Taylor Canova:
All right, good afternoon, everyone and welcome to Noridian's Open Public Meeting. My name is Taylor Canova, and I am one of the Medical Policy Specialists here at Noridian Healthcare Solutions.

We will be presenting the following LCDs: Surgical Treatment of Nails and Automated Detection and Quantification of Brain MRIs.

Before we begin the meeting, I would like to make the following announcements. This meeting will be recorded, and the recording and written transcript will be posted on our website following today's meeting. All lines are currently being muted and will remain muted throughout the meeting. Only those who are registered to present will be allowed to comment on the proposed LCDs today. If closed captions are needed, please click the more button at the top of your screen, then go to language and speech and show live captions. A visual representation for these instructions is also shown on this slide.

For the presenters, you are being allotted 10 minutes to make comments. Your line will be opened when it is your turn to speak. Make sure you are not on mute within your system or we will not be able to hear your comments. You should be prepared to begin your presentation immediately when called upon and will hear the moderator's voice when one-minute remains. Please speak clearly to ensure that the system will be able to translate into captions for display.

If you submitted a PowerPoint presentation, please tell the moderator when to advance to the next slide. If you reach the end of your 10-minute time limit, your line will be muted, and we will move to the next speaker to allow all registered commenters time for their presentations. And by signing in today, you are giving consent to the use of your recorded voice and your comments. Please be mindful of sharing any personal health information during your presentation.

In addition to comments that are made today, all comments should also be submitted in writing. To ensure that all cited literature can be reviewed and considered during the LCD development process, commenters who reference published studies, articles, or other supporting evidence should submit a complete copy of each cited document with their comments. References provided without an accompanying full text copy may not be considered if the full text cannot be obtained for review. All written comments received will be recorded in the Response to Comments article.

I will now turn this meeting over to Dr. Luke Barre. Dr. Barre, you may begin.

Dr. Luke Barre:
Thank you so much, Taylor. So are we starting still with the Surgical Treatment of Nails or are we going to go to the Automated Detection? Perfect. Thank you. And then I guess if Dr. Poggio joins us later, then maybe we can go back to Surgical Treatment of Nails.

So thanks everybody for making the time to join this Open Meeting. I think it's just a general comment, as all of you know, it takes a lot of time and effort to engage with Medicare. I think all of us on the line on our side recognize how much effort it takes you to make time out of your day to engage with us and it is an important part of the process. The Open Meeting and comment period allows us to refine and hopefully make the best possible policy at the end of the day, and so it is truly appreciated that all of you are making the time.

This policy that we're discussing first instead of second is Automated Detection and Quantification of Brain MRIs. As all of you know, this policy is largely non-coverage for artificial intelligence assistive software tools for the automated detection and quantification of the brain MRIs.

I think I only also wanted to emphasize two points that Taylor already made, but they bear repeating, the first is that while the oral presentations and materials that you're giving today are very important and help to add a lot of nuance to the discussion, please make sure that you submit all of your comments in writing so that they can be responded to and integrated appropriately and clarified if needed. Secondly, and Taylor also said this already, but for any citations that you would like for us to consider, please do make sure to send the full copies of those manuscripts for us to be able to review and consider, particularly if they weren't already included in the bibliography of the policy.

With that, I will hand it over to our first speaker, which I believe is Dr. Smeets. Dr. Smeets, are you able to unmute?

Dr. Dirk Smeets:
Yes, thank you very much for the opportunity to comment to this presentation today. I would like to start sharing some insights of how the use of automated detection and quantification of brain MRI is actually impacting patient care.

The next slide are my disclosures, and after that, I would like to dive into Alzheimer's disease. So that's on the next slide, yes.

Where the role of MRI has been established, clinical guidelines and practice parameters today recommend the use of MRI to support the diagnosis, the staging, and the prognosis of the disease. And moreover, recently, the appropriate use recommendations of the disease modifying treatments on the market stress the importance of MRI for treatment eligibility, as well as for the FDA mandated safety monitoring. However, individual assessment of these MRIs, radiologists estimate disease progression based on eyesight, which is demonstrated to be subjective, variable, and consistently lacks sensitivity. Especially, longitudinal visual comparisons are notoriously difficult.

