Multimodal adversarial representation learning for breast cancer prognosis prediction

With the increasing incidence of breast cancer, accurate prognosis prediction of breast cancer patients is a key issue in current cancer research, and it is also of great significance for patients' psychological rehabilitation and assisting clinical decision-making. Many studies that integrate data from different heterogeneous modalities such as gene expression profile, clinical data, and copy number alteration, have achieved greater success than those with only one modality in prognostic prediction. However, many of these approaches that exist fail to dramatically reduce the modality gap by aligning multimodal distributions. Therefore, it is crucial to develop a method that fully considers a modality-invariant embedding space to effectively integrate multimodal data. In this study, to reduce the modality gap, we propose a multimodal data adversarial representation framework (MDAR) to reduce the modal heterogeneity by translating source modalities into distributions for the target modality. Additionally, we apply reconstruction and classification losses to embedding space to further constrain it. Then, we design a multi-scale bilinear convolutional neural network (MS-B-CNN) for uni-modality to improve the feature expression ability. In addition, the embedding space generates predictions as stacked feature inputs to the extremely randomized trees classifier. With 10-fold cross-validation, our results show that the proposed adversarial representation learning improves prognostic performance. A comparative study of this method and other existing methods on the METABRIC (1980 patients) dataset showed that Matthews correlation coefficient (Mcc) was significantly enhanced by 7.4% in the prognosis prediction of breast cancer patients.