Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Zhidai Liu, Tingting Zhou, Xing Han, Tingyuan Lang, Shan Liu, Penghui Zhang, Haiyan Liu, Kexing Wan, Jie Yu, Liang Zhang, Liyan Chen, Roger W. Beuerman, Bin Peng, Lei Zhou, Lin Zou

ABSTRACT

BACKGROUND: The altered concentrations of amino acids were found in the bone marrow or blood of leukemia patients. Metabolomics technology combining mathematical model of biomarkers could be used for assisting the diagnosis of pediatric acute leukemia (AL). METHODS: The concentrations of 17 amino acids was measured by targeted liquid chromatograph-tandem mass spectrometry in periphery blood collected using dried blood spots. After evaluation, the mathematical models were further evaluated by prospective clinical validation cohort for AL diagnosis. RESULTS: The concentrations of 13 in 17 amino acids were statistically different between the periphery blood dried serum dots measured by targeted LC-MS/MS. The receiver operating characteristic analysis for the models of amino acid panel showed that the area under curve for AL diagnosis were 0.848, 0.834 and 0.856 by SVM, RF and XGBoost. The Kappa values in further prospectively evaluated clinical cohort were 0.697, 0.703 and 0.789 (p > 0.05) respectively, and the accuracies for the models were 84.86%, 85.20% and 89.46% respectively with further clinical validation. CONCLUSIONS: The established mathematical model is a faster, cheaper and more convenient way than conventional methods, and no significant difference on the effect of diagnosis comparing with conventional methods. The mathematical model can be clinically useful for assisting pediatric AL diagnosis. More... »

PAGES

38

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12967-019-1783-9

DOI

http://dx.doi.org/10.1186/s12967-019-1783-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111593815

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30674317


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