Optimizing Breast Cancer Recurrence Time Prediction with Attention-Enhanced LSTM Networks

Abstract

Breast cancer recurrence prediction remains a critical challenge in oncological surveillance and personalized treatment planning. Accurate temporal modeling of recurrence risk requires sophisticated handling of longitudinal patient data characterized by irregular time intervals, missing values, and complex temporal dependencies. This study presents an attention-enhanced Long Short-Term Memory (LSTM) framework optimized for breast cancer recurrence time prediction. The proposed architecture integrates multi-head self-attention mechanisms with bidirectional LSTM networks to capture critical prognostic temporal patterns from electronic health records. We incorporate time-aware position encoding and specialized loss functions combining survival analysis metrics with cross-entropy objectives. Comprehensive experiments on longitudinal breast cancer datasets demonstrate superior predictive performance compared to traditional Cox proportional hazards models and standard recurrent neural networks. The attention mechanism successfully identifies key temporal biomarker changes and clinical events contributing to recurrence risk, achieving an overall C-index of 0.891. Ablation studies confirm the substantial contribution of attention-based temporal modeling, with improvements of 7.3% in time-dependent AUC over baseline LSTM architectures. The interpretable attention weights provide clinically actionable insights for oncologists in developing personalized surveillance strategies. This research advances temporal deep learning methodologies for cancer prognosis and establishes a foundation for AI-driven recurrence monitoring systems in clinical practice.