Validation of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes in chronic myelomonocytic leukaemia: A novel approach for improved risk stratification
Journal
British journal of haematology
Journal Volume
204
Journal Issue
4
Pages
1529 - 1535
Date Issued
2024-02-27
Author(s)
Mosquera Orgueira, Adrian
Perez Encinas, Manuel Mateo
Diaz Varela, Nicolas
Wang, Yu-Hung
Mora, Elvira
Diaz-Beya, Marina
Montoro, Maria Julia
Pomares Marin, Helena
Ramos Ortega, Fernando
Tormo, Mar
Jerez, Andres
Nomdedeu, Josep
de Miguel Sanchez, Carlos
Arenillas, Leonor
Carcel, Paula
Cedena Romero, Maria Teresa
Xicoy Cirici, Blanca
Rivero Arango, Eugenia
Del Orbe Barreto, Rafael Andrés
Benlloch, Luis
Pérez Míguez, Carlos
Crucitti, Davide
Díez Campelo, María
Valcárcel, David
Abstract
Chronic myelomonocytic leukaemia (CMML) is a rare haematological disorder characterized by monocytosis and dysplastic changes in myeloid cell lineages. Accurate risk stratification is essential for guiding treatment decisions and assessing prognosis. This study aimed to validate the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) in CMML and to assess its performance compared with traditional scores using data from a Spanish registry (n = 1343) and a Taiwanese hospital (n = 75). In the Spanish cohort, the AIPSS-MDS accurately predicted overall survival (OS) and leukaemia-free survival (LFS), outperforming the Revised-IPSS score. Similarly, in the Taiwanese cohort, the AIPSS-MDS demonstrated accurate predictions for OS and LFS, showing superiority over the IPSS score and performing better than the CPSS and molecular CPSS scores in differentiating patient outcomes. The consistent performance of the AIPSS-MDS across both cohorts highlights its generalizability. Its adoption as a valuable tool for personalized treatment decision-making in CMML enables clinicians to identify high-risk patients who may benefit from different therapeutic interventions. Future studies should explore the integration of genetic information into the AIPSS-MDS to further refine risk stratification in CMML and improve patient outcomes.
Subjects
AIPSS-MDS; CMML; MDS; artificial intelligence; leukaemia; prognosis
SDGs
Type
journal article