Myelodysplastic Syndromes (MDS)
Following in the footsteps of acute myeloid leukemia: are we witnessing the start of a therapeutic revolution for higher-risk myelodysplastic syndromes?
For most patients with higher-risk myelodysplastic syndromes myelodysplastic syndromes: (my-eh-lo-diss-PLASS-tik SIN-dromez)
The IPSS-R more accurately captures fatigue severity of newly diagnosed patients with myelodysplastic syndromes compared with the IPSS index
We aimed to compare fatigue of newly diagnosed patients with myelodysplastic syndromes myelodysplastic syndromes: (my-eh-lo-d
Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes
Morphological interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes make clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine learning technique devised to identify patterns of co-occurrence among morphologic features and genomic events.
Amit K. Verma, M.B.B.S.
Institution
Albert Einstein College of Medicine
Physician Status
available for consultation
Primary Disease Area of Focus
Myelodysplastic Syndromes (MDS)
About
Amit K. Verma, MBBS, is a Professor in the Departments of Medicine (Oncology) and Development & Molecular Biology at Albert Einstein School of Medicine. Dr. Verma is the Director of the Division of Hemato-Oncology, Montefiore Department of Oncology. His primary area of research is in MDS.