Wendling Thierry, Mistry Hitesh, Ogungbenro Kayode, Aarons Leon
Manchester Pharmacy School, The University of Manchester, Stopford Building Room 3.32, Oxford Road, Manchester, M13 9PT, UK.
Drug Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research, 4002, Basel, Switzerland.
Cancer Chemother Pharmacol. 2016 May;77(5):927-38. doi: 10.1007/s00280-016-2994-x. Epub 2016 Mar 3.
Measures derived from longitudinal tumour size data have been increasingly utilised to predict survival of patients with solid tumours. The aim of this study was to examine the prognostic value of such measures for patients with metastatic pancreatic cancer undergoing gemcitabine therapy.
The control data from two Phase III studies were retrospectively used to develop (271 patients) and validate (398 patients) survival models. Firstly, 31 baseline variables were screened from the training set using penalised Cox regression. Secondly, tumour shrinkage metrics were interpolated for each patient by hierarchical modelling of the tumour size time-series. Subsequently, survival models were built by applying two approaches: the first aimed at incorporating model-derived tumour size metrics in a parametric model, and the second simply aimed at identifying empirical factors using Cox regression. Finally, the performance of the models in predicting patient survival was evaluated on the validation set.
Depending on the modelling approach applied, albumin, body surface area, neutrophil, baseline tumour size and tumour shrinkage measures were identified as potential prognostic factors. The distributional assumption on survival times appeared to affect the identification of risk factors but not the ability to describe the training data. The two survival modelling approaches performed similarly in predicting the validation data.
A parametric model that incorporates model-derived tumour shrinkage metrics in addition to other baseline variables could predict reasonably well survival of patients with metastatic pancreatic cancer. However, the predictive performance was not significantly better than a simple Cox model that incorporates only baseline characteristics.
源自纵向肿瘤大小数据的指标越来越多地用于预测实体瘤患者的生存情况。本研究的目的是检验这些指标对接受吉西他滨治疗的转移性胰腺癌患者的预后价值。
回顾性使用两项III期研究的对照数据来构建（271例患者）和验证（398例患者）生存模型。首先，使用惩罚Cox回归从训练集中筛选31个基线变量。其次，通过对肿瘤大小时间序列进行分层建模，为每位患者插补肿瘤缩小指标。随后，采用两种方法构建生存模型：第一种方法旨在将模型衍生的肿瘤大小指标纳入参数模型，第二种方法仅旨在使用Cox回归识别经验性因素。最后，在验证集上评估模型预测患者生存的性能。
根据所应用的建模方法，白蛋白、体表面积、中性粒细胞、基线肿瘤大小和肿瘤缩小指标被确定为潜在的预后因素。生存时间的分布假设似乎会影响危险因素的识别，但不会影响描述训练数据的能力。两种生存建模方法在预测验证数据方面表现相似。
除其他基线变量外，纳入模型衍生的肿瘤缩小指标的参数模型可以较好地预测转移性胰腺癌患者的生存情况。然而，其预测性能并不显著优于仅纳入基线特征的简单Cox模型。
