AstraZeneca, Oncology R&D, Cambridge, UK.
Certara, Princeton, NJ, 08650, USA.
Eur J Cancer. 2021 Jun;150:42-52. doi: 10.1016/j.ejca.2021.03.027. Epub 2021 Apr 20.
Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.
Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.
Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.
Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.
癌症疾病负担通常通过 RECIST 标准在实体瘤中进行放射学评估，以支持反应评估。这些纵向数据适合数学建模，这些模型描述了初始肿瘤大小、应答患者的初始肿瘤缩小以及随着患者疾病进展的肿瘤复发性率。了解这些参数在患者群体和治疗之间的变化情况，将有助于从非临床数据到临床试验设计的转化模型方法。
本文报告了对报告的模型参数值的荟萃分析。通过 PubMed 搜索和基于文本的聚类方法识别了适当的文献。然后以图形和 ANOVA 的方式检查结果的参数估计值。
总共确定了 80 个治疗臂的参数值，这些参数值来自 80 个试验臂，共包含 34881 名患者。参数估计值通常是一致的。研究发现，肿瘤缩小和复发性率的变化很大程度上是由不同的癌症和治疗引起的，癌症类型占缩小率变化的 66%，占报告的复发性率变化的 71%。还按癌症和治疗报告了平均平均参数值。
纵向数据的数学建模通常是基于每个临床试验进行报告的。然而，这里报告的结果表明，更综合的方法将有利于新治疗方法的开发，以及现有治疗方法的进一步优化。
