The first AI breast cancer sleuth that shows its work
New AI for mammography scans aims to aid rather than replace human decision-making

Date:
January 14, 2022
Source:
Duke University
Summary:
Researchers have developed an artificial intelligence platform
to analyze potentially cancerous lesions in mammography scans to
determine if a patient should receive an invasive biopsy. But
unlike its many predecessors, the algorithm is interpretable,
meaning it shows physicians exactly how it came to its conclusions.



FULL STORY ========================================================================== Computer engineers and radiologists at Duke University have developed
an artificial intelligence platform to analyze potentially cancerous
lesions in mammography scans to determine if a patient should receive
an invasive biopsy.

But unlike its many predecessors, this algorithm is interpretable,
meaning it shows physicians exactly how it came to its conclusions.


==========================================================================
The researchers trained the AI to locate and evaluate lesions just like
an actual radiologist would be trained, rather than allowing it to freely develop its own procedures, giving it several advantages over its "black
box" counterparts. It could make for a useful training platform to teach students how to read mammography images. It could also help physicians
in sparsely populated regions around the world who do not regularly read mammography scans make better health care decisions.

The results appeared online December 15 in the journal Nature Machine Intelligence.

"If a computer is going to help make important medical decisions,
physicians need to trust that the AI is basing its conclusions on
something that makes sense," said Joseph Lo, professor of radiology at
Duke. "We need algorithms that not only work, but explain themselves
and show examples of what they're basing their conclusions on. That way, whether a physician agrees with the outcome or not, the AI is helping to
make better decisions." Engineering AI that reads medical images is a
huge industry. Thousands of independent algorithms already exist, and
the FDA has approved more than 100 of them for clinical use. Whether
reading MRI, CT or mammogram scans, however, very few of them use
validation datasets with more than 1000 images or contain demographic information. This dearth of information, coupled with the recent failures
of several notable examples, has led many physicians to question the
use of AI in high-stakes medical decisions.

In one instance, an AI model failed even when researchers trained it with images taken from different facilities using different equipment. Rather
than focusing exclusively on the lesions of interest, the AI learned to
use subtle differences introduced by the equipment itself to recognize
the images coming from the cancer ward and assigning those lesions a
higher probability of being cancerous. As one would expect, the AI did not transfer well to other hospitals using different equipment. But because
nobody knew what the algorithm was looking at when making decisions,
nobody knew it was destined to fail in real- world applications.



==========================================================================
"Our idea was to instead build a system to say that this specific part
of a potential cancerous lesion looks a lot like this other one that
I've seen before," said Alina Barnett, a computer science PhD candidate
at Duke and first author of the study. "Without these explicit details,
medical practitioners will lose time and faith in the system if there's
no way to understand why it sometimes makes mistakes." Cynthia Rudin, professor of electrical and computer engineering and computer science at
Duke, compares the new AI platform's process to that of a real- estate appraiser. In the black box models that dominate the field, an appraiser
would provide a price for a home without any explanation at all. In a
model that includes what is known as a 'saliency map,' the appraiser
might point out that a home's roof and backyard were key factors in its
pricing decision, but it would not provide any details beyond that.

"Our method would say that you have a unique copper roof and a backyard
pool that are similar to these other houses in your neighborhood, which
made their prices increase by this amount," Rudin said. "This is what transparency in medical imaging AI could look like and what those in
the medical field should be demanding for any radiology challenge."
The researchers trained the new AI with 1,136 images taken from 484
patients at Duke University Health System.

They first taught the AI to find the suspicious lesions in question
and ignore all of the healthy tissue and other irrelevant data. Then
they hired radiologists to carefully label the images to teach the AI
to focus on the edges of the lesions, where the potential tumors meet
healthy surrounding tissue, and compare those edges to edges in images
with known cancerous and benign outcomes.



========================================================================== Radiating lines or fuzzy edges, known medically as mass margins, are
the best predictor of cancerous breast tumors and the first thing that radiologists look for. This is because cancerous cells replicate and
expand so fast that not all of a developing tumor's edges are easy to
see in mammograms.

"This is a unique way to train an AI how to look at medical imagery,"
Barnett said. "Other AIs are not trying to imitate radiologists; they're
coming up with their own methods for answering the question that are often
not helpful or, in some cases, depend on flawed reasoning processes."
After training was complete, the researches put the AI to the test. While
it did not outperform human radiologists, it did just as well as other
black box computer models. When the new AI is wrong, people working with
it will be able to recognize that it is wrong and why it made the mistake.

Moving forward, the team is working to add other physical characteristics
for the AI to consider when making its decisions, such as a lesion's
shape, which is a second feature radiologists learn to look at. Rudin
and Lo also recently received a Duke MEDx High-Risk High-Impact Award
to continue developing the algorithm and conduct a radiologist reader
study to see if it helps clinical performance and/or confidence.

"There was a lot of excitement when researchers first started applying AI
to medical images, that maybe the computer will be able to see something
or figure something out that people couldn't," said Fides Schwartz,
research fellow at Duke Radiology. "In some rare instances that might be
the case, but it's probably not the case in a majority of scenarios. So
we are better off making sure we as humans understand what information
the computer has used to base its decisions on." This research was
supported by the National Institutes of Health/National Cancer Institute (U01-CA214183, U2C-CA233254), MIT Lincoln Laboratory, Duke TRIPODS (CCF-1934964) and the Duke Incubation Fund.

========================================================================== Story Source: Materials provided by Duke_University. Original written
by Ken Kingery. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan
Chen,
Yinhao Ren, Joseph Y. Lo, Cynthia Rudin. A case-based interpretable
deep learning model for classification of mass lesions in digital
mammography.

Nature Machine Intelligence, 2021; 3 (12): 1061 DOI:
10.1038/s42256-021- 00423-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220114103014.htm

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