@misc{10498/36640,
year = {2025},
month = {2},
url = {http://hdl.handle.net/10498/36640},
abstract = {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.},
publisher = {Elsevier},
keywords = {Computational flow cytometry},
keywords = {B-cell leukemia},
keywords = {Machine learning},
keywords = {Relapse prediction},
title = {Computational flow cytometry immunophenotyping at diagnosis is unable to predict relapse in childhood B-cell Acute Lymphoblastic Leukemia},
doi = {https://doi.org/10.1016/j.compbiomed.2025.109831},
author = {Martínez Rubio, Álvaro and Chulian García, Salvador and Niño López, Ana del Rosario and Picón González, Rocío and Rodríguez Gutiérrez, Juan Francisco and Gálvez de la Villa, Eva and Caballero-Velázquez, Teresa and Molinos Quintana, Águeda and Castillo Robleda, Ana and Ramírez Orellana, Manuel and Martínez-Sánchez, María V. and Minguela Puras, Alfredo and Fuster Soler, José Luis and Blázquez Goñi, Cristina and Pérez-García, Víctor M. and Rosa Durán, María},
}