Latest / Diagnosing Dx / 98% of Cancer Patients Outside the US Miss Genomic Testing. Can AI Change That?

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98% of Cancer Patients Outside the US Miss Genomic Testing. Can AI Change That? · Diagnosing Dx

• David Filby: welcome to the show this week, Travis. How are you? Travis Wold: Great. Thank you, David. Appreciate you having me here. David Filby: Yeah, absolutely. And where in the world are you? Travis Wold: right now I'm in Jacksonville, Florida. David Filby: Lovely. Are you watching the World Cup? Travis Wold: A little bit, mainly the highlights. So it's been kind of a fun couple weeks here. I've seen a lot of excitement. David Filby: Yeah, I think it's awesome. It's awesome. Every time the World Cup comes around. So it's great that it's in North America and getting around really cool so Travis Wold: Yeah, are you going to any games? David Filby: definitely not when I saw the price Travis Wold: Ha ha David Filby: when I seen the price of tickets. Travis Wold: Yeah, yeah. David Filby: Justifying that to the wife might be Travis Wold: Yeah. David Filby: a tough one. But yeah, it gets the whole country going, I think. We've got quite a lot to get into today. I'm actually really excited because it sort of feels like what Imagenomix are doing is at the forefront of everything that's really interesting and exciting in the industry at the moment. but I think before we do that, because what's equally as exciting is some of the experiences you've been through in your career, I'd love to get a bit more into your background, your career, what you've been doing up until now and sort of you introduce yourself. Travis Wold: So born and raised in Alaska. I'm half Alaskan native. grew up in a small fishing town where they do that show Deadliest Catch. So everyone asks if I still like fishing. And I say, not if I have to, since I was on the boats for hours and hours a day in the cold water and pulling in nets all the time. But you know, it was a good experience coming from that and really learning about what free health care looks like, right? You know, in the Alaska native system, you have the Indian Health Service and you get "free healthcare." And we appreciate it and it's good until you get into the advanced diseases where you need more advanced testing, more advanced medications, then you're really looking at only generics, right? And that extends out past that. So you know our passion at Imagenomix and when we came out with our mission statement is cancer genetic testing for everyone. Alaska is just a snapshot of my background into all the LMIC countries out there that don't get any mutation-targeted testing to be put on a targeted cancer therapy to help fight their cancer, right? the testing's really expensive. You need it to be put on those drugs. using Imagenomix AI in the beginning, just as a screening tool, rule in, rule out mutations for a very inexpensive price until we can potentially get CDx on some of these mutations, is one way to allow more access globally to cancer patients. So that spoke home to me from where I'm from and my background and you know, trying to answer that question actually answers a much bigger question outside of just small Alaska. David Filby: So that's interesting. So your background in rural Alaska is a really good insight into a challenge across the country that is really common, the access to a better standard of care for patients and which shouldn't be concentrated in big cities like New York and so forth, where you know healthcare is probably really advanced and robust. so this is a a personal experience that you've got growing up there that's actually motivated you to come on board. Travis Wold: Yeah, and it's really encouraging. I mean, to be in this timeline and having this type of technology being advance and being able to bring this out of the NYU Langone laboratories to the rest of the world is super exciting to see. There's already great targeted therapies out there, you know, AstraZeneca, Pfizer. there's so many Eli Lilly, they have great targeted therapies. Just not enough patients are gaining access to them. Because they have that mutational fence, if you will, right? Until you get that testing, you don't get to cross. So being able to identify more of those and giving access to more targeted therapies to fight those cancers is what we're really passionate about at Imagenomix. David Filby: Interesting. So take us back to what you did in your career before Imagenomix and Travis Wold: Yeah. David Filby: up until you'd taken the role and now your current role with Imagenomix. Travis Wold: Yeah, I started off in sales, got into that portion and moved into Florida where you know it's a much bigger market and eventually licensed a technology out of Northwestern. So a friend, Sam Zhang and I launched a company out of there. It was a multiplex PCR for H. pylori we did really well. We sold that in late 2010. and at the time, one of my current mentors, Bonnie Anderson, was starting a company called Veracyte and they were about to commercialize. And I met her and Chris Hall, the now president of Personalis and Rick Lanman, the former CMO of Guardant, who was at Verisight too. So we had a really nice pedigree there. and it was just an amazing company. And I learned so much there on, you know, being able to rule in, rule out patients out of thyroid surgery and their commercialization models. But during that time there, I I was able to also launch the international markets and it was very difficult. You know, we were working with 44 countries, but y they just couldn't afford any of this type of genetic testing. my next