title: Predictive models for diffuse low-grade glioma patients under chemotherapy.
abstract: Diffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients' monitoring. MRI is nowadays the recommended modality to achieve this. Furthermore, if surgery remains the first option for diffuse low-grade gliomas, chemotherapy is increasingly used (before or after a possible surgery). However, crucial and difficult questions remain to be answered: identifying subgroups of patients who could benefit from chemotherapy, determining the best time to initiate chemotherapy, defining the duration of chemotherapy and evaluating the optimal time to perform surgery, or otherwise radiotherapy. In this study, we propose to help clinicians in decision-making, by designing new predictive models dedicated to the evolution of the diameter of the tumor. Two proposed statistical models (linear and exponential) have been validated on a database of 16 patients whose temozolomide-based chemotherapy lasted between 14 and 32 months, with an average duration of 22.8 months. The selection of the most appropriate model has been achieved with the corrected Akaike's Information Criterion. The results are very promising, with coefficients of determination varying from 0.79 to 0.97 with an average value of 0.90 for the linear model. This shows it is possible to alert the clinician to a change in the tumor diameter's dynamics.
authors.forename: Meriem
authors.initials: M
authors.lastname: Ben Abdallah
authors.forename: Marie
authors.initials: M
authors.lastname: Blonski
authors.forename: Sophie
authors.initials: S
authors.lastname: Wantz-Mezieres
authors.forename: Yann
authors.initials: Y
authors.lastname: Gaudeau
authors.forename: Luc
authors.initials: L
authors.lastname: Taillandier
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Moureaux
mesh_terms: D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000473:pathology*; D003606:Dacarbazine / Q000031:analogs & derivatives / Q000627:therapeutic use; D016208:Databases, Factual; D005910:Glioma / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000473:pathology*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D060787:Neoplasm Grading; D000077204:Temozolomide
chemical_list: D003606:Dacarbazine; D000077204:Temozolomide