The Integration of Artificially Intelligent Technologies With Breast Imaging

Mary Beth Massat

Disclosures

Appl Radiol. 2019;48(5):32-36. 

In This Article

Abstract and Introduction

Introduction

Radiomics and deep learning (DL) tools are making inroads into breast imaging. Several DL-infused breast screening and imaging technologies have recently received FDA 510(k) clearance, with more in the pipeline, while studies are demonstrating the value and potential superiority of deep learning compared to conventional techniques and models.

For example, a study in Radiology recently reported a new deep learning tool developed by researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital that is able to better predict future risk of breast cancer in women compared to the Tyrer-Cuzick model, a widely available and well-studied model.[1] The DL tool performed equally well across women of different ages, races, and family histories.

In a statement released by RSNA, the study's lead author Adam Yala, a PhD candidate at MIT, says, "There's a very large amount of information in a full-resolution mammogram that breast cancer risk models have not been able to use until recently. Using deep learning, we can learn to leverage that information directly from the data and create models that are significantly more accurate across diverse populations."

Radiomics, which extracts mineable quantitative data from medical images for decision support, was central to a study by researchers from the University of Chicago, University of California San Francisco (UCSF), and the H. Lee Moffit Cancer Center and Research Institute. The study investigated combining mammography radiomics with a novel quantitative breast imaging technique based on dual-energy mammography to reduce false-positive biopsies. This technique, called three-compartment breast (3CB) imaging, was developed at UCSF by a team led by John Shepherd, PhD (now a professor at the University of Hawai`i Cancer Center). It captures a typical diagnostic mammogram plus an additional high-energy mammography image with a small in-image phantom to derive the water, lipid, and protein tissue composition throughout the entire imaged breast. These tissue composition maps can be visualized as images and provide biological water-lipid-protein signatures of suspicious findings as well as of normal breast parenchyma.

Figure 1.

ProFound AI rapidly and accurately analyzes each DBT image, or slice, and provides radiologists with crucial information, such as Certainty of Finding lesion and Case Scores, which can assist in clinical decision making and prioritizing caseloads.

Karen Drukker, PhD, research associate professor at the University of Chicago, led the study that examined mammograms and 3CB images from 109 women with suspicious breast masses that were all biopsied. Of these 109 suspicious masses, only 35 were cancerous and the remaining 74 represented 'unnecessary' benign breast biopsies. Their mammograms were analyzed with a radiomics method developed by Maryellen L. Giger, PhD, and Dr. Drukker's team, and the water-lipid-protein signatures were obtained from the corresponding 3CB images. Dr. Giger is a co-founder of Quantitative Insights, now Qlarity Imaging (Chicago, IL), which incorporated a similar radiomics technology for MRI into QuantX, the industry's first FDA-cleared computer-aided diagnosis platform incorporating machine learning for the evaluation of breast abnormalities in breast MRI studies.

Drukker et al found that the combination of mammography radiomics and 3CB image analysis would have improved the positive predictive value of biopsy from 32 percent (35 of the 109 breast masses) for visual interpretation alone, to nearly 50 percent, resulting in 39 fewer 'unnecessary' benign biopsies or a 36 percent overall reduction in biopsies. The technique had a 97 percent sensitivity rate, missing one of the 35 cancers.[2]

"Most abnormal findings on screening and diagnostic mammograms are benign," Dr. Drukker explains. "This is an imaging technique that also looks at the biology of the lesion to help facilitate better decisions by the clinicians. By combining 3CB image analysis with mammography radiomics, the potential reduction in recall was substantial."

Although the approach and the 3CB technique are experimental, Dr. Drukker and her colleagues are also planning to study the technique on digital breast tomosynthesis (DBT) images in the near future. She is also interested in looking at the technique to examine other breast findings that may represent cancer, such as microcalcifications and architectural distortions.

If commercialized, the 3CB technique could be used with any full-field digital mammography system and would not require any new equipment, just some small modifications, including the addition of a mechanism that can pull the small in-image phantom into place, Dr. Drukker says. Although the study was focused on diagnostic mammograms, it could also potentially be used for screening mammograms after further investigation.

While emerging medical imaging artificial intelligence (AI) and machine learning (ML) technologies and results of studies published in peer-reviewed journals appear promising, many still need to be validated on sufficiently large external independent datasets (such as those provided in the Cancer Imaging Archive, https://www.cancerimagingarchive.net/), Dr. Drukker adds. Moreover, attaining promising stand-alone performance is a necessary first step for any computerized analysis method, but reader studies are needed to assess their impact on human performance.

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