%0 Journal Article
%T Data-driven predictive models of diffuse low-grade gliomas under chemotherapy
%+ Centre de Recherche en Automatique de Nancy (CRAN)
%+ Service de Neuro-Oncologie [CHRU Nancy]
%+ Institut Élie Cartan de Lorraine (IECL)
%+ Biology, genetics and statistics (BIGS)
%+ Université de Strasbourg (UNISTRA)
%+ CRLCC Val d'Aurelle - Paul Lamarque
%+ Laboratoire Charles Coulomb (L2C)
%+ Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)
%+ Institut des Neurosciences de Montpellier (INM)
%A Ben Abdallah, Meriem
%A Blonski, Marie
%A Wantz-Mézières, Sophie
%A Gaudeau, Yann
%A Taillandier, Luc
%A Moureaux, Jean-Marie
%A Darlix, Amelie
%A Menjot de Champfleur, Nicolas
%A Duffau, Hugues
%< avec comité de lecture
%Z L2C:19-044 - BioSiS
%@ 2168-2194
%J IEEE Journal of Biomedical and Health Informatics
%I Institute of Electrical and Electronics Engineers
%V 23
%N 1
%P 38-46
%8 2019-01
%D 2019
%R 10.1109/JBHI.2018.2834159
%M 29993901
%K Brain tumor
%K Chemotherapy
%K Glioma
%K MRI
%K Predictive model
%K Temozolomide
%K TMZ
%Z Cognitive science/NeuroscienceJournal articles
%X Diffuse low-grade gliomas (DLGG) are brain tumors of young adults. They affect the quality of life of the inflicted patients and, if untreated, they evolve into higher grade tumors where the patient's life is at risk. Therapeutic management of DLGGs includes chemotherapy, and tumor diameter is particularly important for the follow-up of DLGG evolution. In fact, the main clinical basis for deciding whether to continue chemotherapy is tumor diameter growth rate. In order to reliably assist the doctors in selecting the most appropriate time to stop treatment, we propose a novel clinical decision support system. Based on two mathematical models, one linear and one exponential, we are able to predict the evolution of tumor diameter under Temozolomide chemotherapy as a first treatment and thus offer a prognosis on when to end it. We present the results of an implementation of these models on a database of 42 patients from Nancy and Montpellier University Hospitals. In this database, 38 patients followed the linear model and four patients followed the exponential model. From a training data set of a minimal size of five, we are able to predict the next tumor diameter with high accuracy. Thanks to the corresponding prediction interval, it is possible to check if the new observation corresponds to the predicted diameter. If the observed diameter is within the prediction interval, the clinician is notified that the trend is within a normal range. Otherwise, the practitioner is alerted of a significant change in tumor diameter.
%G English
%L hal-02097695
%U https://hal.science/hal-02097695
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%~ INRIA
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%~ INM
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%~ L2C
%~ INRIA-LORRAINE
%~ FNCLCC
%~ VALDAURELLE
%~ INRIA-NANCY-GRAND-EST
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%~ UNIV-LORRAINE
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%~ MIPS
%~ BS
%~ UNIV-MONTPELLIER
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%~ UM-2015-2021
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