NEW YORK (Reuters Health) – A prediction model that assesses clinical characteristics as well as imaging findings may help reduce the false-positive rate in women with extremely dense breasts who undergo additional breast cancer screening with MRI, a secondary analysis of the DENSE trial suggests.
The DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial evaluated the effectiveness of screening with mammography plus MRI compared to mammography alone in Dutch breast cancer screening participants ages 50 to 75 with extremely dense breasts.
“In the first round of the DENSE trial the false positive rate was 79.8 per 1,000 screenings,” Dr. Bianca den Dekker of the University Medical Center Utrecht in Utrecht, the Netherlands, told Reuters Health in an email on behalf of the authors. “These false-positive recalls lead to additional work-up, including repeat MRI scans, targeted ultrasound, and (MR-guided) biopsy.”
“We were positively surprised when we discovered that our full prediction model, based on all collected clinical characteristics and MRI findings, could have prevented 45.5% of false-positive recalls and 21.3% of benign biopsies, without missing any cancers,” she said. “This brings supplemental screening MRI for women with dense breasts one step closer to implementation.”
As reported in Radiology, Dr. den Dekker and colleagues built prediction models based on data from the DENSE trial using multivariable logistic regression analysis to distinguish true-positive MRI findings from false-positive ones.
Among 454 women (median age, 52) with a positive MRI result in an initial supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results.
As Dr. den Dekker indicated, the full prediction model (area under the receiver operating characteristics curve, 0.88), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% of the false-positive recalls and 21.3% of benign biopsies without missing any cancers.
Further, the model based solely on MRI findings and age had a comparable performance (AUC, 0.84) and could have prevented 35.5% of false-positive MRI screening results and 13.0% of benign biopsies.
Dr. den Dekker said, “The next step is to perform validation studies using data from different populations. In addition, we are interested in the performance of prediction models in subsequent screening rounds.”
“The false-positive rate was sharply reduced in the second round compared with the first round,” she said. “This can be partly explained by the availability of prior MRI examinations, which allows comparison for interval change. As incident screening rounds have a much lower false-positive rate, separate models may have to be created.”
Dr. Kate Lampen-Sachar, a breast radiologist at Miami Cancer Institute, part of Baptist Health South Florida, commented in an email to Reuters Health, “We are very lucky at our Institute that implementing this tool would be relatively simple because we already acquire all of the patient characteristics that go into the authors’ prediction model.”
“Breast MRI is so exciting and so incredibly impactful, but it is not utilized to its fullest potential because of so many factors: cost, access, time, contrast, lack of awareness of its uses/availability, to name a few,” she said. “Additionally, many institutions don’t acquire all of the patient characteristics needed for the proposed model.”
Notably, she added, “this paper focused only on women with…extremely dense breast tissue. Does this model hold up for women with mammographic heterogeneously dense or scattered fibroglandular tissue?”
“Unfortunately, breast MRI is not available or affordable in many countries and even in the U.S. for so many women,” she said. “In order to implement a model such as this on a wide-scale basis, we need to first make breast MRI affordable, accessible, and available to those that need it. We need to raise awareness of the utility of MRI in high-risk patients.”
SOURCE: https://bit.ly/3mhwQhN Radiology, online August 17, 2021.
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