Publications by authors named "Maria-Teresa Cedena-Romero"
Article Synopsis
- * The study validated the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS), showing it was better than traditional scoring systems in predicting overall and leukemia-free survival in two different patient groups from Spain and Taiwan.
- * The strong performance of AIPSS-MDS suggests it could be a key tool for tailoring treatment in CMML, and future research should look into adding genetic data to enhance risk assessment and patient care.
Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions.
View Article and Find Full Text PDFIntroduction: Congenital dyserythropoietic anemias (CDA) are characterized by hyporegenerative anemia with inadequate reticulocyte values, ineffective erythropoiesis, and hemolysis. Distinctive morphology of bone marrow erythroblasts and identification of causative genes allow classification into 4 types caused by variants in CDAN1, c15orf41, SEC23B, KIF23, and KLF1 genes.
Objective: Identify pathogenic variants in CDA patients.