For example, for finding amyloid-related imaging abnormalities, miss rates up to 84% have been reported for community-based radiologists, and even specialized neuroradiologists indicate a lack of ARIA grading confidence. Because of these, the deaths that are caused by the disease modifying treatments and by ARIA, the FDA also has sent out a drug safety communication recommending earlier identification of ARIA-E. And note that unfortunately today over 250 deaths have been reported, some of which could have been avoided with better monitoring. That's why I believe that it's all and Medicare's duty to improve the monitoring of the beneficiaries, considering and receiving treatments to avoid these life-threatening events.

Besides more education, we strongly believe there is a role for quantitative image analysis software to help with supporting the diagnosis, treatment eligibility, and the assessment of safety monitoring.

On the next slide, I would like to summarize the evidence of how Icobrain helps with that.

In particular, we have shown in a randomized controlled study that Icobrain ARIA significantly improves the detection of ARIA-E and ARIA-H, and it also improves the grading which is shown on the right-hand side of this slide.

The data shows that Icobrain makes radiologists reading in clinical practice interchangeable with experts reading the clinical trials, and that is not the case without the use of Icobrain. It's also exciting to see that independent evaluations by the Mayo Clinic and Duke University confirmed these findings, demonstrating the value of Icobrain ARIA, especially to reduce the high miss rate of ARIA in a clinical practice setting, and hence to reduce the drug adverse events.

This is also further consolidated in the recent publication with expert panel recommendations. The panel with world renowned neurologists and neuroradiologists recommends the use of Icobrain with radiologists in the loop to enhance patient safety. And of course, we want to generalize the performance, and that's why I would like to point out that there is a robust performance monitoring process in place that ensures that every single analysis provided back to the radiologist is trustworthy and within the performance specifications. Moreover, there is regular retrainings done of the software with real-world data to demonstrate also improved performance over time. Current users of Icobrain indicate the extraordinary high usability and clinical impact, which demonstrates the real-world impact in different clinical settings.

In the next slide, we will also show the impact of quantitative MRI software for the diagnosis, staging, and treatment eligibility using Icobrain DM.

From multiple studies, Icobrain DM demonstrate that it's helping to differentiate Alzheimer's disease from cognitive normals and from dementias, other dementias like frontotemporal lobe dementia. The quantification itself does not allow a definitive diagnosis, but it improves the diagnostic process by increasing the sensitivity of the visual reads. Besides that, multiple publications show that there is a prognostic and predictive value of Icobrain. And that is highly relevant today to select those patients that benefit the most from the disease modifying treatments that are currently available.

Although today there is no standardized references for the brain volumes between the different vendors of quantitative MRI analysis software, there is evidence that the relative patient ordering is largely preserved and hence also the interpretation. And that is thanks to those population graphs for which you can see examples on the left-hand side. For Icobrain, that consists of over 20,000 subjects coming from a wide variety of scanners.

On the next slide, we go into multiple sclerosis, in which the role of MRI has been ever increasing.

The American Academy of Neurology guidelines, for example, require the monitoring of evaluating disease activity and progression on MRI. The hallmark are the new and enlarging lesions indicating acute inflammation. On top, brain volume loss is also widely recognized as an important prognostic market. Currently, physicians estimate disease activity and progression visually and missing through that a substantial proportion of acute inflammation. And this leads to missed opportunities to treat patients better, as excellent disease modifying treatment options are currently available.

The use of quantitative image analysis software, such as Icobrain MS, unlocks the data that allows to identify treatment failure earlier and prevents irreversible disability that happens in these patients with multiple sclerosis. Of particular interest is the ability of Icobrain to track the detectivity longitudinal historical data are automatically compared with the current study, and that allows to identify disease trajectories that are impossible to reliably assess through a single time point visual review. This is nicely demonstrated on the example here on the right-hand side of the slide, where the disease trajectory is also compared with normal aging in other MS patients.

