LOS ANGELES and REDWOOD SHORES,
Calif., July 9, 2020 /PRNewswire/ --
The Lawrence J. Ellison Institute for Transformative Medicine of
USC ("Ellison Institute") and Oracle
reveal a promising two-step technique to train a high-confidence
predictive algorithm for enhanced cancer diagnostics. The study
uses novel tissue "fingerprints"—discriminating microscopic
hematoxylin and eosin (H&E) histologic features—of tumors
paired with correct diagnoses to facilitate deep learning in the
classification of breast cancer ER/PR/HER2 status.
The approach was able to achieve unprecedented diagnostic
accuracy for an algorithm of its type and purpose, while using less
than a thousand annotated breast cancer pathology slides. The
findings suggest that the algorithm's ability to make correlations
between a tumor's architectural pattern and a correct diagnosis can
ultimately help clinicians determine how a tumor will behave to a
given treatment.
The study was facilitated by Oracle for Research, Oracle's
global program that provides selected researchers with access to
Oracle Cloud technology, consulting and support, and participation
in the Oracle research user community.
The research appears in Scientific Reports.
Challenges of medical machine learning
The challenge of developing artificial intelligence (AI) tools to
diagnose cancer is that machine learning algorithms require
clinically annotated data from tens of thousands of patients to
analyze before they can recognize meaningful relationships in the
data with consistency and high confidence. An ideal size dataset is
nearly impossible to gather in cancer pathology. Researchers
training computers to diagnose cancer typically only have access to
hundreds or low thousands of pathology slides annotated with
correct diagnoses.
To overcome this limitation, the Ellison Institute scientists
introduced a two-step process of priming the algorithm to identify
unique patterns in cancerous tissue before teaching it the correct
diagnoses.
"If you train a computer to reproduce what a person knows how to
do, it's never going to get far beyond human performance," said
lead author Rishi Rawat, PhD. "But
if you train it on a task 10 times harder than anything a person
could do you give it a chance to go beyond human capability. With
tissue fingerprinting, we can train a computer to look through
thousands of tumor images and recognize the visual features to
identify an individual tumor. Through training, we have essentially
evolved a computer eye that's optimized to look at cancer
patterns."
The first step in the process introduces the concept of tissue
"fingerprints," or distinguishing architectural patterns in a
tumor's tissue, that an algorithm can use to discriminate between
samples because no two patients' tumors are identical. These
fingerprints are the result of biological variations such as the
presence of signaling molecules and receptors that influence the 3D
organization of a tumor. The study shows that AI spotted these
fine, structural differentiations on pathology slides with greater
accuracy and reliability than the human eye, and was able to
recognize these variations without human guidance.
In this study, the research team took digital pathology images,
split them in half and prompted a machine learning algorithm to
pair them back together based on their molecular
fingerprints. This practice showcased the algorithm's ability
to group "same" and "different" pathology slides
without paired diagnoses, which allowed the team to train the
algorithm on large, unannotated datasets (a technique known as
self-supervised learning).
"With clinically annotated pathology data in short supply, we
must use it wisely when building classifiers," said corresponding
author Dan Ruderman, PhD., director
of analytics and machine learning at the Ellison Institute. "Our
work leveraged abundant unannotated data to find a reduced set of
tumor features that can represent unique biology. Building
classifiers upon the biology that these features represent enables
us to efficiently focus the precious annotated data on clinical
aspects."
Once the model was trained to identify breast cancer tissue
structure that distinguishes patients, the second step called upon
its established grouping ability to learn which of those known
patterns correlated to a particular diagnosis. The discovery
training set of 939 cases obtained from The Cancer Genome Atlas
enabled the algorithm to accurately assign diagnostic categories of
ER, PR, and Her2 status to whole slide H&E images with 0.89 AUC
(ER), 0.81 AUC (PR), and 0.79 AUC (HER2) on a large independent
test set of 2531 breast cancer cases from the Australian Breast
Cancer Tissue Bank.
While using Oracle Cloud technology, the study's groundbreaking
technique creates a new paradigm in medical machine learning, which
may allow the future use of machine learning to process unannotated
or unlabeled tissue specimens, as well as variably processed tissue
samples, to assist pathologists in cancer diagnostics.
