CytoVeris | Alan Kersey, President & CEO

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Alan Kersey  0:01  

Thank you, Christiana. And good afternoon, everyone. It's so great pleasure to be here this afternoon to talk to you about the next generation of AI powered cancer visualization technology that our company Cytoveris is developing. All all cancer patients go through immense emotional stress. And for those facing cancer resection surgery, in the near future, there are five dreaded words that none of them want to hear. We didn't get it all. So why does this happen? Because it happens very frequently. Well, it's all about the margin. It's all about the status of the cancer margin during that during that procedure. And in any cancer surgery, the surgeon is faced with a delicate balance, I have to remove all of the cancer, but I have to leave as much normal tissue as I possibly can. In breast cancer, this means that the surgeon is trying to balance an oncologic outcome and a cosmetic outcome. But this sort of problem manifests itself in just about every type of cancer surgery. And in every case, it can potentially a positive margin results in negative and worse outcomes for that patient. If you look at the the data and the statistics on this problem, it's very clear that there is a big need to improve the precision of cancer resection surgery. If you just look at bladder cancer term T procedures were small lesions or root annually, or very frequently removed out of a patient's bladder, those small lesions, she's about 50% of those are deemed insufficient. And those patients have been faced complications are a repeat surgical procedure. In breast cancer, depending on the institution that the patient goes to, up to 35% of the patients can be told a week later, after surgery after they've been closed up and sent home that if we didn't get all the cancer, you have to come back for a repeat surgery. And that creates not only emotional stress, and other reoperation health burden to the healthcare system, of course, and complications for that patient in the future. And in radical prostatectomy up to 25% of the of the margin cases there are positive, and this leads to additional different types of complications for those patients. Most importantly, a two to three fold increase in the chance of biochemical recurrence of the disease in the future. So excited various, our mission is to change that through by enabling more precise intraoperative decisions, which will improve patient outcomes through the use of real time data provided an imaging provided to the surgeon so they can see how that how that resection surgery is going and and from those insights provide better, better outcomes. So the system we've developed is called tumor map and it allows for this guided cancer resection it requires in the system, it works on an ex vivo specimen. So we have a resected specimen out of a patient, it goes into the imaging chamber and the upper part of this system. And the system requires the patient to have no dyes, no agents administered prior to the surgery, and no dyes used on the specimen. Once you've had the surgery. The system basically relies on analyzing the endogenous biochemical makeup of the tissue and analyzing detecting where there's cancer presents. So what we're doing there is looking at the impact that cancer has on the tissue and the changes in the bio molecular and morphological makeup local to the cancer tissue, it provides real time information, the information is provided to the surgeon during the operational window allowing them to to for it to be actionable and improve the patient outcome. And we it this whole system is powered on machine learning algorithms which utilize a state of the art image processing algorithms and it provides an intuitive output to the to the to the surgeon as an overlay on the tissue image. So the benefits of the system are we believe as we take this into interoperative application will be increased precision, a seamless fit of this platform with the with the workflow, cost savings to the healthcare system and better outcomes overall for patients facing cancer resection surgery. The tumor map system the science behind it, the technology behind it relies on a convergence of three key technology areas, an understanding of course of cancer, cancer biology, and the biomolecular makeup of cancer, not only the cancer cells, but the cancer microenvironment, the extracellular matrix, everything that's going on inside that matrix, we have developed a multispectral optical imaging technology that is very powerful. This this this system utilizes multistage multispectral excitation, and multispectral imaging, which provoked which essentially provides a cube of images that relate that is very powerful in detecting the variety of different biochemicals associated with normal tissue types, and also diseased tissue types. And then we feed those, those images into the machine learning, and that machine learning learns the patterns and the subtleties, and indeed the modulation in those in those in those in those biomolecular signatures to detect cancer. So this is what the system ultimately will do. This is a is a piece of a lumpectomy specimen is on the left shown in white light imaged in the tumor map system. And the algorithm that we've developed, we've caught we call Onko site then basically takes mock that multitude of different images, it's passed through the algorithm. And what we do is simply overlay a cancer heat map. Now in this particular case, this is a section out of a lumpectomy specimens. So it's cut through the lumpectomy. So as we as we'd expect, the cancer the tumor is in is running down the center of that lumpectomy