A Breast Cancer Prognosis Prediction Model Based on Cross-Modal Contrastive Learning

Abstract

Breast cancer is a common malignant tumor that poses a serious threat to women's health. The incidence and mortality rates of breast cancer have shown an increasing trend worldwide in recent years; therefore, an accurate assessment of breast cancer prognosis is crucial for the development of individualized treatment plans and for the improvement of survival quality of patients. The traditional prognosis assessment of breast cancer mainly depended on doctors' clinical experience and multidisciplinary comprehensive judgment, which lacks unified objective evaluation criteria. This study proposes an innovative cross-modal contrastive learning model PreGAT based on graph neural networks and attention mechanism. The proposed model can efficiently integrate features from multiple sources of patient data, including clinical features and constructed graph structure features, and significantly improve the performance of the model through a novel contrastive learning loss function. The PreGAT model achieves excellent performance on the public METABRIC dataset with an average accuracy of 92.9% and an AUC value of 0.969. This research provides a promising technique for breast cancer prognosis prediction in clinical practice, which can provide more accurate and reliable decision support for the development of precise treatment programs.