10th International Congress on Information and Communication Technology

Authors - Ainhoa Osa-Sanchez, Paulina Carcamo Ibarra, Begonya Garcia Zapirain
Abstract - Breast cancer remains one of the most diagnosed cancers and a leading cause of cancer-related mortality worldwide. Advances in predictive modeling have introduced innovative methods to improve breast cancer prognosis and recurrence prediction, particularly through the integration of clinical, radiomic, and temporal data. This study focuses on the application of advanced feature selection techniques and machine learning algorithms, including Random Forest, XGBoost, and Lasso Regression, to optimize the performance and interpretability of predictive models. Radiomic features, such as the median of intensity histogram and the difference entropy of the grey level co-occurrence matrix (GLCM), alongside clinical and temporal variables, were identified as key predictors of recurrence. Our findings underscore the potential of combining multimodal data with robust feature selection techniques to enhance personalized treatment strategies for HER2-positive breast cancer patients. Future research should address dataset generalizability and incorporate multi-omics data to further refine these predictive approaches.