CAGCL: Predicting Short- and Long-Term Breast Cancer Survival with Cross-Modal Attention and Graph Contrastive Learning.

In breast cancer treatment, accurately predicting how long a patient will survive is crucial for decision-making. This information guides treatment choices and supports patients' psychological recovery. To address this challenge, we introduce a novel predictive model to forecast breast cancer prognosis by leveraging diverse data sources, including clinical records, copy number variation, gene expressions, DNA methylation, microRNA sequencing, and whole slide image data from the TCGA Database. The methodology incorporates graph contrastive learning with cross-modality attention (CAGCL), considering all possible combinations of the six distinct data modalities. Feature embeddings are enhanced through graph contrastive learning, which identifies subtle differences and similarities among samples. Further, to learn the complementary nature of information across multiple data modalities, a cross-attention framework is proposed and applied to the graph contrastive learning-based extracted features from various data sources for breast cancer survival prediction. It performs a binary classification to anticipate the likelihood of short- and long-term breast cancer survivors, delineated by a five-year threshold. The proposed model (CAGCL) showcases superior performance compared to baseline models and other state-of-the-art models. The model attains an accuracy of 0.932, a sensitivity of 0.954, a precision of 0.958, an F1 score of 0.956, and an AUC of 0.948, underscoring its effectiveness in predicting breast cancer survival.

Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral.

Breast cancer is the most prevalent kind of cancer among women and there is a need for a reliable algorithm to predict its prognosis. Previous studies focused on using gene expression data to build predictive models. However, recent advancements have made multi-omics cancer data sets (gene expression, copy number alteration, etc.) accessible. This has acted as the motivation for the creation of a novel model that utilizes a graph convolutional network (GCN) and Choquet fuzzy ensemble, incorporating multi-omics and clinical data retrieved from the publicly available METABRIC Database. In this study, graphs have been used to extract structural information, and a Choquet Fuzzy Ensemble with Logistic Regression, Random Forest, and Support Vector Machine as base classifiers has been employed to classify breast cancer patients as short-term or long-term survivors. The model has been run using all possible combinations of gene expression, copy number alteration, and clinical modality, and the results have been reported. Furthermore, a comparison has been made between the obtained results and different baseline models and state-of-the-art to demonstrate the efficacy of the proposed model in terms of different metrics. The results of this model based on Accuracy, Matthews correlation coefficient, Precision, Sensitivity, Specificity, Balanced Accuracy, and F1-Measure are 0.820, 0.528, 0.630, 0.666, 0.871, 0.769, and 0.647, respectively.