370P Genotype-phenotype correlations in a Japanese cohort with BRCA1/2 pathogenic variants

Background: Breast cancer(BC) remains the most commonly diagnosed malignancy among women worldwide.Early identification of high-risk genetic mutations, particularly BRCA1/2, is critical for therapeutic decision-making and risk-reduction strategies.However, access to genetic testing is limited in resource-constrained settings due to cost and logistical barriers.We aim to develop a non-invasive, AIbased approach to predict breast cancer-associated genetic mutations using digital mammography (DM). Methods:We developed an AI-driven framework using convolutional neural networks (CNNs), pre-trained on the Mini-DDSM dataset to learn foundational imaging features and subsequently fine-tuned on a single-center clinical cohort of 100 breast cancer patients (50 with pathogenic mutations and 50 with negative genetic testing).Multiple deep learning architectures (DenseNet-121, ResNet-50, MobileNetV2, InceptionV3, and custom CNN) were evaluated.Ensemble strategies (stacking and weighted averaging) were employed to improve predictive performance.Explainability was assessed using class activation mapping (CAM)-based techniques, and predictive uncertainty quantified using Monte Carlo dropout.Results: DenseNet-121 achieved the highest individual model accuracy (73.9%).Ensembles further improved performance, with stacking achieving 78.4% accuracy and weighted averaging demonstrating the best generalizability (accuracy 82.6%, F1score 0.83).Explainability analyses consistently highlighted clinically relevant mammographic regions.Genotype-phenotype correlations were observed: nonmutated patients mostly exhibited luminal tumors, BRCA1 carriers were younger with high-grade triple-negative disease, and BRCA2/non-BRCA mutation carriers largely demonstrated intermediate-grade luminal type BC. Conclusions:This study demonstrates the feasibility of using AI-based analysis of DM to identify mutation-associated imaging patterns in BC.While not a substitute for genetic testing, this approach may serve as a cost-effective, non-invasive triaging tool to prioritize patients for genetic evaluation, in settings with limited access to molecular testing.