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Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling

BACKGROUND: In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or the...

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Autores principales: Burny, Claire, Rabilloud, Muriel, Golfier, François, Massardier, Jérôme, Hajri, Touria, Schott, Anne-Marie, Subtil, Fabien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702411/
https://www.ncbi.nlm.nih.gov/pubmed/26732086
http://dx.doi.org/10.1186/s12874-015-0106-y
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author Burny, Claire
Rabilloud, Muriel
Golfier, François
Massardier, Jérôme
Hajri, Touria
Schott, Anne-Marie
Subtil, Fabien
author_facet Burny, Claire
Rabilloud, Muriel
Golfier, François
Massardier, Jérôme
Hajri, Touria
Schott, Anne-Marie
Subtil, Fabien
author_sort Burny, Claire
collection PubMed
description BACKGROUND: In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes. METHODS: A model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles. RESULTS: The resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes. CONCLUSION: The use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0106-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-47024112016-01-07 Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling Burny, Claire Rabilloud, Muriel Golfier, François Massardier, Jérôme Hajri, Touria Schott, Anne-Marie Subtil, Fabien BMC Med Res Methodol Research Article BACKGROUND: In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes. METHODS: A model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles. RESULTS: The resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes. CONCLUSION: The use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0106-y) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-05 /pmc/articles/PMC4702411/ /pubmed/26732086 http://dx.doi.org/10.1186/s12874-015-0106-y Text en © Burny et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Burny, Claire
Rabilloud, Muriel
Golfier, François
Massardier, Jérôme
Hajri, Touria
Schott, Anne-Marie
Subtil, Fabien
Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title_full Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title_fullStr Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title_full_unstemmed Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title_short Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
title_sort early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702411/
https://www.ncbi.nlm.nih.gov/pubmed/26732086
http://dx.doi.org/10.1186/s12874-015-0106-y
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