PCGMMF: a prediction method for breast cancer prognostic recurrence and metastasis risk based on enhanced multimodal feature fusion

Abstract

Background

Breast cancer is a highly heterogeneous disease with high morbidity and mortality rates. Despite the availability of various treatments, a significant number of patients still face a high probability of recurrence or metastasis, which severely impacts their survival status. Traditional prognostic methods based on single-modality data and machine learning algorithms often fail to adequately capture the complex biological relationships and heterogeneous characteristics of breast cancer, leading to suboptimal prognostic performance. Therefore, there is an urgent need for a more accurate and effective method to predict the risk of recurrence and metastasis in breast cancer prognosis.

Methods

In this study, we propose a novel method termed PCGMMF for breast cancer prognostic analysis. This method integrates histopathological images, clinical data, gene expression data, and DNA methylation data through multimodal fusion. We leverage a pre-trained Vision-LSTM model based on transfer learning to extract features from histopathological images. Additionally, we design a comprehensive feature selection strategy that includes support vector machine (SVM), Mantel test, and correlation analysis to filter features from gene expression data and DNA methylation data. Furthermore, to address the high heterogeneity of breast cancer and the independence and intersectionality of multimodal features, we propose a bidirectional attention and self-attention based enhanced multimodal feature fusion module called BSAMF.

Results

Through a series of experiments, we evaluate the performance of PCGMMF. When predicting the recurrence and metastasis risk of breast cancer prognosis, PCGMMF achieves an accuracy of 0.903 and an AUC value of 0.924, outperforming other state-of-the-art methods. Furthermore, we provide an interpretability analysis of highly significant regions from histopathological images, which can serve as a reference for clinical practice.

Conclusion

PCGMMF offers a robust and innovative solution for breast cancer prognostic analysis by effectively integrating multimodal data and utilizing advanced deep learning techniques. It can effectively conduct breast cancer prognostic analysis and provide significant references for personalized precision treatment and clinical practice.