company I went over to was a company called Circulogene, the liquid biopsy space, doing NGS and we got the cost down for the countries to a thousand dollars per test. They said if you can get the cost down to a thousand dollars per test, We're gonna send you hundreds per week. We'll get ministry coverage, et cetera, et cetera. And so we trialed it, right? We did it for six months and we got two tests per month. You know, they still couldn't afford it at even $1,000 per test with the masses for their cancer population. So AI, I think, is gonna be a beautiful entry point for a lot of these countries and patients to have access, even if it's just a screening tool in the beginning that rules in, rules out the mutations, and you test the. population that has a high percentage towards having a potential mutation like an EGFR/KRAS a PIK3CA right? Like they're still rare, they're 25% and under, but countries can't afford to test everybody, right? Because you run out of your budget really quickly. But can you use some an inexpensive screening tool for? you know, fifty to a hundred dollars to rule in, rule out those patients, enrich that population, and then pay for that more expensive NGS testing. Right? So that's our goal right now at Imagenomix until AI has caught up to where it could actually replace some of the NGS. but NGS is a great great tool at this point. we actually think early on AI will expand the usage of NGS because of the Rule in, rule out methods that we're utilizing to just enrich that pool of positives. So we can turn up the sensitivity. But instead of them testing a thousand out of a thousand NGS for thousands of dollars, maybe they only test 200 to have a very high likelihood of having those mutations, right? So for countries that have those budgets, that opens it up for them to allow more patients to go on these targeted therapies for their particular cancer. David Filby: Hmm. Okay, so in that example of working with a country that you've had to work hard to get the cost down, which it still allows you know, the product to be profitable and as business to thrive, and they ultimately have ended up giving you such low volume at two a month, you're saying that actually Imagenomix sits before that NGS test because it's a it's a screening test. But with the right screening, you can actually improve the volume of NGS because there's a much more likelihood that that NGS test is going to show what needs to be showed. So countries and healthcare systems are going to look at that and go, we've already been screened. We've taken a certain population of patients down to a very high likelihood, and therefore we should be now running NGS on this. So actually, volume of NGS should then go up in that screened population. Travis Wold: Yeah, and because you think about it, there's a few problems right now. One one is cost of the NGS. The second, turnaround time. So most clinical trials fail in oncology just from turnaround time, right? You know, two to three weeks minimum, depending on how they ship out the sample to the laboratory. did it pass the first time and yield a result the first trial? about 25% of NGS samples are QNS or quantity not sufficient. So, you know, the physicians go in with a fine needle aspiration into the lung, into the breast, they collect just enough tissue to call that a cancer and maybe a little bit more, right? Then you go through an H&E process, an IHC process, you know, maybe multiple stained slides on that tissue block. So by the time it gets off to the next gen sequencing, the NGS portion, 25% don't have enough tissue. Where Imagenomix layers in is once they build that H&E slide, which is on 100% a confirmed cancer. globally, right? So this is something that can scale globally, not just in the US. We can then just run that off the scanner, the image. We work with multiple scanner companies and then we yield a result in about one to two minutes. David Filby: Wow. And so you've come in as the CEO of the company. Travis Wold: Yeah. David Filby: What attracted you to the company and how did that come about? But also how did it come about taking the IP out of NYU Langone? Travis Wold: Yeah, really good question. I had met one of the founders during COVID. they had a they Matija Snuderl he's fantastic. And he he created a 500 gene plus panel that's FDA approved And they could get the cost down quite a bit on that panel. And I thought maybe that was the answer to go international and even domestically on some of these NGS panels, right? Just to keep costs down on them. even clinical trials. But what what we realize over time is that still wasn't the answer on that portion, because staffing the laboratories is very expensive. You know, running a NGS lab, it's expensive. So even if you can get reagent costs down, you still have the HR, you have all the people, you have the div the machinery, like it's not scalable into LMIC countries. You know, you think about Africa, India, etc. Like that requires a lot of specialized personnel. AI requires a scanner. We're now testing a $500 camera that goes onto a microscope from a company called VitruvianMD Verge Health Tech Fund out of Singapore introduced us to them, one of their other partner companies. So we're trialing this inexpensive camera that goes onto microscopes. And then secondarily, we're looking at potential what's called stitching of cell phone camera images. you know, we got we got a continually go down from an FDA approved Philips scanner, that's very good, super high resolution. And then we're going to try out the Vitruvian port camera. And then we'll see if we can do this off just simple cell phone images eventually. That would be