A Tumor Growth Inhibition Model for Low-Grade Glioma Treated with Chemotherapy or Radiotherapy |
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Authors: | Ribba Benjamin Kaloshi Gentian Peyre Mathieu Ricard Damien Calvez Vincent Tod Michel Cajavec-Bernard Branka Idbaih Ahmed Psimaras Dimitri Dainese Linda Pallud Johan Cartalat-Carel Stéphanie Delattre Jean-Yves Honnorat Jér?me Grenier Emmanuel Ducray Fran?ois |
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Affiliation: | Authors' Affiliations: INRIA, Project-team NUMED, Ecole Normale Supérieure de Lyon; Hospices Civils de Lyon, H?pital Neurologique, Neuro-oncologie; EA3738 CTO, Faculté de Médecine Lyon-Sud, Oullins; Pharmacie, H?pital de la Croix Rousse, Hospices civils de Lyon; Université de Lyon, Claude Bernard Lyon 1; Lyon Neuroscience Research Center INSERM U1028/CNRS UMR 5292, Lyon, France; AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle; Université Pierre & Marie Curie Paris VI, Faculté de médecine Pitié-Salpêtrière, CNRS UMR 7225 and UMR-S975; H?pital d'Instruction des Armées du Val-de-Grace; Service de Neuropathologie, H?pital de la Salpêtrière; and Service de Neurochirurgie, Centre Hospitalier Sainte-Anne, University Paris Descartes, Paris, France. |
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Abstract: | PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules. Clin Cancer Res; 18(18); 5071-80. ?2012 AACR. |
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