"Oracle for Research is thrilled to support and accelerate the
Ellison Institute's trailblazing discoveries through advanced cloud
technology," said Mamei Sun, Vice President, Oracle. "The Ellison
Institute's innovative use of machine learning and AI can
revolutionize cancer research, treatment, and patient care – and
ultimately improve the lives of many."
Technique democratizes cancer diagnosis
In breast
cancer, tumors that express a molecule called estrogen receptor
look unique at the cellular level and fall into their own
diagnostic category because they typically respond to anti-estrogen
therapies. Currently, pathologists must use chemical stains
to probe biopsy samples for the presence of the estrogen receptor
to make that diagnosis, and the process is time-consuming,
expensive and variable.
The established algorithm aims to improve pathologists' accuracy
and efficiency in a digital pathology workflow by directly
analyzing tumor images to diagnose them as "estrogen receptor
positive" without staining specifically for estrogen receptor. The
study's results support the notion that the use of tissue
"fingerprints" may allow for a direct treatment response
prediction, potentially obviating the need for molecular staining
approaches currently utilized in cancer theragnosis.
An exciting application of this technology lies in the
possibility of deploying computer-assisted diagnostics in medically
underserved regions and developing nations that lack expert
pathologists, specialists and the laboratory infrastructure to
stain for molecular markers.
While the study suggests additional investigation is warranted
to gain a deeper understanding of AI's ability to determine
molecular status based on tissue architecture, it sets the stage
for future applications where the technique could potentially aid
in troubleshooting challenging tumor classification issues and
enhance human pathologists' abilities to arrive at correct
diagnoses and better inform treatment decisions.
About this study
In addition to Rawat and Ruderman, other study authors include
Itzel Ortega, Preeyam Roy and
David Agus of the Ellison Institute;
along with Ellison Institute affiliate Fei
Sha of USC Michelson Center for
Convergent Bioscience; and USC
collaborator Darryl Shibata of the
Norris Comprehensive Cancer Center at Keck School of Medicine.
The study's computing resources were facilitated by Oracle Cloud
Infrastructure through Oracle for Research, Oracle's global program
providing free cloud credits and technical support to researchers,
and was supported in part by the Breast Cancer Research Foundation
grant BCRF-18-002.
In addition to his appointment at the Ellison Institute,
Ruderman is an assistant professor of research medicine at
USC's Keck School of Medicine.
About Lawrence J. Ellison Institute for Transformative
Medicine of USC
The Lawrence J. Ellison Institute for Transformative Medicine was
founded in 2016 to leverage technology, spark innovation, and drive
interdisciplinary, evidence-based research to reimagine and
redefine cancer treatment, enhance health, and transform
lives. Under the leadership of Dr. David B. Agus,
MD, the Ellison Institute was designed to tackle the difficult
questions in health care and research to push the boundaries of
medicine forward. The objective of the Ellison Institute is
the rigorous and rapid translation of novel technologies into
practice for use in clinical, diagnostic, and laboratory settings.
The Institute is comprised of dedicated clinicians, experts and
thought-leaders from disparate backgrounds who have come together
to make a meaningful, positive impact on the lives of patients.
This one-of-a-kind Institute hopes to serve as a powerful catalyst
for innovation and reimagining the status quo in medical research
and cancer treatments. For more information, visit
Ellison.usc.edu.
About Oracle for Research
Oracle for Research is a global community that is working to
address complex problems and drive meaningful change in the world.
The program provides scientists, researchers, and university
innovators with high-value, cost-effective Cloud technologies,
participation in Oracle research user community, and access to
Oracle's technical support network. Through the program's free
cloud credits, users can leverage Oracle's proven technology and
infrastructure while keeping research-developed IP private and
secure. Learn more at
https://www.oracle.com/oracle-for-research/.
About Oracle
The Oracle Cloud offers a complete suite of integrated applications
for Sales, Service, Marketing, Human Resources, Finance, Supply
Chain and Manufacturing, plus Highly Automated and Secure
Generation 2 Infrastructure featuring the Oracle Autonomous
Database. For more information about Oracle (NYSE: ORCL), please
visit us at www.oracle.com.
Trademarks
Oracle and Java are registered trademarks of Oracle and/or its
affiliates. Other names may be trademarks of their respective
owners.
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