On the next slide, we're going to summarize the evidence of clinical value of Icobrain and other tools in the market.

Indeed, a recent meta-analysis showed a pooled effect of 46% increased lesion activity detection with confidence intervals that are significantly above zero. And that indicates that this improved performance can also translate in improved outcomes. And that is done through a decision analytic health economic model that demonstrates the improved outcomes, but also at a lower cost, up to $15,000 for a 10-year window. The capability of improved detection of lesion activity has been independently confirmed at studies at the University of Buffalo in the US, and universities in Australia and Belgium. Also, for brain atrophy, the utility was confirmed in a study through an international registry with over 3,000 patients.

Moreover, the evidence is strong that Icobrain MS works also on diverse populations, with studies of a Japanese, Brazilian, Saudi Arabian, Australian, and of course, American population, across a broad spectrum of age. This shows that these models are trained on data with sufficient diversity.

To end, I would like to summarize in my next slide that we believe that there is a strong need for quantitative image analysis software in Alzheimer's disease and multiple sclerosis, especially because of the low sensitivity to find ARIA and progression of MS.

We have shown evidence that Icobrain improves the detection and grading of ARIA and increases the sensitivity to pick up subclinical disease activity in MS, while being generalizable through a robust quality control and performance monitoring framework.

Hence, we would like to request Noridian to reconsider the proposed LCD and possibly construct a policy that identifies ideal patient use. For example, it's covered when ordered by a treating physician, limited to FDA cleared tools used as adjunct to radiology interpretation, and requiring an indication where longitudinal quantitative MRI analysis is expected to affect management —

Taylor Canova:
One minute.

— including the monitoring of multiple sclerosis, the ARIA surveillance, and the neurodegenerative disease evaluation. Finally, I think also technical quality controls and physician oversight are essential to include in that policy.

So with that, I would like to thank Noridian for the opportunity to share our evidence and vision on how the quantitative image analysis software could contribute to patient impact.

Thank you very much.

Dr. Luke Barre:
Thank you so much, Dr. Smeets. Appreciate the comments, context, and careful presentation that you've crafted.

With that, we will move to Dr. Bash. Dr. Bash, are you able to unmute?

Dr. Suzie Bash:
Yes, I'm here.

Dr. Luke Barre:
Wonderful. Good. Okay.

Dr. Suzie Bash:
Thank you so much for allowing me to comment. My name is Suzie Bash. I'm a neuroradiologist at RadNet. These are my disclosures.

Go ahead and keep advancing with me, Taylor. Next slide.

The AI innovation of quantitative MRI, or QMRI, is frequently used in clinical practice, enabling precision-based medicine. Next slide. Go ahead and just keep advancing, Taylor. Thank you. Can we move to the next slide, please?

Okay, this is an example of what segmentation looks like. Next slide.

So why is quantitative MRI important? Well, FDA clear quantitative MRI tools improve diagnostic value by providing objective, quantifiable volumetric data, which augments accuracy and plays a critical role in single time point and longitudinal assessment of disease. Visual analysis alone shows low consistency compared to quantitative software, especially in subtle cases, and remains subjective and less sensitive. Integrating volumetric tools into clinical practice enhances diagnostic accuracy and consistency in the evaluation of neurologic diseases such as dementia and multiple sclerosis.

So QMRI adds critical value to brain MRIs. It's an augmented tool ordered by the referring provider. QMRI is a separate entity that provides critical added value to a brain MRI. The quantitative data improves accuracy, reduces reader subjectivity, and enables precision-based, patient-centric care. The neuroradiologist's clinical interpretation of QMRI findings is essential. So for example, deciding whether statistically enlarged ventricles reflect hydrocephalus or central atrophy, you need the neuroradiologist for that. QMRI results can directly impact patient management.

