This project focuses on improving the understanding and management of Myelodysplastic Syndromes (MDS), a group of bone marrow disorders frequently affecting elderly patients. These conditions are characterized by complex genetic and chromosomal abnormalities, making diagnosis and prognosis challenging.
The innovation lies in leveraging advanced artificial intelligence (AI) techniques to analyze omics data—detailed genetic and molecular information—combined with patient clinical data. Through machine learning algorithms, the research identified eight distinct subgroups of MDS, each defined by specific genomic and clinical traits, such as RNA splicing gene mutations influencing disease progression and prognosis.
The project also developed a novel prognostic model that integrates 63 clinical and genomic variables to provide personalized survival predictions. This model outperforms existing prognostic tools in predictive accuracy. To ensure clinical accessibility, a web portal was created, enabling physicians to input patient-specific characteristics and receive tailored disease outcome predictions.
This initiative represents a significant advancement in precision medicine for MDS. By harnessing the power of AI and omics data, the project enhances understanding of age-related impacts on clinical outcomes and supports the development of more effective and personalized therapeutic strategies.