Advances in artificial intelligence
are slowly transforming the specialty, much the way radiology is being
transformed by similar advances in digital technology.
John Halamka, M.D., president, Mayo Clinic Platform, and Paul
Cerrato, senior research analyst and communications specialist, Mayo Clinic
Platform, wrote this article.
Any patient who faces a potential
cancer diagnosis knows how important an accurate, timely pathology report is. Similarly, surgeons often require fast
pathology results when they are performing a delicate procedure to determine
their course of action during an operation. New technological developments are
poised to meet the needs of patients and clinicians alike.
AI can improve pathology practice in
numerous ways. The right digital tools can automate several repetitive tasks,
including the detection of small foci. It can also help improve the staging of
many malignancies, make the workflow process more efficient, and help classify
images, which in turn gives pathologists a “second set of eyes”. And those
“eyes” do not grow tired at the end of a long day or feel stressed out from too
much work.
Such capabilities have far-reaching
implications. With the right scanning hardware and the proper viewer software,
pathologists and technicians can easily view and store whole slide images
(WSIs). That view is markedly different from what they see through a
microscope, which only allows a narrow field of view. In addition, digitization
allows pathologists to mark up WSIs with non-destructive annotations, use the
slides as teaching tools, search a laboratory’s archives to make comparisons
with images that depict similar cases, give colleagues and patients access to
the images, and create predictive models. And if the facility has cloud storage
capabilities, it allows clinicians, patients, and pathologists around the world
to access the data.
A 2020 prospective trial conducted by University of Michigan and
Columbia University investigators illustrates just how profound the impact of
AI and ML can be when applied to pathology. Todd Hollon and colleagues point out that
interoperative diagnosis of cancer relies on a “contracting, unevenly
distributed pathology workforce.”1 The process can be quite
inefficient, requiring a tissue specimen travel from the OR to a lab, followed
by specimen processing, slide preparation by a technician, and a pathologist’s
review. At University of Michigan, they are now using Stimulated Raman
histology, an advanced optical imaging method, along with a convolutional
neural network (CNN) to help interpret the images. The machine learning tools were
trained to detect 13 histologic categories and includes an inference algorithm
to help make a diagnosis of brain cancer. Hollon et al conducted a 2-arm,
prospective multicenter, non-inferiority trial to compare the CNN results to
those of human pathologists. The trial, which evaluated 278 specimens,
demonstrated that the machine learning system was as accurate as pathologists’
interpretation (94.6% vs 93.9%). Equally important was the fact that it took
under 15 seconds for surgeons to get their results with the AI system, compared
to 20-30 minutes with conventional techniques. And that latter estimate does
not represent the national average. In some community settings, slides have to
be shipped by special courier to labs that are hours away.
Mayo Clinic is among several
forward-thinking health systems that are in the process of implementing a
variety of digital pathology services. Mayo Clinic has partnered with Google and is leveraging their
technology in two ways. The program will extend Mayo Clinic’s comprehensive
Longitudinal Patient Record profile with digitized pathology images to better
serve and care for patients. And we are exploring new search capabilities to
improve digital pathology analytics and AI. The Mayo/Google project is being
conducted with the help of Sectra, a digital slide review and image storage and
management system. Once proof of concept, system testing, and configuration
activities are complete, the digital pathology solution will be introduced
gradually to Mayo Clinic departments throughout Rochester, Florida, and
Arizona, as well as the Mayo Clinic Health System.
The new digital capabilities taking
hold in several pathology labs around the globe are likely to solve several
vexing problems facing the specialty. Currently there is a shortage
of pathologists worldwide, and in some countries, that shortage is severe.
One estimate found there is one pathologist per 1.5 million people in parts of
Africa. And China has one fourth the number of pathologists practicing in the
U.S., on a per capita basis. Studies predict that the steady decline of the
number of pathologists in the U.S. will continue over the next two decades. A
lack of subspecialists is likewise a problem. Similarly, there are reports of
poor accuracy and reproducibility, with many practitioners making subjective
judgements based on a manual estimate of the percentage of positive cells for a
biomarker. Finally, there is reason to believe that implementing digital
pathology systems will likely improve a health system’s financial return on
investment. One study has suggested that it can “improve the efficiency of pathology workloads
by 13%.” 2
As we have said several times in these
columns, AI and ML are certainly not a panacea, and they will never replace an
experienced clinician or pathologist. But taking advantage of the tools
generated by AI/ML will have a profound impact of diagnosis and treatment for
the next several decades.
References
1. Hollon T, Pandian B, Adapa A et al. Near real-time intraoperative
brain tumor diagnosis using stimulated Raman histology and deep neural
networks. Nat. Med. 2020. 26:52-58.
2. Ho J, Ahlers SM, Stratman
C, et al. Can digital pathology result in cost savings? a financial projection
for digital pathology implementation at a large integrated health care
organization. J Pathol Inform. 2014;5(1):33; doi:
10.4103/2153-3539.139714.
