practice. One thing is that we don't want to rush to take something to clinical practice based just on enthusiasm. Because not only is the true positive important, we also want to make sure that we minimize the false positives, which means that we don't give false alarms to people who don't go on to develop cancer. Not only does it have a psychological penalty associated with that, it also has a big penalty to the healthcare system and the cost. So what we're doing now is we are doing a five year prospective trial, which is funded gratefully by a benefactor at Mayo.
- [23:45]
Kathy Werzer
Can you talk more about this trial? What will it involve?
- [23:49]
Dr. Ajit Goenka
What we will be doing as part of that trial is that we will be taking those people who have new onset diabetes and weight loss, which are some of the signals that Dr. Chari has identified and validated. And we would be inviting those people who have those kind of factors to undergo a CT scan at the baseline, along with blood draw. We would be storing that blood draw and the CT scan that we will be doing on these people will be then augmented with AI. And of course, we are not allowed to release those results into the chart right now until we complete the full scope of validation, which is, I think, very fair and reasonable. Once we do that, in the first three years or so, we would come to know how much of a difference that AI augmented CT scan does in these people who go on to develop cancer. And what is our false positive? What are the challenges? What are the barriers and the breaking points of the models? The second thing is that we've been talking about AI, and certainly AI is great, but AI cannot and does not work in a vacuum. We need to be able to have important and well established biomarkers, some of which Dr. Chari is already working on.
- [24:54]
Kathy Werzer
Now, Dr. Shari, you are running a clinical trial, early detection initiative for pancreatic cancer. Edi, I think that's going to be finished in 2028. With what you're doing, can you then help with what Dr. Goenka is doing?
- [25:10]
Dr. Suresh Chari
Absolutely. That was the whole goal of this. So the NIH has set up a consortium to identify new ones in diabetes patients. At the first time, their blood sugar rose above a certain level, not when the physician made the diagnosis. It's a biomarker of pancreatic cancer. So for us, you need to react to this right away. We can't afford to wait for a physician to make a diagnosis whenever he or she chooses to call the patient diabetic. So we ended up going back and developing an algorithm which is not AI, but it's all human intelligence. But it basically scours the emr, electronic medical records and lab records and identifies a patient who first crosses the diabetes threshold and make sure he didn't have he or she didn't have diabetes before, and flags that patient as he wants a diabetes based on blood glucose or A1C parameters. So we have collected a group of 20,000 subjects who meet these criteria of new onset diabetes. So the EDI is an effort in that direction of trying to put another brick in this wall we are trying to create where we show that you can find patients by using electronic surveillance of the records and you can identify their impact score. And if they have a high impact score, their risk is doubled, tripled. So we are slowly generating prospective data to convince people that this line of work will lead to practical improvement on the ground in terms of at least stage shifting.
- [26:54]
Kathy Werzer
So, Dr. Gurenka, then with your AI tool, if this all works right, I mean, some of that data can be used then to sharpen your tool in a sense?
- [27:04]
Dr. Ajit Goenka
Absolutely. The trial that we are launching in the next three to four months, which is what we've been working on, is called the AI based. AI stands for AI and paced, which stands for pancreas cancer early detection. What we are doing as part of that is we are taking the work that Dr. Chari has done, which is of people with new onset diabetes and weight loss. When he worked on it was through human review of the medical records. So what we are trying to do is that we have partnered with a company that has expertise in automated review of the medical chart, because you need this to be done at a scale and you can't do things at scale when you have human beings involved. So we are working with them. To develop technologies that can do a technological overview of the medical chart to quickly identify those people who may have new onset diabetes.
- [27:56]
Kathy Werzer
I am wondering if there should be monitoring for pancreatic cancer at the first sign of elevated blood sugar levels.
