Mammography would be more accurate and comfortable for patients with mammographic pressure standardization.
Mammographic pressure standardization could help reduce the current wide range of applied forces and pressures, and lessen discomfort for the patient while increasing accuracy, according to a study published in the European Journal of Radiology.
Researchers from the Netherlands undertook a retrospective study to compare current compression practice among different imaging sites in the Netherlands and U.S. They sought to determine if there could be improvement in the compression protocols as an objective mechanical parameter.
The parameters of 37,518 mammographic compressions (9,188 women in the Netherlands) and 7,171 compressions (1,851 women in the U.S.) were studied. The researchers assessed the applied average force, pressure, breast thickness, breast volume, breast density, and average glandular dose (AGD) as a function of the size of the contact area between the breast and the paddle.
The results showed that the Dutch data set had, on average, significantly higher applied forces and than did the U.S. data set. The relative standard deviation was larger in the U.S. data set than in the NL data set.
“Breasts were compressed with a force in the high range of greater than 15 daN for 31.1 percent and greater than 20 kPa for 12.3 percent of the NL data set versus, respectively, 1.5 percent and 1.7 percent of the US data set,” the authors wrote. “In the low range we encountered compressions with a pressure of less than 5 daN for 21.1 percent and less than 5 kPa for 21.7 percent of the US data set versus, respectively, 0.05 percent and 0.6 percent in the NL data set. Both the average and the standard deviation of the AGD were higher in the US data set.”
The researchers concluded that there was a wide range of applied forces and pressures used to perform mammography, and that pressure standardization would could decrease variation, improve reproducibility, and reduce the risk of unnecessary pain, unnecessary high radiation doses, and inadequate image quality.
Could a Deep Learning Model for Mammography Improve Prediction of DCIS and Invasive Breast Cancer?
April 15th 2024Artificial intelligence (AI) assessment of mammography images may significantly enhance the prediction of invasive breast cancer and ductal carcinoma in situ (DCIS) in women with breast cancer, according to new research presented at the Society for Breast Imaging (SBI) conference.
Mammography-Based AI Abnormality Scoring May Improve Prediction of Invasive Upgrade of DCIS
April 9th 2024Emerging research suggests that an artificial intelligence (AI) score of 75 or greater for mammography abnormalities more than doubles the likelihood of invasive upgrade of ductal carcinoma in situ (DCIS) diagnosed with percutaneous biopsy.
Mammography Study: AI Improves Breast Cancer Detection and Reduces Reading Time with DBT
April 3rd 2024An emerging artificial intelligence (AI) model demonstrated more than 12 percent higher specificity and reduced image reading time by nearly six seconds in comparison to unassisted radiologist interpretation of digital breast tomosynthesis (DBT) images.