So let's use some examples here, starting with dementia. Why is QMRI important in Alzheimer's disease? Well, imaging detects biomarkers, which are now required for the diagnosis and staging of biological Alzheimer's disease. And biological Alzheimer's is defined using the ATN system, where "A" for amyloid, "T" for Tau, and "N" for Neurodegeneration, which we can directly detect with quantitative MRI.

So if you look across the Alzheimer's continuum here, you can see the biomarkers are present throughout the disease. This is our treatment window, stage 3 and stage 4, but unfortunately patients, over 50%, have a delayed diagnosis, so we really want to catch them at the beginning of the treatment window. We know from sub-study analysis of the clinical trials, they do much better on the medications if you can catch them early.

So quantitative MRI's role in the Alzheimer's landscape, go ahead and advance the slide, we use it for eligibility screening, disease staging, and ARIA surveillance.

Starting with eligibility screening, if its neurodegeneration is more advanced, we can use it to help differentiate Alzheimer's from frontotemporal dementia and dementia with Lewy bodies type atrophic pattern.

And really, when we move on to eligibility screening, we can use this software to detect the amount, go ahead and advance the slide, the amount of white matter, vascular, microvascular ischemic disease in the brain. It will give you an automated objective Fazekas Score. It will also automatically detect the number of micro-hemorrhages and superficial siderosis. This is an example of the report here.

Go ahead and progress the slides, and keep going.

So this is the microvascular ischemic disease report here with the objective Fazekas Score provided.

The next slide is an example of the NeuroQuant Hemorrhage Report where it's calculating the number of micro-hemorrhages on the current study and prior as well as screening for superficial siderosis.

Now quantitative MRI is also used for staging and that's really a lot of what we use it for now to assess how advanced that atrophic neurodegeneration is at the time the patient presents. Time is brain for Alzheimer's disease. So we're really trying to catch patients, ideally when they still have relatively preserved brain volumes but a positive amyloid PET.

So I'm going to show you an example here of how we use this in clinical practice. This was a husband and wife that both had a mild cognitive impairment, and they both had a positive amyloid PET. So the clinical question is which patient was most likely to benefit from anti-amyloid therapy? The family and the neurologist did not want to put both the husband and wife on treatment, only one. So a brain MRI with quantitative MRI was ordered. And what you see here on the left, go ahead advance the slide, is the husband's MRI of the brain with QMRI, and then on the right, the wife's.

If you move to the next slide, these are the reports of the husband and the wife. And you can see here that the husband has much less atrophic neurodegeneration compared to the wife. If you look at the red circles there, that's what is statistically significant. Next slide.

You can see the number of red boxes that are statistically significant are much less for the husband when compared to the wife, so the wife's atrophic neurodegeneration was much more advanced than the husband's. Next slide.

The neurologist and the family really opted to start anti-amyloid therapy for the husband only, and the anti-amyloid therapy was directed to the patient most likely to benefit. So quantitative MRI directly impacted patient management.

So on the baseline MRI, QMRI will aid in eligibility screening by quantifying micro-hemorrhages and the extent of microvascular ischemic disease. It will help identify patients at the earliest stage of Alzheimer's disease where they have less neurodegeneration, helping the neurologist decide which patients are likely eligible for and will benefit most from the anti-amyloid therapy. Treatment benefits are greatest at the earliest stage of disease.

Now, we also use it to assess for the side effects that you can see sometimes with medications, which we call ARIA.

And the next slide here, you'll see an example of what ARIA can look like in the brain.

Go ahead and advance.

This is a patient of ours that had ARIA-E, and you see the automatic segmentation as it progressed over time. It will actually measure the largest lesion for you. Next slide.

And it will also detect the number of micro-hemorrhages as well as superficial siderosis at present in this particular patient.

Just go ahead and keep advancing the slides here.

This is automated segmentation here. This patient didn't have superficial siderosis.

And the next slide shows what the report output would be, where it will measure the largest lesion for you when you're assessing for ARIA-E on the left. And on ARIA, when you're assessing for ARIA-H on the right, it will calculate the number of micro-hemorrhages on the current study and prior, as well as if superficial siderosis is present. Next slide.

