Pittsburgh Health Data Alliance, AWS Develop AI Models for Breast Cancer Screening, Depression
In work funded through an alliance between the Pittsburgh Health Data Alliance (PHDA) and Amazon Web Services (AWS), a research team is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer.
The project builds on an collaboration that commenced last year between PHDA and AWS, with the goal to advance innovation in areas such as cancer diagnostics, precision medicine, electronic health records (EHRs), and medical imaging. 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, have 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.
More recently, a team of experts in computer vision, deep learning, bioinformatics, and breast cancer imaging has been working together to develop a more personalized approach for patients undergoing breast cancer screening. The initiative is being led by Shandong Wu, Ph.D., an associate professor in the University of Pittsburgh Department of Radiology.
For the research, 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 Amazon SageMaker, 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 percent and 35 percent improvement over these existing models, based on metrics that incorporate sensitivity and specificity, according to researchers.
“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,” Wu said. “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. “
According to the researchers, tools that could provide more accurate predictions from screening images could be used to guide clinical decision making related to 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, they contend.
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, officials said.
A separate project under this collaboration entails researchers 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, researchers explained. 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, they added.
These 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,” officials attest.
