Where are the biggest opportunities to leverage AI in cancer diagnosis and treatment? What are the biggest barriers remaining to move away from a one-treatment-fits-all approach to treating cancer? And how are AI, radiomics, machine learning and deep learning helping to understand which patients will respond best to which treatments?

 

We will learn all that and more in this episode of Research in Action with Otavio Clark, M.D. Ph.D. and Principal Research Consultant at Oracle Life Sciences.

 

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Episode Transcript:

 

00;00;00;00 - 00;00;26;16
Where are the biggest opportunities to leverage AI in cancer diagnosis and treatment? What are the biggest barriers remaining to move away from a one treatment fits all approach to treating cancer? And how are ready omics, machine learning and deep learning. Figuring out which patients will respond best to which treatments will learn. All that and more on research and action in the lead in the world.
 
00;00;26;19 - 00;00;48;02
Hello and welcome to Research and Action, brought to you by Oracle Lifesci Answers. I'm Mike Stiles, and today our guest is Ottavio Clark and Oncology and Specialty Therapeutics executive at Oracle Life Sciences. Now that's a field he's been in his entire career. He has his Ph.D. in oncology and specializes in all things evidence based research, real world data, real world evidence.
 
00;00;48;02 - 00;01;13;25
And what we're going to be talking about today, AI and the critical field of cancer research. Octavio, thanks a lot for being with us today. I might tinker for the end of the invite. It's a pleasure to be here. And we are really discussing a fascinating issue. That is how the ACA is changing the healthcare landscape. But before we start, I'd like to make a disclaimer.
 
00;01;13;28 - 00;01;45;27
We would discuss a lot about the study's findings, but we have to to have in our minds that these results that you discuss, they are still early. These findings. We have yet to be validated in prospective longer term studies, but we will discuss the only things that we have a clear direction of the trend. You added that things are going, so it's important for everybody to to think about this product by the cancer, something introductory.
 
00;01;45;29 - 00;02;10;17
So I think that's pointing towards trends but not about something definitive when you see something moving on in this direction. Okay. Okay. Yeah, that's totally understood and understandable that that would be the case. I do really want to dive right into this so we can make good use of our time. So what are some of the more impressive advancements that we've made in cancer treatment lately?
 
00;02;10;17 - 00;02;40;18
And does that mean success rates are satisfactory? Has personalized medicine helped to that? Where are those most promising opportunities to improve personalized medicine where cancer is concerned? It's a revolution in personalized medicine. It changes everything in oncology. And honestly, when I was in the medical residence in 1996, 1998, I did not think that we could see these during my lifetime spent This person.
 
00;02;40;25 - 00;03;15;05
The medicine has changed the way that we practice quality because it's today for many different types of tumors. We can pick treatments that are tailored to read their genetic profiles, and it enhances the precision and the effectiveness of the therapies. We left our scenario before Where do we use the same drug for everything? And now we can get the genetic profile of the patient of the tumor and try to find a targeted therapy that is limited to any specific type of cell.
 
00;03;15;06 - 00;03;42;03
Sometimes growth genes. This is wonderful. It has improved a lot. The outcomes of the patients have been becoming better and better in the last years, but we still have challenges here. The first one is that we don't have this kind of personalized medicine for all types of tumors, and one very important things. Not all patients respond to the personalized medicine as we would expect.
 
00;03;42;05 - 00;04;13;05
What it means. We still have patients that do very well, but we still have patients that don't do so well as we would want to to to have it. So the overall success rate in treating cancer with this personalized medicine approach have improved, but they are not yet 653 across all cancer types in demographics. We are still trying to see some improvements in upfront patients elections.
 
00;04;13;08 - 00;04;39;27
That is, how can I making this personalization even better by selecting out the fraud patients that have a similar genetic profile, but that they can I can identify those that. Do you have a good response to the therapy and those that will not get a good response to the therapy? If we could do this separation based split, we would have a much more effective treatment.
 
00;04;39;27 - 00;05;10;15
Of course, what are the opportunities and being able to select those patients who are most likely to respond to a particular treatment and identify those who aren't likely to respond? I mean, how might those kind of better patient classifications affect the current staging systems and the epidemiology of cancer? That's a long history. But let's start. If you if you can select patients, we will, of course, be able to do two things.
 
00;05;10;15 - 00;05;32;29
The first one is offering the patients that whom you will you expect to have a good response to the treatment, to give an effective treatment, and you split the basis that we expect that you not respond to that kind of therapy. To me, you try to offer them some sort of therapy or to select a clinical trial for these patients.
 
00;05;33;01 - 00;06;06;08
Well, how are we dealing with this? First, there is are there is an artificial intelligence to that we call radio omics today. These are the army is is is a technique that can extract huge quantities of information from medical imaging like key MRI scans and so on. And these really omics can analyze very complex patterns that we human beings can not see and it can give us an additional classification.
 
00;06;06;08 - 00;06;41;20
And this is something that will help us in dividing this patient, possible responders and possible night responders when we integrated these Arabian Sea tourists in deep learning machine learning technologies, we can identify the subgroups of patients that will really be more beneficial. There is a very interesting study that was recently published this year to the European Studies. This patient included 1300 patients with no small cell lung cancer without early stage disease.
 
00;06;41;20 - 00;07;17;16
You let these early stage stage one station through this model was able to predict three, six, seven, 6% accuracy. The patients that would be old in not have a nearly relapse just after the treatment. So they analyzed the data from 3000 patients they put inside of these machine learning system. And in this system the tools could be told that around 40% of the patients could have avoided treatment that was not effective for them.
 
00;07;17;19 - 00;07;44;25
40%. This number is huge and it reflects what we see in practice. Even in this personalized medicine, we still have 46% of patients that would not respond adequately. The problem is we don't know how how to split the patients to be, how to they try to station. So they and these new tools, these artificial intelligence tools, the omics machine learning, deep learning, they are offering the opportunity for this better selection.
 
00;07;44;28 - 00;08;17;05
And of course it opens huge opportunities for research and development because, okay, we have now these subset of patients that we respond, what do you do with those that don't respond? So it's brought to the need for developing new drugs and new tools that when you get to these subset of patients that are not responding to current treatment into new developments and new new forms of treatment, well, but it is complex and it is still in its infancy.
 
00;08;17;05 - 00;08;40;27
Everyone's still trying to figure out what it can and can't do best, what the best applications are, What are the complexities of bringing a high end to cancer diagnosis and treatment? And, you know, in what ways do we need to kind of be careful as we start incorporating it? Yeah, we need to be very, very careful with this because we still don't know everything about even the specialists.
 
00;08;40;28 - 00;09;12;16
They they really don't understand how these tools fully functions. Well, we can really spend a day discussing this topic, but I'd like to call attention to three important feature is here. The first line is we have to care about data, privacy and security because these systems, they use patient data to be treatment. You know, you have to teach the machine about what to do, about what to do, analyze, and we have to have data from real patient.
 
00;09;12;18 - 00;10;03;21
And often these training data sets that people are using in different approach. So we have to be sure that they have privacy of the data. The security of the data is is assuring and that we have a legal standards like HIPA and that can maintain the confidentiality and the trust of the patient in the system. The second and very important one is the bias in many of these A.I. systems that you see that we have today, because they way that they are trained and again, the machine is learning what we want them to learn and they can sometimes perpetuate or amplify biases