A machine-learning model found that background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be an indicator of breast cancer risk in patients with extremely dense breasts, according to a study published by Wang et al in Radiology.
Patients with extremely dense breasts are at a three- to six-times higher risk of developing breast cancer compared to patients who have fatty breasts. Since mammography is less sensitive in detecting early-stage breast cancer in patients with dense breasts, patients between the ages of 50 and 75 years with dense breasts may benefit from additional MRI screening.
Another breast cancer risk factor is BPE, which is the level of normal fibroglandular tissue that enhances on breast MRI. However, not much is known about how BPE compares to other, more established clinical risk factors associated with breast cancer, such as age, body mass index (BMI), family history, and breast density.
"Thus far, studies on breast cancer risk factors have typically focused on [patients] at high lifetime risk of developing breast cancer," said study coauthor Kenneth G. A. Gilhuijs, PhD, of the Department of Radiology at the University Medical Center Utrecht in the Netherlands. "This is the first study that we know of that demonstrates an association between BPE and occurrence of breast cancer in [patients] with extremely dense breasts."
Study Details and Role of BPE
To determine how much BPE is an indicator of breast cancer risk, the researchers used dynamic contrast-enhanced MRI exams from 4,553 participants in the Dense Tissue and Early Breast Neoplasm Screening (DENSE) Trial, a large, multi-institutional study based in the Netherlands, to develop a deep learning model to automatically identify fibroglandular tissue. The MRI scans were performed every 2 years in eight hospitals in the Netherlands between December 2011 and January 2016.
After adjusting for age, BMI, and BPE, the researchers found that breast cancer occurrence was greater in patients with higher volumes of enhancing parenchyma compared to those with low volumes of enhancing parenchyma.
Of the 4,553 patients included in the study, 122 were diagnosed with breast cancer. Roughly 63% of them were diagnosed after the first round of screening. An average cancer detection time of 24 months was associated with the remaining patients diagnosed with breast cancer.
"Parenchyma does not enhance uniformly on MRI," Dr. Gilhuijs said. "This method calculates all the different subvolumes at which the parenchyma enhances and sorts them from high to low."
The researchers pointed out that while the implementation of supplemental MRI screening in patients with dense breasts will result in fewer interval cancers, it will also further strain radiologist workloads. Developing more personalized strategies to deal with the added number of screenings may help alleviate the strain on the health-care field.
"Our study is a first step in a direction to further tailor the frequency of supplemental MRI screening to individual [patients] with dense breasts, focusing not only on breast density as a main risk factor but also on other properties of the breast established from a first screening MRI," Dr. Gilhuijs concluded.
Disclosure: For full disclosures of the study authors, visit pubs.rsna.org.