This patient had moderate ARIA-E and mild ARIA-H, so the dosing was correctly suspended. Now, there is impact of using these AI tools. This study, next slide, shows significant improvement of ARIA detection and severity assessment when you use an AI assist tool. Manual ARIA screening and follow-up can be difficult.

Progress the slides, please.

In one study, 84% of local radiologists initially missed the ARIA on MRI. Inter-reader variability is a significant challenge for ARIA screening, and accurate interpretation is critical as it can directly impact therapy decisions. So CMS now covers anti-amyloid therapies. ARIA monitoring is required under the appropriate use criteria for safe administration of covered therapies. QMRI improves the accuracy of ARIA detection. The FDA Drug Safety Notice states that early ARIA-E identification mitigates serious and fatal events.

Now let's move on here to multiple sclerosis, where this software will detect and color code plaques throughout different areas of the brain.

Keep advancing the slides, please.

It will show which plaques are new or increasing in size. So next slide.

Increased or new would be this orangish color. Decrease would be bluish color. ext slide.

If a patient has moderately severe or severe plaque burden, it's very hard visually to see what is new or enlarging, but very easy for the software to do. It's hard visually due to differences in slice sampling and head angulation. Next slide.

Here's automated detection of individual lesions. And then next slide, let's talk about how we use it.

And this is a clinical patient that I had, a 65-year-old with MS. The patient was on Copaxone for many years but had a progressive hemiparesis. An annual brain MRI was ordered with quantitative MRI.

The next slide shows you here the lesion burden on the current study was 6.95 milliliters compared to the prior study of 4.57. And it also will tell you the volume of new, enlarging or shrinking plaques. Next slide.

The plaque burden had increased by 52% over one year. Our neurologists will switch disease modifying therapies typically if they plaque burden increases by more than 20% a year. So the baseline MRI, based on these baseline QMRI results, the patient's medication was changed to Vumerity. A six-month follow-up, they had significant clinical improvement. So here, quantitative MRI directly impacted patient management.

Evidence shows that high efficacy disease modifying therapies reduce relapse rates and reduce disability progression and brain atrophy. Neurologists find quantitative MRI highly valuable because it directly informs treatment decisions. It will also detect things like thalamic volume. Progress to the next slide, please.

Taylor Canova:
One minute.

Dr. Suzie Bash:
Thalamic atrophy is a strong early predictor of cognitive impairment and also related to physical disability score. It's very hard to detect thalamic atrophy by visual assessment alone but very easy for the software.

Keep progressing.

This is just the second and third page of the report. And if you progress on, this is the automated report impression here. So basically, QMRI will improve detection of disease.

Keep progressing through the slides, please.

24% of patients were assessed to have disease activity when the software wasn't used. Next slide.

76%, that jumped up to 76%, of patients that were assessed to have disease activity, which means new or enlarging when you use the software. Next slide.

It also improves the reliability of reading, so it improves both the intra-reader and inter-reader variability.

So QMRI improves detection and accuracy of new or enlarging plaques, accurate annual quantitative tracking, informs prognosis and management. Visual assessment of lesion change is less accurate, and objective measurement reduces inter-reader and inter-reader variability. Quantitative MRI guided management changes can improve quality of life.

And lastly, I'd just like to talk about this clinical validation trial.

Keep progressing through the slides here.

Where we use QMRI at time point 1 and time point 2.

Keep going.

And what you can see here is the hippocampal volumes and these other biomarkers remained identical at both time points, so high volumetric quantification accuracy across two time points, consistent clinical classification. And this was a multi-reader, multi-center clinical validation trial. It supports the reliability and consistency of QMRI.

So in summary, referring neurologists value QMRI and will continue to order it. Early and accurate detection and monitoring guide optimal treatment, better treatment decisions, improve patient health and well-being, and continued coverage ensures equitable access for every single patient.

Thank you so much.