scalable globally, right? the problem you end up having even with scanners, sending those into you know different countries all over the world is they have to be fixed. And they have to be operated, right? So that that's a tough portion of of scaling globally, which is how we think about this, is you know, we want to start off with these small projects, but all of them are being built to be globally scaled. David Filby: Mm. And so how did that happen in terms of you coming on board to Imagenomix? Travis Wold: all right, yes. So I met Matija and the team. They had this story, and it was exactly that, right? we want to be able to offer mutational testing to the masses. You know, outside in inside the US, only about twenty-five percent of lung, breast, and colon get tested. And those are the top three. The rest are below that, right? Outside of the US, ninety-eight percent do not receive any NGS testing or which would lead to targeted therapy, right? So that 98% is a really high number. And so if we can start to solve that portion, that that's really our biggest goal. And that's why I came over to Imagenomix is that was their passion, is can we solve this just off a simple H&E? I went to Matija and the team, they published the first paper in 2018, right? and it was in Lung. So then they filed the patents on this, which were granted in 2022. and that that's what I thought was really powerful about this is they had the expertise around it, but they also had the patents on this as well. David Filby: And so your your experience with multiple exits in your career as well, and the commercial background you have, did that feel like a really natural fit there in terms of what they bring to the table and already had and what you could bring to the table as a as a business plan from here? Travis Wold: Yeah, yeah, it I fit in really well. I mean, my whole background has been you know in the cancer therapeutics and also international markets. And I look at I got so lucky in my career leading up to this. You know, the other portion that was part part of the passion project for me is they came to me originally for gliomas and brain cancer mutations. And so we have product called NeuroInsight that's for IDH1, IDH2. IDH2 doesn't have a really good IHC around it, so immediately we would add 15% market share to a couple companies that already have IDH drugs out. and we're our initial studies in that show that we're better than the IHC very quickly. And my father passed away of a glioma brain cancer. He never got any testing for any of this, right? So when they came to me for that, that that really piqued my interest. the And that'll be a future product. Right now we're going after lung and breast. Those are the bigger markets. They have the most targeted mutational therapies. So we're gonna start there because we can access the most amount of data there. And then we wanna go into the the brain cancer side as well. David Filby: And so what what data have you been able to gather so far and clinical trials and studies that you've been able to gather? Travis Wold: Yeah, so a lot of what we're doing right now in preparation for a July launch has been utilizing retrospective samples with NGS Truth. So for instance, we talked to some of the potential pharma partners in our pipeline. we have access to a lot of these samples, and so we'll run our algorithms on them and let them know first in a pilot study can we pick this up or not. Some are really easy to pick up, some are really difficult. So if they're difficult, we let them know that and say, hey, we can either try this again and access more, or this is just not a good target at this time. Like let's pass on this. But if they do pass through our QC process and we find that we can find these targets, that that's a good targeted marker. So we're working on three right now to be launched later this year. David Filby: And so what is the story up until now as a company? You mentioned the patents, and then obviously you've come on board. What's happened so far in terms of capital raise, growth, and commercialization or partnerships that you you already have in place? Travis Wold: Yeah, good question. So we raised recently Modi Ventures led a round with Sahir and his brother Amir Ali with Modi Ventures. They're fantastic. They put us into their pipeline. They actually have a channel partner for us called PathPresenter. PathPresenter is an image management system that is in a ton of hospitals globally, right? And so PathPresenter is an image, they they hold all these images, and so they have. Hundreds of thousands of lung cancers, right? So they have all this breadth. But what's on those? We don't know. So Imagenomix will give depth to those lung, breast, eventually melanomas, brains. So we can give a lot more access to the data on what exactly is being held in those images. that's important for future studies, it's important for targeting, you can know. which areas of the country are having more of this particular mutation. it's important for targeting. there's there's so many things that you can extrapolate once you know a lot of this data inside just a lung cancer or a breast cancer. Like what's below that, right? David Filby: Trust him. And have you set up other partnerships or or had commercial traction elsewhere? Travis Wold: Yeah, so again on the image management side, the way we view them is is almost like an iPhone with limited apps at this point. Pathpresenter, Proscia, there's a few others out there, but Proscia we're also working very closely with because they have a very large image management they have a lot of images with NGS truth, right? So we did a three year deal with