Understanding the Evolving Role of a Pathologist in Lung Cancer Care

Dr. Mary B. Beasley sat down for an interview with CURE at the IASLC 2025 World Conference on Lung Cancer to discuss the evolving role of pathologists in lung cancer diagnosis and treatment, as well as how emerging technologies like artificial intelligence may shape the future of the field.

Beasley is professor of Pathology, Molecular and Cell-Based Medicine, and professor of medicine at the Mount Sinai Health System, in New York, specializing in Anatomic Pathology and Clinical Pathology.

Transcript

Can you describe the current and future role of pathologists in lung cancer, and how emerging technologies may impact the field?

Pathology is very important in diagnosing lung cancer. Obviously, we're the ones that get the tissue. We make the diagnosis. We perform the molecular testing, which is obviously very critical to determine if a patient has a molecular alteration that might be amenable to a targeted therapy if they're at a stage that they need that. And we're investigating many other aspects of lung cancer as well.

What I'm going to be talking about primarily during my talk on Tuesday is the future of pathology. There is currently a major shortage of pathologists, so we're having increasing workload and increasing complexity of case workup. For example, a lung cancer case is not a matter of just giving the lung cancer a name. It has to have various predictive markers, like PD-L1, for example, other immunostains, and it needs molecular testing. And the number of things that you have to do with the lung cancer in order to understand how to best treat a patient is just increasingly becoming larger. That takes up more of our time. At the same time, we have fewer pathologists, and less time to participate in research, which is really important.

The role of the pathologist in translational research: traditionally, the pathologist is kind of the link between clinical and basic sciences. When we get a lung cancer, for example, we're looking at it under a microscope, and you're trying to determine, "Why did this lung cancer behave more aggressively? What's different about this one in regard to treatment response?" We're trying to tease all of that out, apply the molecular knowledge that we know, and apply other features of the tumor, like the stroma, the lymphocytes and things like that. Do those have meaning? And that's ultimately led to immunotherapy development, which is a mainstay now.

But the big thing that's coming down the pike that I'm going to talk about is AI, or artificial intelligence, and that's supposed to replace pathologists. I really don't see that happening. I think it's much more complicated than people realize. I'm hopeful that it will become a useful tool and help streamline our work so that we have more time to participate in research and keep up that end of things, rather than just being overwhelmed with clinical work, which is, of course, equally important. But you want to try to do both to move the field forward and improve things for patient outcomes.

I'm going to be talking about where we are with artificial intelligence. I think it gets a lot of press, a lot of headlines. A lot of people are jumping into this space, but in reality, not many pathology labs currently use digital pathology. They kind of don't. It's a lot of financial outlay to incorporate digital pathology in your lab, and then having digital pathology is not the same as having computational pathology or artificial intelligence that you can then apply to the specimens on either a research or a diagnostic level.

I'm going to be talking about where we are with that and some of the barriers to implementation, and just kind of a reality check on where we are with that.

Transcript has been edited for clarity and conciseness.

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