Ribba, INRIA, Project-team NUMED, Ecole Normale Superieure de Lyon, 46 allee d0Italie, 69007 Lyon Cedex 07, France.
Clin Cancer Res. 2012 Sep 15;18(18):5071-80. doi: 10.1158/1078-0432.CCR-12-0084. Epub 2012 Jul 3.
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.
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.
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.
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.
开发一种能够描述接受化疗或放疗的成人弥漫性低级别胶质瘤（LGG）患者肿瘤大小演变的肿瘤生长抑制模型。
利用 21 例接受一线丙卡巴肼、1-(2-氯乙基)-3-环己基-1-亚硝脲和长春新碱（PCV）化疗的患者的纵向平均肿瘤直径（MTD）数据，我们构建了一个由微分方程系统组成的模型，其中纳入了反映增殖性和静止性肿瘤组织对治疗反应的肿瘤特异性和治疗相关参数。然后，我们将该模型应用于分析接受一线替莫唑胺（TMZ）化疗的 24 例患者和接受一线放疗的 25 例患者的纵向肿瘤大小数据。
该模型成功描述了 LGG 在接受 PCV 化疗之前、期间和之后的 MTD 动力学。使用相同的模型结构，我们还能够成功描述接受 TMZ 化疗或放疗的 LGG 患者的 MTD 动力学。发现肿瘤特异性参数在三种治疗方式中是一致的。该模型对敏感性分析具有鲁棒性，初步结果表明，它可以根据治疗前的肿瘤大小数据预测治疗反应。
使用 MTD 数据，我们提出了一种能够描述接受化疗或放疗的 LGG 患者肿瘤大小演变的肿瘤生长抑制模型。未来，该模型可能用于预测 LGG 患者的治疗效果，并可能成为设计更有效的化疗方案的合理工具。
