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Women's Health Homepage

Remembering Dr. Nancy Cappello A patient advocate and champion of breast density awareness has passed away, but not without leaving her mark on the world

Screenings reduce risk of breast cancer death by 47-60 percent: study New research confirms what most physicians have long believed

Could a new technique make ultrasound interpretation easier? Enhanced contrast, considers intensity and duration of echoes

DBT detects 34 percent more cancers than 2D mammo Increased recall rate should not be a concern, expert says

Are women at low risk undergoing unneeded imaging due to dense breasts? Breast density notification laws may have unintended consequences

Etta Pisano American College of Radiology names chief research officer

GE to provide training to at least 140 Kenyan radiographers Partnering with Society of Radiography in Kenya

More than 20 percent of insured mammo screenings require some out-of-pocket payment Could prevent screening for lower-income women

GE launches Invenia ABUS 2.0 in US Fifty five percent more efficient in detecting breast cancer than mammography alone

New AI approach identifies recalled but benign mammograms May reduce workload by providing more accurate recall selection

A new algorithm may be just as good
as an experienced mammographer
in interpreting breast density
says a study

Is AI a match for manual interpretation of breast density?

by John R. Fischer , Staff Reporter
A new algorithm designed to measure breast density may be just as accurate as an experienced mammographer, says a new study.

Breast imagers and AI experts at Massachusetts General Hospital (MGH) and Massachusetts Institute of Technology (MIT) have devised a new approach for automatically measuring breast density in an attempt to overcome the subjective discrepancies found in manual interpretations by different clinicians, and are using it at MGH in what marks the first example of a deep-learning mechanism of its kind to be implemented in clinical practice on real patients.

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"Unfortunately, it is widely documented that radiologists' assessments of density are often inconsistent and highly subjective. Using machine computed density eliminates this inconsistency," Regina Barzilay, Delta Electronics professor of the Electrical Engineering and Computer Science Department at MIT, told HCB News.

The presence of dense breast tissue can mask tumors, preventing mammograms from detecting them and raising the risk of false negatives. Supplemental screening options, such as breast MR and ultrasound, though effective, may not be reimbursable and require expensive, out-of-pocket costs for patients.

Utilizing tens of thousands of high-quality, digital mammograms from MGH, researchers trained and tested the algorithm prior to implementing it in routine clinical practice. Eight radiologists then reviewed 10,763 findings determined by the model to be either dense or non-dense tissue, agreeing with its distinctions for 10,149 mammograms, the amount of which made up 94 percent of its total assessments.

Rejection of the other six percent, however, does not necessarily mean the algorithm was wrong when taking into consideration reader variability among radiologists. Barzilay says the next step is to develop technology that can predict future risks from images and combine those findings with those on breast density.

"While density correlates with risk, it doesn't on its own determine who is gonna get breast cancer," she said. "We are currently working on the algorithms that can predict future risk directly from images."

The researchers attribute the availability of high-quality, annotated data evaluations by radiologists and the collaborative efforts of experienced medical and computer science professionals as the key to the model’s success in clinical practice.

Approximately 16,000 images have been processed by the system since its implementation in January.

The study was published this month in the journal, Radiology.

Women's Health Homepage


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