Data-driven predictive models of diffuse low-grade gliomas under chemotherapy - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue IEEE Journal of Biomedical and Health Informatics Année : 2019

Data-driven predictive models of diffuse low-grade gliomas under chemotherapy

Résumé

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.

Domaines

Neurosciences
Fichier non déposé

Dates et versions

hal-02097695 , version 1 (12-04-2019)

Identifiants

Citer

Meriem Ben Abdallah, Marie Blonski, Sophie Wantz-Mézières, Yann Gaudeau, Luc Taillandier, et al.. Data-driven predictive models of diffuse low-grade gliomas under chemotherapy. IEEE Journal of Biomedical and Health Informatics, 2019, 23 (1), pp.38-46. ⟨10.1109/JBHI.2018.2834159⟩. ⟨hal-02097695⟩
111 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More