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WEDNESDAY, Dec. 18, 2019 (HealthDay News) — A deep learning (DL) model can predict which women are at risk for subsequent development of breast cancer, with higher accuracy than density-based models, according to a study published online Dec. 17 in Radiology<\/i>.<\/p>\n
Karin Dembrower, M.D., from Karolinska Institutet in Stockholm, and colleagues developed a risk score associated with future breast cancer and compared it with density-based models in a retrospective study. Network development was based on cases diagnosed from 2008 to 2012; for each woman, the DL risk score, dense area, and percentage density were calculated for the earliest available digital mammogram.<\/p>\n
A total of 2,283 women were assessed, 278 of whom were subsequently diagnosed with breast cancer. The researchers found that compared to those without a breast cancer diagnosis, women with breast cancer had higher age at mammography (mean, 55.7 versus 54.6 years), dense area (mean, 38.2 versus 34.2 cm\u00b2), and percentage density (mean, 25.6 versus 24.0 percent). Compared with dense area and percentage density, age-adjusted DL risk score had higher odds ratios (1.56, 1.31, and 1.18, respectively) and areas under the receiver operating characteristic curves (0.65, 0.60, and 0.57, respectively). The false-negative rate was lower for age-adjusted DL score (31 percent versus 36 and 39 percent, respectively); for more aggressive cancers, this difference was most pronounced.<\/p>\n
“The deep neural network overall was better than density-based models,” Dembrower said in a statement. “And it did not have the same bias as the density-based model. Its predictive accuracy was not negatively affected by more aggressive cancer subtypes.”<\/p>\n