- [28:06]
Dr. Suresh Chari
That's what the UK NIH has actually given guidance. They call it clinical guidance, and it is there in their recommendations. So I think that it'll make a difference. So we have recently written a paper, and I said that any discussion of early detection of pancreatic cancer evokes both hope and optimism, as well as deep skepticism among primary physicians that this approach will work. And our only plea to them is, give us a chance. We cannot do this without you.
- [28:42]
Kathy Werzer
Where do you think that given this technology will be at, say, even in five years from now when it comes to trying to diagnose pancreatic cancer much earlier in the process?
- [28:57]
Dr. Ajit Goenka
I think five years is a very reasonable time for us to see real difference into where we are right now. And AI is certainly one aspect of that technological revolution. We also have molecular imaging, which is another form of technology where we are developing more specific radio tracers, which are kind of this small dive can inject into the patient and they can go and tag the cancer earlier. But again, we are building upon the work that has been pioneered by the likes of Dr. Chari over the last 25 years, which in a way makes it easy for these next five years to really translate this hope and optimism into real difference into our clinical practice. So I am very optimistic that in the next five years we will see the difference into our clinical practice.
- [29:38]
Kathy Werzer
What do you think about that five year mark, Dr. Chari?
- [29:41]
Dr. Suresh Chari
So I think that there is a strategy that Dr. Koenka is using that makes that timeline much more likely than the strategy we are using with edi, for example, whether it's difficult in recruitment. And so doing a scientific study with patient consent and doing this in a manner takes much longer. But what Dr. Goenka and Mayo Clinic are offering in the background is to be able to apply this to every patient who comes to the door and offering this as an additional tool, once the data is there, to show that it makes a difference. So there's a simple thing here. We talked about missed spots in the pancreas that were not seen. That is very easy. AI gives radiologists a heads up saying, spend 15 seconds more on the pancreas, will you please? That missed lesion is not missed anymore because the AI has given the radiologist heads up that there's something in the pancreas. Please look. So that one is very easy to implement in practice. You're doing nothing. You're just identifying what's already there but was going to be missed because it was not part of the original question being asked of the radiologist, but looking deeper and finding stuff that is not visible to the eye. As long as the data shows that that's real and should be acted upon, which is he's working on, then you apply it in real world. Patient comes to the ER with abdominal pain or something and this AI is being applied to that patient anyway. You start to now use this in clinical practice. That I think is a much faster and smoother entry into clinical practice than the approach we are taking, which will be much more organized and much more methodical, but will take longer just because recruitment is not that easy to do in a prospective study. But we are both very optimistic that this will be there sooner rather than later.
- [31:45]
Kathy Werzer
You both sound very excited about this. I can see it and hear it.
- [31:50]
Dr. Suresh Chari
This is what we do for a living. We love breathe and sleep and dream this thing.
- [31:57]
Kathy Werzer
Dr. Chari, Dr. Goenka, it's really been a pleasure talking to you both. I wish you all the best. Thank you so much.
- [32:05]
Dr. Ajit Goenka
Thank you.
- [32:05]
Dr. Suresh Chari
Thank you Kathy. Appreciate it.
- [32:13]
Kathy Werzer
Tomorrow's Cure is a co production of Mayo Clinic and prx. Our producers are Deborah Balthazar and David Newtown. The show is edited by Genevieve Sponsler. Our sound engineer is Tommy Bazarian. Our theme was composed and produced by Terence Bernardo. Tony Carlson is our production manager and our executive producers, Jocelyn Gonzalez. The Mayo Clinic production team includes Pui Hong Ang, Margaret Shepherd, Samantha Clarkson, Kara Mangold, Felipe Sosa, Kim Onna and Bryn Sanke. Special thanks to Preston Spire for audio visual production support. Be sure to follow Tomorrow's CURE wherever you get your podcasts. I'm your host Kathy Werzer. Thank you so much for listening. Want even more health information and insights from the experts at Mayo Clinic? Sign up Today for Mayo Clinic's email newsletter at www.mayoclinic.org Email Signup podcast from PRX.