Computational flow cytometry immunophenotyping at diagnosis is unable to predict relapse in childhood B-cell Acute Lymphoblastic Leukemia

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 cytome...

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Bibliographic Details
Published in:Computers in biology and medicine 2025-04, Vol.188, p.109831, Article 109831
Main Authors: Martínez-Rubio, Álvaro, Chulián, Salvador, Niño-López, Ana, Picón-González, Rocío, Rodríguez Gutiérrez, Juan F., Gálvez de la Villa, Eva, Caballero Velázquez, Teresa, Molinos Quintana, Águeda, Castillo Robleda, Ana, Ramírez Orellana, Manuel, Martínez Sánchez, María Victoria, Minguela Puras, Alfredo, Fuster Soler, José Luis, Blázquez Goñi, Cristina, Pérez-García, Víctor M., Rosa, María
Format: Article
Language:English
Subjects:
Acute lymphoblastic leukemia
Algorithms
B-cell leukemia
Children
Computational flow cytometry
Computer applications
Data integration
Diagnosis
Flow cytometry
Leukemia
Lymphatic leukemia
Lymphocytes B
Machine learning
Patients
Relapse prediction
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Summary: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. [Display omitted] •Computational integration of flow cytometry data from 188 pediatric patients.•Clustering and cell abundance do not provide relevant predictive performance.•Intensity distribution summaries also fail to predict relapse.•Comparison with existing algorithms confirms limited prognostic value.•Future studies may need other single-cell data or different computational methods.
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.109831