The Future of Pathology With AI: Setting the Stage

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, provide an overview of the integration of artificial intelligence (AI) into modern pathology and the development of foundation models for cancer diagnosis.

Toufic Kachaamy, MD: Thank you all for joining us on this new series of OncChats. I am Toufic Kachaamy, MD, chief of medicine at City of Hope in Phoenix, [Arizona]. My cohost is Madappa Kundranda, MD, PhD, who is the chief of cancer medicine at Banner MD Anderson in Phoenix. And we are excited to be joined by Kun-Hsing Yu, MD, PhD, today, who is an [associate] professor in the Department of Biomedical Informatics at Harvard Medical School. Dr Yu developed the first fully automated artificial intelligence [AI] algorithm to extract thousands of features from whole slide histopathology images. He discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells and successfully identified previously unknown cellular morphologies associated with patient prognosis. His lab integrates cancer patient multiomics, genomics, epigenomics, transcriptomics, and proteomics profiles with quantitative histopathology patterns to predict their clinical phenotypes.

And if you feel that you're lost with everything that I talked about, continue [to listen], because he will explain all of this to us. Dr Yu, thank you for joining us. Can we start by you just giving us an overview of where pathology is today and how AI is being integrated? [Let's unpack this] before we jump to the future, which is really [where] your work [is focused].

Kun-Hsing Yu, MD, PhD: Thank you so much for having me, Dr Kachaamy. As you know, my lab is primarily focusing on developing novel AI approaches to analyze pathology data to enhance our current clinical practice. We have developed several foundation models that leverage a large and diverse amount of data to teach our machines how to recognize the basic pathology patterns related to diagnosis and prognosis. We've been fortunate to be successful in applying such platforms to many different clinical use cases, including a few prospective studies that are currently ongoing in our hospitals.

Toufic Kachaamy, MD: Thank you for this clarification, Dr. Yu. Now, my first question to you is: Can AI diagnose cancers using standard pathology slides today?

Kun-Hsing Yu, MD, PhD: That's a great question. In the past few decades, many researchers have been trying to develop quantitative approaches to analyze the whole slide digital pathology images. In our group, we show that using our recent foundation model approach, we can accurately diagnose more than 19 different types of cancers, including identifying the parts that were occupied by cancer cells from the adjacent benign tissue, as well as classify different types and subtypes of cancers that may have different clinical and treatment implications. We show that our diagnosis accuracy is on par with expert pathology evaluation. This indicated that as long as we are training the AI model using the right amount of data and with the right architecture, we can build expert AI systems that can recapitulate important diagnostic signals from standard histopathology slides.

Madappa Kundranda, MD, PhD: Excellent.