Abstract：
Individual curves for tumor growth can be expressed as mathematical models. Herein we exploited a pharmacokinetic-pharmacodynamic (PKPD) model to accurately predict the lung growth curves when using data from a clinical study. Our analysis included 19 patients with non-small cell lung cancer treated with specific hypofractionated regimens, defined as stereotactic body radiation therapy (SBRT). The results exhibited the utility of the PKPD model for testing growth hypotheses of the lung tumor against clinical data. The model fitted the observed progression behavior of the lung tumors expressed by measuring the tumor volume of the patients before and after treatment from CT screening. The changes in dynamics were best captured by the parameter identified as the patients\' response to treatment. Median follow-up times for the tumor volume after SBRT were 126 days. These results have proven the use of mathematical modeling in preclinical anticancer investigations as a potential prognostic tool.
摘要：
肿瘤生长的个体曲线可以表示为数学模型。在本文中，我们利用药代动力学-药效学（PKPD）模型来使用临床研究数据准确预测肺生长曲线。我们的分析包括19例非小细胞肺癌患者，这些患者接受了特定的大分割方案，定义为立体定向身体放射治疗（SBRT）。结果显示PKPD模型用于针对临床数据测试肺肿瘤的生长假设的实用性。该模型拟合了观察到的肺肿瘤的进展行为,通过测量患者在CT筛查治疗前后的肿瘤体积来表达。动力学的变化最好通过确定为患者对治疗的反应的参数来捕获。SBRT后肿瘤体积的中位随访时间为126天。这些结果已证明在临床前抗癌研究中使用数学建模作为潜在的预后工具。