Proscia to start developing a lot of our products there. They also have a centralized application Where people can go in and pick the applications that they want, the pathologists. So eventually on lung cancers, not only will it load into the Prochia system, but they can also utilize the Imagenomix predict. Our particular one in lungs called Pulmogene, which will be the first one launched with Prochia in July. So now pathologists, pharma, biobanks, they'll all have access to Pulmogene to identify EGFR, KRAS, TP53. etc. etc. We have a probably about six genes that we'll put in. Those three will be the first, EGFR, KRAS, TP53. And now you can start utilizing these as a screening tool right away. So that's the way we view a lot of these partnerships is, you know, there's image management systems with Philips, Proscia, PathPresenter, Indica, etc. They need more applications to make them more robust in that pathology workflow. And Imagenomix is just one of those applications in the cancer side on lung and breast to start that can help with those workflows. David Filby: So because of where you guys sit in the workflow and in clinical workflows, how does the product development look and things like regulatory pathway, reimbursement? How do those things play into a product like this? Travis Wold: Really good question. in the beginning, we're gonna start as an inexpensive screening tool, right? that does not need FDA clearance because we're not a diagnostic at this time. You know, our our view on this is that AI is not quite ready to be a diagnostic in most of the disease states for mutational analysis in cancer. There needs to be a lot more studies and validation first. So we think doing this as a rule in, rule out screening tool in the beginning is gonna be the most powerful way to build up enough of that data to where you can eventually get this to where it could have a CDx indication and reimbursement. So it's all about, is it worth it? Right? Monetarily it's worth it because what we know is we can rule out anywhere from 70 to 80% of samples in lung and breast right now today, right? That do not need NGS testing for those particular mutations we're looking for. Okay? And these are the most common five or six in breast and lung cancer. We know we can do that today. We know we can call positives and turn the specificity up in our testing, but there still needs to be a lot of trials that go on with the AI side to develop trust. Especially with oncology pathology. That's a long window of trust. So we view this as screening till. We have enough proof, there's enough studies, and then we can start going after the reimbursement and the CDx. In the interim, it would be codes to reimburse for screening tools. there are some out there right now, but they're not being reimbursed. So the codes are established, but they're not being reimbursed at this time. David Filby: Mm. So there is there is still a goal to go down that path and ultimately yeah. Okay. you said at this point AI isn't ready to be a full diagnostic. I was gonna say, do you see that becoming but I think that there's an obvious answer to that. It's but probably a better question is how long, how far away do you think we are from AI being you know, the the the full diagnostic package. Travis Wold: that's a really good question. I don't know that it'll be the full diagnostic package, but even over the past year, from two years ago to a year ago, there were significant s advancements in what we're doing with the AI. From a year ago to now, you just feel and see the acceleration go even faster. So it was kind of like here and then it starts shooting up, right? So I don't have an accurate time window because it's moving so quickly. The partnerships that we're working on, what we're doing alone is not going to answer a lot of the questions that I think in the future could be. So responders, non-responders, right? So you utilize Imagenomix in the future, you're like, hey, this is an EGFR or a KRAS. But then even though you've identified that mutation and the targeted therapy is queued up. There's non-responders, even though they have those mutations, right? We don't know why. But we're talking to a couple companies that we're gonna be partnering with shortly to actually look at the 3D, 4D cell structure and figure out which ones will be responders and non responders. So what you want to look at is here's the mutation. Will they respond to the drug or not? Right? That's gonna be kind of this next step layer, is not just catching it. But what are they going to react to the best to get rid of that cancer or stop that cancer from continuing to expand? And maybe David Filby: Because of that, do you do you see the commercial pathway in partnering with pharma companies, biotech companies to to help conduct trials and develop their therapeutics, does that become stronger versus being strictly a screening tool, used in in live ongoing patients' workflows and journeys? Travis Wold: Yeah, good question. I and that's that type of advancement, if we can make that work and show that not only the mutation is there, but this patient will respond, not respond, that that's that next big hockey stick, right? And that 'cause that's what people that that's what the treating physicians want. It's great the mutations there, are they gonna respond or not? Right? 