Performance of an image-only deep learning breast cancer risk model with the addition of a polygenic risk score.
Academic Article
Overview
abstract
BACKGROUND: Mammograms contain imaging biomarkers that can predict future breast cancer risk using deep learning (DL) models. We evaluated whether adding a polygenic risk score (PRS) improves performance of the image-only DL breast cancer risk model Mirai. METHODS: This nested case-control study within the Nurses' Health Study 2 included 902 women (270 cases, 632 controls) who underwent bilateral 2D digital screening mammography between 2001-2017. Risk was assessed using Mirai and, for clinical comparison, the Gail 5-year model. A PRS was calculated using 313 breast cancer-associated single-nucleotide polymorphisms. The primary outcome was incident breast cancer within five years of the index mammogram. Discrimination was evaluated using area under the receiver operating characteristic curve (AUC), with comparisons using the DeLong test. RESULTS: Mean age was 55.5 years(SD 5.3). Among cases, median time from index mammogram to diagnosis was 2.0 years (IQR0.5-4.0). Mirai alone achieved an AUC of 0.66 (95% CI: 0.62-0.70), increasing to 0.73 (95% CI 0.67-0.78; P = 0.05) with PRS. The Gail model improved from 0.52 (95% CI: 0.47-0.57) to 0.69 (95% CI: 0.62-0.76; P < 0.001) with PRS. Mirai+PRS significantly outperformed Gail+PRS (P < 0.001). CONCLUSIONS: Integrating PRS with DL-based mammographic models modestly improves risk discrimination and may enhance personalized screening.
