diagnose the type or grade of cancer. “Tumor cells
can [be] quite difficult to identify amongst all the
lymphocytes and other cells and structures,” says
Lillard, who works in Alcester, England.
The Mind in the Machine
AI is already changing modern life. For a variety of
applications, computer scientists have produced soft-
ware that uses algorithms to analyze and categorize
data and make decisions based on that data. This is
the idea behind self-driving cars and virtual assistants
like Siri and Alexa. The photo-sharing app offered by
Google recognizes human faces. It can group similar
objects and differentiate between cats and dogs, and
can even be “trained” to recognize beloved pets.
(These algorithms aren’t perfect, though. A group of
MIT students showed that Google’s photo app misidentified a 3D printed turtle as a rifle.)
In a study published online in February 2018
in Nature Biomedical Engineering, researchers
from Google and Stanford University in California
reported on algorithms that can detect risk factors
for cardiovascular disease by identifying abnormalities in a person’s eye.
These examples of AI each use a deep-learning
algorithm, essentially an artificial brain that attempts
to mimic the learning process encoded in the human
brain. Just as people get better at a task through
repetition—like riding a bike or doing long division—
deep-learning algorithms show improved performance as they accumulate and analyze more data. So,
for example, the algorithms that outperformed the
human pathologists in the CAMELYON16 study are
expected to get better as they analyze more slides.
Hugo Aerts sees a future in which deep-learning
algorithms do the heavy lifting in cancer detection and
characterization. Aerts is an expert in computational
imaging at Harvard Medical School in Boston. In
pathology, these algorithms could analyze whole slides,
not just hot spots, quickly. In radiology, algorithms
could analyze 3D CT scans to measure physical features like size and shape to guide treatment decisions.
These techniques could be useful in diagnos-
ing a variety of cancer types. Consider lung cancer.
Diagnosis often happens when nodules are detected
in a person’s lung by a CT scan, but about 90 percent
of small nodules found in the lung turn out to be
benign. It’s important for oncologists and radiologists
to know whether they need to act, and AI can help
differentiate between malignant growths and those
that won’t continue to grow. “AI is really good at
doing this automatically, fast and with high accuracy,”
says Aerts. “A physician would take an hour to do
what an algorithm can do in a matter of minutes.”
A Bright Future
In recent years, radiology has gone almost completely digital, says Aerts. Patients can have copies
Using Computers to Make
a Good Test Better
Cancer pathologists and radiologists are increasingly turning to
artificial intelligence to better and more quickly analyze slides and
images. But the use of computer programs to improve patient health
isn’t new. “Weʼve been doing Pap testing this way for a long time,”
says physician and epidemiologist Peter Gann of the University of
Illinois at Chicago.
In the 1920s, Greek physician Georgios Papanicolaou conducted
research that would revolutionize the detection of cervical cancer by
introducing a way to identify abnormal cells. The disease was a leading
cancer killer of women at the turn of the 20th century, but since the
adoption of the Pap test in the 1940s, the death rate has plummeted
in developed nations. (In resource-poor countries, where people
have less access to health care, the disease remains a leading cause
of death for women.) The development and use of tests that detect
cancer-causing strains of the human papillomavirus (HPV) has also led
to a decline in incidence and mortality.
Part of a Pap test involves identifying abnormal cells in a sample collected
from a patient. Pathologists have traditionally determined whether the
cells are cancerous, but the task is laborious.
The late 1990s brought the introduction of computer programs
designed to automatically find cancerous cells in cervical slides.
Those include the ThinPrep Imaging System, which was approved
in 2003 and has been widely adopted. Studies of the system have
found that it significantly increases the detection of abnormal cells
compared to manual screening done by human eyes alone. Such
systems won’t make skilled pathologists obsolete, says Gann. But they
do show how technology can help reduce the time needed to analyze
slides and get accurate results to patients more quickly. —S.O.