Dr. Luke Barre:
Well, I mean, that was impressive. I think that there's probably a round of applause going around the room. Dr. Bash, thank you for that very informative presentation. As much as you had to go through those slides quickly, and this applies to everybody, please do make sure that your comments are submitted in writing.

As you know, we're paying very close attention to the presentation. There's a lot of information there and so submitting them in writing will just help to make sure that we can actually give them full consideration. So thank you again, Dr. Bash.

Our next speaker is Dr. Tanenbaum. Dr. Tanenbaum, I wasn't sure if, are you under LT and are you able to unmute?

Dr. Lawrence Tanenbaum:
I just did.

Dr. Luke Barre:
Perfect. Great. Over to you.

Dr. Lawrence Tanenbaum:
All right, great.

So you've seen some very impressive material and testimonials. So I didn't see much point in rolling them out again, but I thought I would just give you my perspective based on my experiences on the issues at hand today.

I think we all know that medical imaging functions is the heart of the healthcare enterprise, and we know that MRI and CT represent the most impactful healthcare innovations over the last 75 years. When it comes down to what a radiologist does, we are tasked to extract insights from hundreds to thousands of medical images and perhaps define a definitive and life-changing diagnosis. But at the very least, our job is to dictate the next steps in a patient's treatment.

Treatments, as you know, as you've heard, can be very costly, particularly at scale, and sometimes carrying significant risks.

I think at this point, you've got a good sense that there are limits to what human eyes can perceive, particularly when looking at serial studies over time and looking at the evolution and progression of a disease, particularly in patients with a disease is subtle, or you know, on the contrary, extensive.

Now, I've been practicing for 37 years as a neuroradiologist, and I've always had the task to make sure that studies we do have the appropriate quality and deliver the appropriate value. And my background is in community, academic, as well as the outpatient settings. And I can tell you that I'm acutely aware that challenges exist in delivering these critical insights in these circumstances, as you've heard in the case with multiple sclerosis. With a heavy burden of disease, you know, how can we quantitate significant change over time with just our eyes? It's virtually impossible. Patients with dementia undergoing disease monitoring therapy, the challenges in making the critical detections alone for anyone, even the best of circumstances. In reality, you heard significant numbers for the average circumstances where we fail. In all of these cases, in both of these cases, an accurate and quantitative output is critical decision making, obviously. Changing, say, to a more effective MS medication, as you've heard from Dr. Bash's examples, or pausing administration of anti-amyloid therapy in the presence of complications like ARIA.

It's easy to see that today's automated tools elevate the ability of a radiologist to deliver the promise to you of these expensive imaging examinations. Treating physicians, typically neurologists, rely upon this output, increasingly insist these tools are employed for their patient studies. Without consistent reimbursement, these practices are just discouraged from offering these tools. And when used, despite the absence of payment, the burden of the cost of these tools tends to fall on the patients that we're here to help.

You've seen a lot of compelling information. In my humble opinion, I'd like to thank you for my opportunity to summarize the way these issues fall upon me and my practice setting. Thank you for your attention. Thanks for the opportunity to address this group on this important topic. Please consider the information you've heard today in finalizing your policies. Thank you.

Dr. Luke Barre:
Thank you, Dr. Tanenbaum, succinctly put.

And with that, I believe that we are at our last presenter for this policy, which is to Dr. Johnson. Dr. Johnson, are you able to unmute?

Dr. Blake Johnson:
Well, I hope I already have.

Dr. Luke Barre:
Yep, there you are. I hear you loud and clear.

Dr. Blake Johnson:
All right. Well, sincere thanks for allowing me to speak on behalf of this important initiative.

I agree with the three prior speakers, all of whom I have worked with as the evolution of these AI tools came to the current level of being clinically reliable and applicable, not only because they're accurate and they decrease inter-reader variability so that you're getting a consistent interpretation that's accurate, quantitative, and reproducible. But in light of the development and the progress that has been made in disease modifying therapies, it's not just something of interest academically or increasing our accuracy for accuracy. It is necessary in order to benefit in the long term from these disease modifying therapies.

