Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes.

Fecha de publicación: 01/10/2023 Fecha Ahead of Print: 11/10/2023

Autores de IIS La Fe

• Elvira Mora Castera

  Autor

• Fuencisla Ramos Fortea

  Autor

• 

  María Ángeles Tormo Mas

  Autor

• Miguel Couselo Jerez

  Autor

Participantes ajenos a IIS La Fe

• Mosquera Orgueira, Adrian
• Perez Encinas, Manuel Mateo
• Diaz Varela, Nicolas A.
• Diaz-Beya, Marina
• Montoro, Maria Julia
• Pomares, Helena
• Nomdedeu, Josep F.
• De Miguel Sanchez, Carlos
• Leonor, Arenillas
• Carcel, Paula
• Cedena Romero, Maria Teresa
• Xicoy, Blanca
• Rivero, Eugenia
• del Orbe Barreto, Rafael Andres
• Diez-Campelo, Maria
• Benlloch, Luis E.
• Crucitti, Davide
• Valcarcel, David

Grupos

• Hematología y Hemoterapia

  

  Investigador Principal

  Javier De La Rubia Comos

• Infección Grave

  

  Investigador Principal

  María Ángeles Tormo Mas

Abstract

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. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems.

Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association.

Keywords

• PROGNOSTIC SCORING SYSTEM