What You Should Know:
– Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) announce a collaboration to produce more accurate machine learning models for breast cancer screening and depression.
– In work funded through the PHDA-AWS collaboration, a research team led by Shandong Wu, an associate professor at the University of Pittsburgh Department of Radiology, is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer.
– A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging, including researchers from the
University of Pittsburgh Medical Center (UPMC), the University of Pittsburgh,
and Carnegie Mellon University (CMU), are working together to develop a more
personalized approach for patients undergoing breast cancer screening.
Last August, the Pittsburgh Health Data Alliance (PHDA)
and Amazon Web Services (AWS)
announced a new collaboration to advance innovation in areas such as cancer
diagnostics, precision medicine, electronic health records,
and medical imaging. One year later: AWS collaboration with Pittsburgh Health
Data Alliance begins to pay dividends with new machine learning innovation.
Researchers from the University of Pittsburgh Medical Center
(UPMC), the University of Pittsburgh, and Carnegie Mellon University (CMU),
who were already supported by the PHDA, received additional support
from Amazon Research Awards to use machine learning
techniques to study breast cancer risk, identify depression markers, and
understand what drives tumor growth, among other projects.
Accurate Machine Learning Models for Breast Cancer Screening
In work funded through the PHDA-AWS collaboration, a
research team led by Shandong Wu, an associate professor in the University of
Pittsburgh Department of Radiology, is using deep-learning systems to analyze
mammograms in order to predict the short‐term risk of developing breast
cancer. A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging are working together to develop a
more personalized approach for patients undergoing breast cancer screening.
Wu and his colleagues collected 452 de-identified normal
screening mammogram images from 226 patients, half of whom later developed
breast cancer and half of whom did not. Leveraging AWS tools, such as
they used two different machine learning models to analyze the images for
characteristics that could help predict breast cancer risk. As they reported in
the American Association of Physicists in Medicine, both
models consistently outperformed the simple measure of breast density, which
today is the primary imaging marker for breast cancer risk, The team’s
models demonstrated between 33% and 35% improvement over these existing
models, based on metrics that incorporate sensitivity and specificity.
Why It Matters
“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” said Dr. Wu. “Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective. “
Tools that could provide more accurate predictions from screening images could be used to guide clinical decision making related to the frequency of follow-up imaging and other forms of preventative monitoring. This could reduce unnecessary imaging examinations or clinical procedures, decreasing patients’ anxiety resulting from inaccurate risk assessments, and cutting costs.
Moving forward, researchers at the University of Pittsburgh
and UPMC will pursue studies with more training samples and longitudinal
imaging data to further evaluate the models. They also plan to combine deep
learning with known clinical risk factors to improve upon the ability to
diagnose and treat breast cancer earlier.
Second Project to Develop Biomarkers for Depression
In a second project, Louis-Philippe Morency, associate
professor of computer science at CMU, and Eva Szigethy, a clinical researcher
at UPMC and professor of psychiatry, medicine, and pediatrics at the University
of Pittsburgh, are developing sensing technologies that can automatically measure
subtle changes in individuals’ behavior — such as facial expressions and use of
language — that can act as biomarkers for depression.
These biomarkers will later be compared with the results of
traditional clinical assessments, allowing investigators to evaluate the
performance of their technology and make improvements where necessary. This
machine learning technology is intended to complement the ability of a
clinician to make decisions about diagnosis and treatment. The team is working with a gastrointestinal-disorder
clinic at UPMC, due to the high rate of depression observed in patients with
functional gastrointestinal disorders.
This work involves training machine learning models on tens
of thousands of examples across multiple modalities, including language (the
spoken word), acoustic (prosody), and visual (facial expressions). The
computational load is heavy, but by running experiments in parallel on multiple
GPUS AWS services have allowed the researchers to train their models in a few
days instead of weeks.
A quick and objective marker of depression could help
clinicians more efficiently assess patients at baseline, identify patients who
would otherwise go undiagnosed, and more accurately measure patients’ responses
to interventions. The team presented a paper on the work, “Integrating
Multimodal Information in Large Pretrained Transformers”, at the July 2020
meeting of the Association for Computational Linguistics.
“Depression is a disease that affects more than 17 million adults in the United States, with up to two-thirds of all depression cases are left undiagnosed and therefore untreated,” said Dr. Morency. “New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole.”
The research projects on breast cancer and depression
represent just the tip of the iceberg when it comes to the research and
insights the collaboration across PHDA and AWS will ultimately deliver to
improve patient care. Teams of researchers, health-care professionals, and
machine learning experts across the PHDA continue to make progress on key
research topics, from the risk of aneurysms and predicting how cancer cells
progress, to improving the complex electronic-health-records