'Cause right now they're like, hey, the the data says they have a seventy five percent response rate, so we're gonna try it on this. You know that just making some numbers up. But now if we can actually do this and say, hey, it's an EGFR mutation and they're going to have a healthy response to this based on our data. Like that's really powerful. Or hey, David Filby: Hmm. Yeah. Travis Wold: we have these particular mutations and they're going to be a non-responder with these EGFR medications. We need to go a different route. Like that starts to tailor the therapies even more. But it also gives us a better understanding of what the AI is seeing. Right? Because what what AI is doing off these, you can't pick up with your eye. You know, so it's picking up something. And we know kind of what we know what those patterns are and what it's picking up now. But in the beginning you don't. You just put in truth, NGS truth, and it comes out with this prediction. You're like, huh, I wonder how it did that, right? And now we know how it's doing that. But over time, as you look at spatial and you look at these 3D forty images, you can now predict response. And we think that's gonna be the next layer of Imagenomix. we don't know how far out that is. The companies we're talking to have already proven this out. So now what we're trying to do is do this off in a single H&E slide with us plus them. And if we can do that over this next year, which is our plan, that that'd be a really powerful tool for drug trials, treatment selection. there's so many applications that just that could answer off a simple H&E that's made everywhere in the David Filby: super exciting. I I feel like the the the everyone talks about AI and a and how it's going to change the world and I think precision medicine is arguably the biggest winner from AI if we get it right because what it has the ability to do and exactly what you've just explained the difference between average therapies being prescribed and see how it goes to being completely precise with with specific therapies is such a big improvement on where we currently are. let me ask you this as a company, if I was to come into the company for a week, would I leave feeling you are an AI company, like a tech AI company, or would I leave feeling you're a diagnostics company? Travis Wold: I th that's a really good question. I think it we're really a good blend of both because, you know, all the AI tools we're using, you know, we want patient advancement eventually. Right. So it leans into that diagnostic. And, you know, we all come from a diagnostic background on the David Filby: Yeah. Travis Wold: medical side, aside from our AI team. And so diagnostics are still really important to us, right? And they, you know, diagnostic lab testing drives about 70% of medical decisions out there. So, can we help diagnostic testing that's currently out there advance just a little bit more? Like that's the goal right now with Imagenomix. And eventually you start getting CDx. I that's where a lot of this will end up going, and drug tailoring. there there's so much that AI is gonna be able to answer that we're seeing right now. but but it's still about a year away from us launching some of the testing that I think will really transform diagnostics, if you will. David Filby: So the next year is heavily focused on these partnerships and the access to samples that you can get and therefore more data, more proof of concept. Will you at some point need to raise more capital as those sort of milestones get closer and you need to sort of ultimately expand and truly commercialise? Travis Wold: Yeah, yeah, it's a really good question. So probably in about 12 months, we have a two-year runway right now. but probably in about 12 months we'll start at the Series A. So we have our goals right now to start monetizing our platform. We've already got great partnerships to do that. but we have another couple of tests coming out that we are applying for our patents for here shortly. And as soon as we do that, a One of them could be a blockbuster FDA breakthrough type of diagnostic AI test. And we're planning on launch or announcing that in October to be determined. We're working on this right now to see what timelines we can do, but essentially we'll be able to work or we have a test coming out in recurrence that doesn't exist yet. David Filby: So you see this as ultimately the once the first well the first test is already being used, you see this the expansion of the algorithm into other indications and other and other patients' challenges that you can solve. Travis Wold: Yeah, yeah. And we're really good at lung, really good at breast, really good at glioma. and there's a million companies out there, and there should be more. You know, we get this question all the time, and there's there's so many questions that need to be answered right now. And the AI portion of this is so young. there could be hundreds of these companies answering different questions in pathology. You know, we're partnering with three right now that everyone thought were competitors of ours. but they're actually just channel partners that answer different questions than we do. and there's a lot more synergies there than p competition. David Filby: With your diagnostics background, what's been the the most interesting learning curve you've had to come in as a as a CEO of what is really an you know, an an AI company? Travis Wold: Yeah, yeah, no, it's really good question. I think a couple of things that were so surprising to me is you know, at the wet labs you you have tons of employees, right? All to yield one test result. And at Imagenomix, we can do this with three people, right? And we have five now, we're coming up on seven here shortly. but we don't need as many people. and that was the biggest eye-opening portion for me. The second was just understanding how it's a continuous puzzle, which our team continually works on every single week on Monday, Thursday during our calls. And