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Researchers aim to improve breast cancer detection

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Researchers at The Johns Hopkins Applied Physics Laboratory (APL), in Laurel, are collaborating with radiologists from Johns Hopkins Medicine (JHM) to develop an upstream data fusion (UDF) method  ̶  leveraging artificial intelligence  ̶  that promises to significantly improve the accuracy of breast cancer screening.

APL’s David Porter is the lead author of the paper, and Dr. Lisa Mullen, of JHM, is the principal investigator of the project. The research team also includes Michael Williams, a mathematician in APL’s Force Projection Sector; William Walton, an electrical engineer in APL’s Asymmetric Operations Sector; Dr. Susan Harvey (formerly of JHM); and Keith Peyton, a defense consultant.

Annual mammography screening has been shown to decrease breast cancer mortality rates by as much as 38 percent. There are two widely used examinations: full field digital mammography (FFDM) and digital breast tomosynthesis (DBT), also known as 3D mammography. FFDM consists of two images of each breast. With DBT, multiple projections of each breast are obtained and then reconstructed into 1-millimeter slices.

A major, general limitation of mammography is the overlap of normal breast tissue, which obscures lesions. But the 1-millimeter slices generated by DBT decrease the superimposition of tissue, thereby improving the detection of lesions and increasing the accuracy of the diagnosis.

Despite DBT’s benefits and performance, the IWBI paper indicates that challenges and limitations still exist. For one, breast imaging radiologists can be overwhelmed by the sheer volume of studies associated with widespread screening programs, a problem made worse by DBT, which produces 200 to 400 images per study compared to only four images per FFDM study. Additionally, the task of interpreting mammography is difficult, with inter- and intra-reader variability as high as 30%.

The authors point out that both screening methods have been known to generate false positives – with associated costs of up to $4 billion annually in the United States – and are limited in their ability to clearly detect invasive cancers in dense breast tissue. Even when the screening is supplemented with ultrasound, false-positive diagnoses still occur.

The paper presents an innovative approach to this critical problem, leveraging UDF to combine the strengths of these complementary breast imaging modalities. UDF refers to the processing, exploitation and fusion of sensor data as close to the raw sensor data feed as possible. APL has been pioneering UDF applications since the early 2000s.

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