Dr. Bash, I believe, showed the slide of the curve of probability of reaching a disability score at which you are completely disabled. From 1980 to 2000, by the time the patient with MS was 60 years old, there's virtually 100% chance that they would be disabled, completely disabled. Today, after since in the last few years, that number has dropped at age 60 to less than 20% being disabled, which is a massive improvement of quality of life, and it depends on the new disease modifying therapies being deployed correctly. And that requires what the three speakers have already emphasized. And that is a very accurate diagnosis and the ability to detect not mass, you know, significantly visible change, but subtle changes. And you saw in the prior presentations how those subtle changes can be difficult to detect with the naked eye. Thalamic atrophy, it's very hard to see that. If I look at a scan from a year ago, the naked eye cannot detect a subtle change that is much more than the age expected amount of atrophy that should normally occur in brain volume, thalamic volume, etc.

You saw examples where there's a heavy lesion burden, i.e. many lesions, and your job is to detect a new lesion amongst a sea of additional pre-existing lesions. Due to slice variability and angle, as Dr. Bash mentioned, that can be very difficult or impossible.

Using these AI tools that, excuse me? I thought somebody said something.

Using these tools that mitigate the problem of correcting for all those variables and —

Dr. Luke Barre:
Doctor Johnson, it looks like you might have gone back on mute.

Dr. Blake Johnson:
Hello, can you hear me?

Dr. Luke Barre:
Now we can hear you, but you cut out for about the last 20 seconds there.

Dr. Blake Johnson:
Okay, thank you. So it is requisite that, I don't know exactly where I was 20 seconds ago, but it's requisite that you have tools that correct for slice variability and that are able to reliably detect subtle new changes. Slowly expanding lesions are important. It can be very hard to see a lesion that is slowly progressing when you compare the most recent surveillance study to the current study. These tools allow you to see that. It's very difficult to determine if lesions are regressing in one location and adjacent to a dense population of lesions, you have a subtle new lesion. My neurologists care about that new lesion, because that may modify their disease modifying therapy that they are utilizing in that patient and switch and change drug protocol, as Dr. Bash mentioned. So these tools are, without question, valuable in improving patients' quality of life by allowing more accurate diagnosis, which will in turn determine their therapeutic regime. The ability to decrease the inter-reader variability so you have accurate reproducible studies, and increased access to patients, who, as Dr. Tanenbaum said, would have to pay for these tools that we have available so that they are getting best practice for disease surveillance.

Dr. Smeets mentioned the increased sensitivity. Everybody emphasized the importance of the accuracy of the report. And I feel these are essential to provide what the neurologists need for adapting their therapies to the refined observations that the AI can provide in addition to the radiologists. These don't replace radiologists. They enhance their ability to perform it, to detect these changes that you've heard discussed today, and the outcome is obviously going to be dependent on the ability to effectively provide an accurate and timely diagnostic information so that the therapies that are being provided are optimized to that individual patient. The use of these increases access to the tools and in an environment where there is a worldwide shortage of radiologists, the ability to enhance their efficiency and their accuracy, I think are critical at this time.

I sincerely thank you for the opportunity to speak. And as Dr. Tanenbaum said, please consider this thoughtfully because this will affect patients' lives, quality of life, and longevity. Thank you all.

Dr. Luke Barre:
Thank you so much, Dr. Johnson, and to all of the speakers for this first LCD that we're covering.

And just as a reminder, as I'm sure you already will, please do make sure to submit your comments in writing. It's very important for us to be able to consider them. While we make every attempt to understand and take in what you're saying during your presentations, in order for it to be considered as part of the Response to Comment and then to offer any clarifications or changes that might be needed, the submission in writing is critical.

So with that, I do believe that we have the speaker that was registered for our first planned policy. Dr. Poggio, are you are you on the line? Are you able to unmute? Do see you on the line. Let me just, Dr. Poggio, if you're able to unmute, I think you should be able to from the settings. As you're working on that, I'll just briefly introduce the policy that we're referencing, and hope that you're able to unmute.