it's really exciting to hear, okay, here's the questions we have, here's the thought process around how we're gonna address these, here's the people that we're gonna call to validate some of this as well, and then here's our plan on moving this forward. Right. It's such an exciting time to start to figure out some of these questions that haven't been answered yet. and advancing diagnostics, if you will, versus just selling a me-too type test. Yeah. David Filby: what are the future plans and milestones that we should really look out for for the people that want to follow the story? Travis Wold: Yeah, really good question. So one of the metrics that we are going to start measuring, and this came out of Verge Health Tech Fund in Singapore, but also Modi, we want to measure how much of an impact we make globally, both within the US but also LMIC countries. Cause if we can figure this out with LMICs, we can launch this anywhere we want to. Right? So if we can democratize, which everybody says, we're democratizing cancer like We really have this goal. We want to we have this metrics that we measure. How many lives did we impact? and how many lives did we impact in those countries, right? Because it it's the only way that I think they're ever going to get access to targeted therapies is just through AI. it's scalable, it's fast, it's off an HE slide, it doesn't require, you know, really the personnel to run an NGS type test. So I think over time with EGFR, I think that's a really good marker. IDH and brain cancer is a fantastic marker that we have a really great initial test for. that could potentially be the first CDx type test, is the IDH1 for us at Imagenomix. we've been working on that for a long time. just because that's our passion project. And Matija and Dan are Dan's a neurosurgeon, Dan Orringer at NYU, and Matija the neuropathologist, so. That that one I could see potentially being a CDx just based on our initial data with that. It's already around the 95% AUC mark. And they don't have an IHC really for IDH2. Right. So that one I could see progressing pretty quickly. And that that would be a really neat milestone to have the first type of CDx, especially in the brain cancer world, that gets ignored a lot. David Filby: And so I I would imagine as well with your global goals, being an AI native heavy company with a lack of personnel requirement, especially in remote locations or to be in country where you're trying to deliver a service, compared to your previous role where you would have come up against these challenges, if I want to, you know, I want to operate in this country, we have to be able to you know have to build a lab there. The the world is your oyster, right? With with the technology you're building. Surely the the shackles are off. It's just, you know, let let let's go. Travis Wold: Yeah, and so, you know, aside from cost, right, which is the number one limiting factor. Second, is time for them. The third is not sending enough tissue or the blood gets QNS because you have to ship it on ice and it comes in, it's warm. that's a problem. The the other is shipping biologics out of a country, right? You don't have to do that with Imagenomix. All you need is the H and E slide image. You can upload it into the we're building a portal now. we anticipate our own portal be done in October. So by October, pathologists globally can access the Imagenomix portfolio and upload their slides and get a rule in rule out. This is not going to be your diagnostic, so just make sure nobody's prescribing based on that, right? But it should give them you know, if you're ruling in, we say, hey, high likelihood of an EGFR/KRAS, run a confirmatory test, you PCR. NGS, you can run small panel NGS, you can run a single panel PCR. those are cheap, right? They're inexpensive. that could be done within the next year. and that's where you start to see a lot of the the cancers and hopefully starting to subside, right? Or a lot more cures, if you will, or people going to remission, so You know, that that's our hope and goal. David Filby: That's awesome. Travis, what what's your outlook on the Diagnostics Omics market for 2026? How do you think the landscape is and where are we going? Travis Wold: it's still young. I mean, you see these you know, Paige and PathAI being purchased. there's gonna be a lot more consolidation over the next two years. They're still trying to figure out where AI fits, who to trust, and then how to use it, right? How do we utilize this best to advance our healthcare system? And that's still a question that everybody's up in the air on. David Filby: this has been super fascinating, Travis, to hear what you guys are doing, where you sit, and the benefits of the product you can bring, which and ultimately how AI is helping to achieve a more precise future, which is what we should all be hoping for. I'm really excited to follow the milestones that come up and what you guys go on to achieve. It really feels like you'll see a hockey stick at some point. It feels like that's quite natural. but thank you so much for coming on and sharing the insights. This was awesome. Travis Wold: Yeah, David, thanks so much for having us and we appreciate what you're doing out there and you know, spreading the message for us and others. It's something that we really need people to have more awareness around. and eventually, you know, we hope that patient advocacy groups can start announcing that Imagenomix has things for brain, lung, breast that are easily accessible to people that couldn't afford these in the past. David Filby: Yeah, awesome. Thank you, Travis. Travis Wold: Yeah. David, thanks so much. Appreciate it.