Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients' molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.

Automated summary

Myelodysplastic syndromes (MDS) have variable clinical manifestations and prognoses. Prognostic systems have been developed to categorize MDS patients into different risk groups based on clinical factors and cytogenetic abnormalities. Incorporating molecular features into these systems may enhance their prognostic power. Machine learning algorithms can help develop precise prognostication models by integrating complex genomic interactions. This review highlights current prognostic models used in MDS and the latest achievements in machine learning-based research.