Model Predicts False-Positives in Breast Cancer MRI Screen

TUESDAY, Aug. 17, 2021 (HealthDay News) — For women with extremely dense breasts, prediction models based on magnetic resonance imaging (MRI) findings can reduce the false-positive first-round screening MRI rate, according to a study published online Aug. 17 in Radiology.

Bianca M. den Dekker, M.D., from Utrecht University in the Netherlands, and colleagues used data from a randomized clinical trial that prospectively collected clinical characteristics and MRI findings in women with extremely dense breasts who had positive first-round MRI screening results after a normal screening mammography. In this secondary analysis, prediction models were built to distinguish true-positive from false-positive MRI screening findings.

The researchers found that 79 of the 454 women with a positive MRI result in a first supplemental MRI screening round were diagnosed with breast cancer (true-positives) and 375 had false-positive results. The full prediction model based on all collected clinical characteristics and MRI findings could have prevented 45.5 percent of false-positive recalls and 21.3 percent of benign biopsies, without missing any cancers (area under the receiver operating characteristic curve [AUC], 0.88). Comparable performance (AUC, 0.84) was seen for a model solely based on readily available MRI findings and age; this model could have prevented 35.5 and 13.0 percent of false-positive recalls and benign biopsies, respectively.

“Our prediction models may identify a substantial number of false-positives after first-round supplemental MRI screenings, reducing false-positive recalls and benign biopsies without missing any cancers,” den Dekker said in a statement. “This brings supplemental screening MRI for women with dense breasts one step closer to implementation.”

Several authors disclosed financial ties to biopharmaceutical companies, including Bayer Pharmaceuticals, which partially funded the study.

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