We are referencing a proposed policy, which is for Surgical Treatment of Nails. This policy has multiple covered indications, and I won't go through and enumerate all of them, as well as several important limitations. It is a policy that is part of our effort to have more consistent nationwide coverage and so is based on a policy that has been long standing in other jurisdictions.

With that, I do believe we have one registered speaker. Dr. Poggio, are you able to unmute now?

Dr. Anthony Poggio:
Can you hear me?

Dr. Luke Barre:
Yes, I hear you loud and clear with success. Good. Okay.

Dr. Anthony Poggio:
Oh, perfect. Yes, finally, technology scores.

Dr. Luke Barre:
Okay, good to hear your voice. Okay, I'll let you go.

Dr. Anthony Poggio:
All right, so thank you. I didn't bring a PowerPoint presentation because we support the policy.

Just to give some background, obviously, as podiatry, we see ingrown nails all the time, whether they're work-related, trauma-related, a million different causes. And the standard treatment is to remove that portion of the nail, especially on the big toe more so that's causing the problem. And we do so successfully. It's a high-success type procedure, and we do that. The problem comes in that sometimes the other side, down the road gets injured for whatever mechanism. And with the previous policy where we couldn't, you know, technically repeat the procedure on the same toe, even though it was in a different area. And that put us in a bind and the patient in a bind, because we would have to tell them that this may not be covered and usually we'd get an ABN just to be proper. And so by allowing additional treatments to the same nail, which I know sort of intuitively sounds incorrect, in fact is proper. And so we definitely support this change where we can do a second procedure, so to speak, on a different part of the nail, keyword being different part of the nail.

So we support the new change. The only sort of question we have is how would that be implemented? I know other carriers use a KX modifier for the subsequent procedure. If there has to be an appeal or other things submitted, that's time consuming and costly for both of us. And so trying to streamline it would be ideal. And so we support the policy and we thank you for changing the policy. Whether you guys want to go ahead with a KX modifier that would seemingly make life easier for people, we would definitely have to have the documentation to support that clearly but that might make life a little bit easier for everybody and more cost effective as well.

So that's pretty much the presentation I had. Again, appreciate you guys looking into this policy for us and making the changes. Thank you.

Dr. Luke Barre:
Thank you very much. Thank you very much, Dr. Poggio, and in particular, if there are recommendations that you would have for implementation, that could certainly be a part of your written comment as well, and that would be helpful for our team.

So thank you, and I'll pass it back over to Taylor Canova for closing.

Dr. Anthony Poggio:
Thank you.

Taylor Canova:
Thank you. In closing, we would like to communicate the next steps in the policy development process.

The comment period for the proposed LCDs will remain open until November 14th, 2026. All comments to be considered by our medical directors for the proposed LCDs must be submitted in writing. And to ensure that all cited literature can be fully reviewed and considered during the LCD development process, commenters who reference published studies, articles, or other supporting evidence should submit a complete copy of each cited document in their comments and references provided without an accompanying full text copy may not be considered if the full text cannot be obtained for review.

Written comments can be emailed to policydraft@noridian.com or mailed to the address on your screen. Comment information for our proposed LCDs are located on our website at noridianmedicare.com.

And upon review of the comments, our medical directors will either finalize or retire the proposed LCDs. Response to comments will be viewable in the Response to Comments articles. Please monitor our website or register for listserv notifications to be informed of actions taken on the proposed LCDs.

And Dr. Barre, do you have anything else you'd like to say before we end the meeting?

Dr. Luke Barre:
No, I appreciate the work that you all did, in particular those who are here in attendance and who made time. Again just thanks for making the time to help to inform these policies. It really is an iterative process. And so those of you who are taking the time out of your already busy schedules, it's much appreciated. Thank you.

Taylor Canova:
Thank you. And with that, that concludes our meeting. Have a wonderful day.