Abstract
Recent advancements in genome characterization have transformed the study of myelodysplastic syndromes (MDS). Accordingly, there has been a shift from traditional classification and prognostication methods, which relied mainly on morphological and clinical data, to next-generation systems that incorporate genomic features. However, genetic abnormalities account for only part of the overall risk related to survival, disease progression, and individual response to hypomethylating agents (HMA), indicating that a significant portion of these risks is still tied to clinical and non-mutational factors. Increasing evidence suggests that transcriptomics, immune dysfunctions, and high-dimensional tumor morphology data extracted by Artificial Intelligence (AI) may play a crucial role in predicting clinical outcomes in human cancers, thereby improving the implementation of personalized medicine programs.
In this scenario, we developed MEGAERA, an innovative, deep learning-based framework for multimodal analysis of hematological malignancies. MEGAERA integrates clinical, multi-omics, and histopathological data, using specific strategies to ensure full clinical explainability and interpretability of predictions. This study was conducted by the GenoMed4All and Synthema EU consortia, with MDS included as use case, to improve personalized predictions of patient outcomes.