Acute Leukemia (LAML) and Artificial Intelligence: Pre-dictive Technologies for Prognosis, Recurrence and Re-sponse to Treatment

This study focuses on the integration of clinical, genomic, and epigenomic data to develop advanced predictive models for risk stratification in acute myeloid leukemia (AML). AML is a hematological disease characterized by the uncontrolled proliferation of immature cells, with genetic mutations and chromosomal alterations that influence prognosis and treatment. The primary aim of the project is to enhance the prediction of survival, therapeutic response, and relapse likelihood using an innovative approach based on machine learning and the integration of multiple data sources, including RNA-seq, DNA methylation, and genetic mutations. The work explores various data fusion techniques, including early and late fusion, as well as the use of explainability methods such as SHAP and Grad-CAM to improve transparency and reliability of clinical decision-making. The data comes from five datasets within the TCGA-AML project, which include clinical, gene expression, and mutation information. The proposed multimodal approach aims to develop robust, personalized, and biologically interpretable predictive models, with the goal of optimizing treatments and reducing the risk of relapse. This approach could have implications not only for AML but also for other types of leukemia and cancers, enhancing the overall management and prognosis of oncology patients.