MIA GAUDET: Hello, everyone.
I'm Mia Gaudet from the American Cancer Society.
How about I tell you about a story
about how we're making cancer research
advances in months rather than years, compared
to just a few years ago?
Machine learning is impacting cancer research
across the continuum of cancer.
Today I want to tell you about a recent breast cancer
project that leverages the American Cancer Society's
long-term investment in cancer data resources,
together in partnership with Slalom and Google Cloud.
For over a century, the American Cancer Society
has dedicated its mission to preventing cancer, saving
lives, and diminishing suffering from cancer through research,
advocacy, education, and service.
As the strategic director of breast cancer research
at the American Cancer Society, I
lead a portfolio of research in cancer prevention
and prognosis.
The more we learn about breast cancer,
the more we understand it's more than just one disease.
So my colleagues and I have dedicated our careers
to better understanding the risk factors for all types of breast
cancer so that we can advance prevention and treatment
of different types of breast cancer
using personalized prevention and treatment approaches.
As cancer researchers, we share a common problem
with many of you.
That is, we have a wealth of data around us
from many diverse sources.
But it's really understanding, how can we yield new insights
into this data?
Data must be collected, standardized,
and analyzed so that the results can
be interpretable and useful.
In our case, we had over 20 years
of lifestyle data and cancer tissue specimens
from women who were diagnosed in the American Cancer Society's
cancer prevention study II, which
is one of the largest cancer research projects in the US.
Slides from these tissue samples,
such as the ones that you can see held here,
can provide new insights about breast cancer.
But the challenge is how to identify and use
clues beyond what a pathologist can see under a microscope
so that we can gain new insights into the different types
of breast cancer.
Last fall, we initiated a collaboration
with machine learning experts at Slalom and Google Cloud
to unlock breast cancer insights from the breast tissue samples
themselves.
So a first step--
and one important, perhaps, to your organization--
is to understand each other's language and tools.
I don't speak fluent computerese,
and my colleagues at Slalom don't speak epidemiology.
But it's important that we understand
each other's perspective and insights.
Working together, we overcame barriers
with the slides themselves.
So there were many-- there was a lot of variability
in the quality of the slides, as well
as the variability in the color, and artifacts,
such as the blue ink that you can see at the bottom there.
From there, we tiled the images to improve
computational efficiency, and applied unsupervised machine
learning at scale using ML Engine.
And this helped us to identify patterns in the tissue images
that you can see here, colored by the different colors
to represent the different patterns that were
detected in the tissue images.
And so some of these patterns that we detected
were similar to what the pathologist sees
under the slides, such as differences
in the cell center caused by the cancer, among other features.
Finally, we grouped these tiles, based
on these different patterns, into groups.
And it would take a team of pathologists years
to identify these patterns.
Even then, each of those pathologists
may not be able to do this consistently.
But a machine-learning algorithm allowed us--
a machine-learning algorithm learned and identified
these patterns without any bias that humans bring,
and also far exceeded the pace of a pathology team.
So any good scientist first checks the quality control
of their data.
So the first step was to examine what
these different groups might represent,
from what we know from the clinic.
And in fact, one of these pattern--
one of these groups showed us how--
the fat cells in--
showed us the fat cells in the different cancers,
as you can see from the box in the blue.
But other groups brought up patterns that we didn't expect,
which is one of the benefits of using an unsupervised approach.
So in the past few months, we've been matching these clusters
with other data--
surveys completed by these women over the past 20 years,
before and after they were diagnosed with breast cancer,
and we're looking at these clinical data, their lifestyles
and other exposures over their lifetime.
And with these data, we hope to better understand
how to prevent different types of breast cancer
and identify the characteristics of the most deadly
of the breast cancer types.
Our ultimate goal is to provide individualized recommendations
for cancer prevention, early detection, and treatment.
At the American Cancer Society, we, like you,
are passionate about our success and what it
means for the world.
And like you, we feel the urgency in our mission,
the need to garner insights from the data around us
and then to act, to give patients
years rather than months.
So finally, I'd like to thank Slalom and Google
for moving us closer to this goal in ways
that we couldn't have imagined even just a few years ago.
Thank you.