ENGELHARDT, Julien; MONTALIBET, Virginie; SAUT, Olivier; LOISEAU, Hugues; COLLIN, Annabelle

doi:10.1016/j.ebiom.2023.104697

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ENGELHARDT, Julien
CHU de Bordeaux Pellegrin [Bordeaux]
Modélisation Mathématique pour l'Oncologie [MONC]

MONTALIBET, Virginie
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]

SAUT, Olivier
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Modélisation Mathématique pour l'Oncologie [MONC]

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EBioMedicine. 2023-07-04, vol. 94, p. 104697

Elsevier

Résumé en anglais

Background: The incidence of newly diagnosed meningiomas, particularly those diagnosed incidentally, is continually increasing. The indication for treatment is empirical because, despite numerous studies, the natural history of these tumors remains difficult to describe and predict. Methods: This retrospective single-centre study included 294 consecutive patients with 333 meningiomas who underwent three or more brain imaging scans. Linear, exponential, power, and Gompertz models were constructed to derive volume-time curves, by using a mixed-effect approach. The most accurate model was used to analyse tumour growth and predictors of rapid growth. Interpretation: Meningiomas exhibit multiple growth phases, as described by the Gompertz model. The management of meningiomas should be discussed according to the growth phase, comorbidities, tumour location, size, and growth rate. Further research is needed to evaluate the associations between radiomics features and the growth phases of meningiomas.< Réduire

Mots clés en anglais

Meningioma

Gompertz model

Natural history

Volumetry

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Evaluation of four tumour growth models to describe the natural history of meningiomas

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