Prediction models based on clinical characteristics and imaging findings may help reduce the false-positive rate in women with dense breasts who undergo supplemental breast cancer screening with MRI, according to a new study in the journal Radiology.
Women with dense breast tissue have a much higher risk of breast cancer compared to those with average breast density. High breast density also markedly reduces the sensitivity of mammography due to the masking effect of the fibroglandular tissue, meaning that cancers can be hidden within dense breast tissue.
For these reasons, breast MRI is considered a potentially useful supplement to mammography screening in women with dense breast tissue. It is the most sensitive imaging technique for diagnosing breast cancer and can differentiate well between lesions and abnormalities of the breast. Research has confirmed its substantial added value as a screening tool for women at high risk of breast cancer.
However, the high sensitivity that makes MRI an excellent screening tool also means it often detects benign lesions that otherwise would have gone unnoticed. Women who get recalled for additional work-up based on these findings potentially face repeat MRI scans, targeted ultrasound, and biopsy. The need for additional investigations may cause anxiety in the patient, increase health care costs or lead to biopsy-related complications.
In the new study, Dr. den Dekker and colleagues developed prediction models to distinguish true-positive MRI screening from false-positives. To create the models, they combined MRI findings with clinical characteristics like body mass index, family history of breast cancer, and use of hormone replacement therapy.