specimen, showing there is a good margin around it. But this is part of our training process, where we where we, we have done over 800 specimens like this and train the algorithm to recognize cancer tissue versus normal tissue by referencing this piece of tissue and tissue like it back to the pathology haich D slides. And that's what allows us to get a very measured, you know, a very strong differentiation capability in the algorithm. So when we run validation tests on the algorithm, by training on a subset of the data, then testing or training by leaving one patient out, we get phenomenally high performance in this system showing that it has developed a very strong potential for recognizing cancer over normal tissue. You can see here, this is for 73 patients, over 280 targets on those different blocks, that we have a sensitivity of over 90% of specificity that right up at night over 99% and an overall accuracy in the system of 97. So these are a few like point measurements that are made. But of course, the real challenge is converting this algorithm into a full specimen imaging to direct and guide the surgeon to to where they need to take further action. So this is how it looks as when the surgeon gets to use this. On the left hand side is again a white light image showing a follow on back to the specimen, you can see the sutures, which provides some referencing for the for the specimen. And on the on the right hand side, you can see an overlay color map a false color map of the status of the bio molecular signatures that we detect in that specimen. The blue and the Greens cool, they show no indication of cancer. And on this there are a couple of regions where there are red hotspots, the surgeon would be directed to those as areas that they need to look at further and explore and indeed take further action in terms of additional excision of tissue to ensure that there's there's there's no cancer, those additional excisions of shavings in the case of breast cancer can also be tested using this instrument to make sure that there's a clear margin. So with this guided tumor resection, it can lead to many many benefits for the patient. Decreased likelihood of things such as secondary surgeries in as in the case of breast cancer, fewer post operative complications, a decreased need for radiation and chemotherapy. Improved functional outcome. For example, in the case of prostate cancer, of course, the surgeon is always looking to do nerve sparing surgery and of course, cosmetic outcomes. Top of the list of course in terms of breast conserving surgery, there's a lower risk of recurrence and spread of the disease. And with this targeted operation, we can also look forward to more targeted therapies following the surgical procedure. The impact of course, for patients is immense as well. If we look at that the reduction of the emotional stress and burden across the patient population. If we just reduce the number of positive margins in breast cancer surgery nationwide, it would mean that 25,000 women didn't have to go back for re-excision surgery. In, in bladder in prostate surgery, we would improve the outcomes for up to 90,000 men in terms of improving the odds of preserving functionality and reducing the complications there. And likewise, in bladder 70,000 Repeat procedures can be avoided by the use of this guided technology. We are focused on we have developed the system and 10 proven out the algorithm in breast we are moving, we are working in bladder and prostate as well. And in for ex vivo specimen analysis, we are looking to take the technology into areas such as head neck, we're of course, Head and Neck Surgery, surgeons often use frozen section quite quite routinely, for the small biopsies that they look at in terms of proceeding with their surgery, we believe our technology could be used as an adjunctive to that or actually used in place of potentially that type of surgery. But the really interesting thing here is we believe this technology while we're using a wide field imaging system in our tumor platform, the way it stands, the technology is implementable in a probe based format. And we're looking to take that into into into various robotic and mis applications of surgical procedures. So the markets that we're already addressing represent in excess of a billion dollar market addressable market for us over there in the in the European and North American markets. So a very strong and growing opportunity. And we feel that with expansion through this platform into other oncologic indications. Of course, there'll be even more opportunity. But we do believe that the real value in this technology will be realized not through ex vivo specimen analysis. But with increasing trends in the use of robotic procedures for cancer surgery, building an in vivo probe based imaging system that can provide the surgeon real time information on on the cancer margin and the cancer. The residual cancer in a cavity during a robotic procedure will be really where the value is. And indeed, if you look at that robotics is side of things. And we do believe that real time molecular imaging will be one of the cornerstones of improved precision surgery. And and I think that the type of technology we're developing inside of Eros can produce transformative capabilities and accelerate adoption of the technology. Our team is, is we have 100 years of leadership experience across the team. If you want to learn more from about what we're working on and what we've done before. Please contact me in the breakout room after all I can tell you about our clinical plans and and where we stand in terms of raising capital. So the future of cancer surgery as we see it is we've got it all. Thank you