RT journal article
T1 Computational flow cytometry immunophenotyping at diagnosis is unable to predict relapse in childhood B-cell Acute Lymphoblastic Leukemia
A1 Martínez Rubio, Álvaro
A1 Chulian García, Salvador
A1 Niño López, Ana del Rosario
A1 Picón González, Rocío
A1 Rodríguez Gutiérrez, Juan Francisco
A1 Gálvez de la Villa, Eva
A1 Caballero-Velázquez, Teresa
A1 Molinos Quintana, Águeda
A1 Castillo Robleda, Ana
A1 Ramírez Orellana, Manuel
A1 Martínez-Sánchez, María V.
A1 Minguela Puras, Alfredo
A1 Fuster Soler, José Luis
A1 Blázquez Goñi, Cristina
A1 Pérez-García, Víctor M.
A1 Rosa Durán, María
A2 Matemáticas
K1 Computational flow cytometry
K1 B-cell leukemia
K1 Machine learning
K1 Relapse prediction
AB B-cell Acute Lymphoblastic Leukemia is the most prevalent form of childhood cancer, with approximately 15% of patients undergoing relapse after initial treatment. Further advancements depend on novel therapies and more precise risk stratification criteria. In the context of computational flow cytometry and machine learning, this paper aims to explore the potential prognostic value of flow cytometry data at diagnosis, a relatively unexplored direction for relapse prediction in this disease. To this end, we collected a dataset of 252 patients from three hospitals and implemented a comprehensive pipeline for multicenter data integration, feature extraction, and patient classification, comparing the results with existing algorithms from the literature. The analysis revealed no significant differences in immunophenotypic patterns between relapse and non-relapse patients and suggests the need for alternative approaches to handle flow cytometry data in relapse prediction.
PB Elsevier
SN 1879-0534
YR 2025
FD 2025-02-20
LK http://hdl.handle.net/10498/36640
UL http://hdl.handle.net/10498/36640
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026