TY - JOUR
T1 - A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy
AU - Ribba, Benjamin
AU - Kaloshi, Gentian
AU - Peyre, Mathieu
AU - Ricard, Damien
AU - Calvez, Vincent
AU - Tod, Michel
AU - Čajavec-Bernard, Branka
AU - Idbaih, Ahmed
AU - Psimaras, Dimitri
AU - Dainese, Linda
AU - Pallud, Johan
AU - Cartalat-Carel, Stéphanie
AU - Delattre, Jean Yves
AU - Honnorat, Jérôme
AU - Grenier, Emmanuel
AU - Ducray, Fraņcois
PY - 2012/9/15
Y1 - 2012/9/15
N2 - 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 tumorspecific 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 inLGGpatients treated withTMZchemotherapy 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: UsingMTDdata, we propose a tumor growth inhibition model able to describeLGGtumor 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.
AB - 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 tumorspecific 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 inLGGpatients treated withTMZchemotherapy 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: UsingMTDdata, we propose a tumor growth inhibition model able to describeLGGtumor 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.
UR - https://www.scopus.com/pages/publications/84866375606
U2 - 10.1158/1078-0432.CCR-12-0084
DO - 10.1158/1078-0432.CCR-12-0084
M3 - Article
C2 - 22761472
AN - SCOPUS:84866375606
SN - 1078-0432
VL - 18
SP - 5071
EP - 5080
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 18
ER -