The Future of Pathology with AI: Future Directions and Clinical Implementation

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, envision AI rapidly analyzing digitized pathology slides to assist diagnosis, highlight uncertainties, assess tumor heterogeneity, and support clinicians—enhancing accuracy while maintaining vital human collaboration.

Toufic Kachaamy, MD: Can you walk us through the workflow that you have today, and what you imagine for the future? Biopsy was obtained, hematoxylin and eosin stains were done, and what happens? They're put in a scanner, and in 5 minutes, you get a report? Does it say 98% chance of this? If it's not one of the 120 cancer subtypes that you talk about, does it say unrecognized? Like for our viewers, can you explain how this [looks] today, and how you envision [this evolving] in the next 5 years?

Kun-Hsing Yu, MD, PhD: The workflow we are envisioning is similar to how you proposed. Essentially, we still need to obtain informed consent and the sample from the patient, and then we can use standard scanners or any digitization method to [turn] the pathology samples into a machine-understandable, digital format. Once we have that, we can apply our published machine learning model to make the first-line screening and evaluation. The output of our model could be a summary report on the most likely diagnosis with the confidence level, or the level of probability that our machine believes the particular sample belongs to the different categories, and also the molecule-based prediction, as we discussed. For example, we can also predict the clinically actionable mutations and clinically actionable genomic variations related to specific treatment in order to inform the right clinical actions.

In addition to this, we're also thinking that we could use a user-friendly user interface to further identify the regions or machines leveraged to make the prediction. For example, if the diagnosis is colorectal adenocarcinoma, then we can first identify the regions that our machine used, that were used to inform, for example, the grade of the colorectal cancer, and also the regions that our machine used to determine microsatellite instability. Those may be the regions largely related to tumor-infiltrating lymphocytes. In addition to this, we can also have a section to further output the regions that our machine wasn't very sure about. And this could be important, because we know there could be a lot of autodistribution data points or some uncertainty even in expert-level diagnosis, so perhaps we can help save clinicians' time by automatically identifying the potential regions that require more extensive evaluation or even seeking a second opinion from a human expert. After all of this, we will be able to ensure that our machine will be able to facilitate a clinical diagnosis without missing some of the real cases or atypical manifestations that may merit additional review.

Toufic Kachaamy, MD: This is fascinating. One of the areas of my interest is human/machine interaction. We've seen in the gastroenterology world that sometimes AI improves outcome and sometimes AI worsens outcome; it depends on how the human/machine interaction happens, and [whether] the human has a positive view of AI or not. We are emotional beings at the end. From what I'm hearing from you, your system would highlight areas [indicating that we should] spend 5 minutes on this to get the diagnosis, or [specifying that it is] not certain [so we may] want to scrutinize this, instead of just spitting out [a diagnosis of] microsatellite-unstable adenocarcinoma. It's going to facilitate and make the pathologist way more effective in getting the diagnosis. Did I get that right?

Kun-Hsing Yu, MD, PhD: Definitely. We are thinking that we shouldn't hide some of the intrinsic uncertainty in medical practice. Instead, our AI can quantify this level of uncertainty and seek a second opinion from expert clinicians.

Madappa Kundranda, MD, PhD: Dr Yu, this is fascinating, in so many different ways. We are literally starting to just scratch the surface as it pertains to AI and pathology. You've just done some real phenomenal work in the context of trying to move the field forward. One of the questions that I have for you, and you kind of alluded to this a few seconds ago, [pertains] to tumor heterogeneity. Can you just comment on that for a second? Because that's a huge part of it, as we look at it across the spectrum of oncology. How can AI and these predictive tools address that concern?

Kun-Hsing Yu, MD, PhD: Definitely. So, as we know, most tumors are quite heterogeneous, and even for the same sample coming from the same patient, we can always see some levels of heterogeneity in terms of their microenvironment within the sample. Our approach, leveraging AI, is that we can even quantify this amount of heterogeneity. For example, we can quantify the amount of tumor-infiltrating lymphocyte in different regions and different microenvironments from the same sample.

With this information at hand, we can first quantify a heterogeneity score for each sample, and this may be useful for us to further interpret the genomic profiling results coming out of the sequences. And we can also use this information to guide the selection of treatments or even model and understand the evolution of the disease. In this case, we are thinking that our AI can augment many of these ongoing investigations on tumor heterogeneity by first quantifying this heterogeneity and investigating its association with clinical outcomes and other important variables.

Madappa Kundranda, MD, PhD: I couldn't agree more with that. For us in oncology, I'm dating myself, but almost like a decade and a half ago, molecular profiling came on board, and they said that it was the fourth tool that we had. Now, we have AI. But I think the key thing [is that we have] expert physicians, and expert pathologists, in this case, actually being able to review this. That's where the beauty of oncology is; it's truly a multidisciplinary sport. There is no way that we can get to a point wherein everything is done by the machine and there's no human interaction. I think that is the key part of it. And now, more than ever, this whole multidisciplinary approach and this collaboration has to be an integral part of what we can do to get the best outcomes for our patients. That's my takeaway from today. Your work has been phenomenal, and we really look forward to you doing a lot more, bigger and greater things, and continue to collaborate with all of us. Truly, thank you so much for your time today. This was very educational for me, and this would certainly be something that I'm looking forward to having ongoing conversations about.

Kun-Hsing Yu, MD, PhD: Fantastic. Thank you so much for having me.

Madappa Kundranda, MD, PhD: Dr Kachaamy, any last words before we wrap up?

Toufic Kachaamy, MD: Yes, Dr Yu, I just want to say that your work is inspirational. It is showing us a glimpse at the future, and as Dr Kundranda said, we look forward to continuing this conversation and seeing where you will take us, and then help in making patients outcomes better.