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The Myelodysplastic Syndromes Channel on VJHemOnc is an independent medical education platform, supported with funding from Geron (Silver). Supporters have no influence on the production of content. The levels of sponsorship listed are reflective of the amount of funding given.
ASH 2024 | Machine learning predicts transformation risk and key features throughout the patient journey in MDS
Jan-Niklas Eckardt, MSc, MHBA, Technical University Dresden, Dresden, Germany, briefly comments on an abstract that studied patient features that predicted the progression of myelodysplastic syndromes (MDS) to acute myeloid leukemia (AML). The study highlighted that genetic and clinical patient features affect this progression differently, alluding to the biological heterogeneity of the disease. Machine learning models may enable monitoring of these features and predicting disease progression throughout the patient journey. This interview took place at the 66th ASH Annual Meeting and Exposition, held in San Diego, CA.
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