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Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia
Acute lymphoblastic leukaemia (ALL) is a blood cancer which mainly affects children and adolescents. Therapy fails for approximately 20% of patients who suffer relapse. We use methods from topological data analysis, which quantifies shapes in data, to analyse pre-treatment ALL datasets with known outcomes. We combine these analyses with machine learning to identify significant shape characteristics in the data (notably, isolated data islands and empty spaces between them) and show that they predict risk of relapse with high accuracy. We also confirm the predictive power of CD10, CD20, CD38 and CD45 as biomarkers for ALL diagnosis. Chulián et al 2023
Image Credit: Salvador Chulián, salvador.chulian@uca.es
Citation: (2023) PLoS Computational Biology Issue Image | Vol. 19(8) September 2023. PLoS Comput Biol 19(8): ev19.i08. https://doi.org/10.1371/image.pcbi.v19.i08
Published: September 6, 2023
Copyright: © 2023 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Acute lymphoblastic leukaemia (ALL) is a blood cancer which mainly affects children and adolescents. Therapy fails for approximately 20% of patients who suffer relapse. We use methods from topological data analysis, which quantifies shapes in data, to analyse pre-treatment ALL datasets with known outcomes. We combine these analyses with machine learning to identify significant shape characteristics in the data (notably, isolated data islands and empty spaces between them) and show that they predict risk of relapse with high accuracy. We also confirm the predictive power of CD10, CD20, CD38 and CD45 as biomarkers for ALL diagnosis. Chulián et al 2023
Image Credit: Salvador Chulián, salvador.chulian@uca.es