The Future of Pathology With AI: Molecular Subtyping and Genetic Testing

In this episode of OncChats: The Future of Pathology With AI, Toufic Kachaamy, MD, of City of Hope; Madappa Kundranda, MD, PhD, of Banner MD Anderson Cancer Center; and Kun-Hsing Yu, MD, PhD, of Harvard Medical School, discuss how artificial intelligence (AI) can rapidly predict molecular subtypes from hematoxylin and eosin (H&E) slides with 87% accuracy, enhancing but not replacing genomic testing.

Madappa Kundranda, MD, PhD: Let me just switch gears for a second. When we're looking at molecular subtyping of these pathology images, at some point, could AI replace genetic testing? Let me give you an example. When we look at the different molecular subtypes that we have in gastric cancer, or even microsatellite instability–high patients, how would AI factor into [this]? Right now, we are doing it through immunohistochemistry [IHC]—the basic [approach]—and then we are doing polymerase chain reaction [PCR]. Then there's a 5% discordance between PCR and doing IHC for [these patients], for example. How would AI factor into that?

Kun-Hsing Yu, MD, PhD: One exciting and kind of surprising finding from our recent paper, [which was] published in Nature, is that our foundation model can not only make traditional classifications based on pathology patterns, but we can also even predict their molecular subtypes and their molecule profiles just based on the standard H&E-stained slide. Then the natural next question is: How are we going to apply such a model? Will this completely replace the current genetic testing? Our thought is that perhaps this could be a useful tool to enhance some of the real-time genomic diagnosis.

For example, in certain cancer types, it's important to identify specific cancer subtypes with different treatments. Our AI model, operating on standard H&E-stained slides, will be able to identify patients with a high likelihood of harboring those crucial genomic variations. So, this could be a way to enhance the current genomic profiling process for [patients with] cancer. However, none of these models [are] perfect in identifying their molecular subtypes. As such, we don't think that, in the short run, our H&E AI-based approach would completely replace standard genomic profiling.

Madappa Kundranda, MD, PhD: What is the concordance and discordance that you currently have? What's the standard-of-care testing that we currently have?

Kun-Hsing Yu, MD, PhD: For many of the important clinical genomic profiles—for example...as you mentioned, microsatellite instability–high and -stable status—we are able to achieve an accuracy of more than 87%. [That means that] more than 87% of the time, we're able to make an immediate prediction at the time of pathology diagnosis. But there's still 13% of the time where we are discordant, with the final truth coming from the sequencers. Those are the domains that we believe additional research on different AI models, and the recent breakthroughs in agentic AI and many other new AI systems, may be able to help.

Toufic Kachaamy, MD: That's fascinating. One of the issues is time to get the results of genomic testing. I assume the AI will give it immediately, or very quickly. Is that correct?

Kun-Hsing Yu, MD, PhD: That's correct. We can optimize our model such that you will be able to provide diagnostic and molecular predictions within a second. This is quite different from the traditional approach, where we may have to wait for days, [or] even weeks, to get genomic results.

Toufic Kachaamy, MD: Have you been successful in, for example, getting a targeted agent approved based on your results, or is it still in the research realm?

Kun-Hsing Yu, MD, PhD: Yeah, we are currently partnering with a few pharmaceutical companies to work with early-stage trials, and we build our AI system as a part of companion diagnostics identifying the patient who would likely be responsive to the particular drug under investigation. We feel that this may be a fast track toward embedding our AI system into clinical practice.

Toufic Kachaamy, MD: I assume that we're only improving with time, right? If you have 87% concordance today, with time, it's going to be better given the fast improvement in this technology. This is fascinating.