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Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data

PURPOSE: 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. M...

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Autores principales: Wendling, Thierry, Mistry, Hitesh, Ogungbenro, Kayode, Aarons, Leon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4844653/
https://www.ncbi.nlm.nih.gov/pubmed/26940939
http://dx.doi.org/10.1007/s00280-016-2994-x
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author Wendling, Thierry
Mistry, Hitesh
Ogungbenro, Kayode
Aarons, Leon
author_facet Wendling, Thierry
Mistry, Hitesh
Ogungbenro, Kayode
Aarons, Leon
author_sort Wendling, Thierry
collection PubMed
description PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00280-016-2994-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-48446532016-05-21 Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data Wendling, Thierry Mistry, Hitesh Ogungbenro, Kayode Aarons, Leon Cancer Chemother Pharmacol Original Article PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00280-016-2994-x) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2016-03-03 2016 /pmc/articles/PMC4844653/ /pubmed/26940939 http://dx.doi.org/10.1007/s00280-016-2994-x Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Wendling, Thierry
Mistry, Hitesh
Ogungbenro, Kayode
Aarons, Leon
Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title_full Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title_fullStr Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title_full_unstemmed Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title_short Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
title_sort predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4844653/
https://www.ncbi.nlm.nih.gov/pubmed/26940939
http://dx.doi.org/10.1007/s00280-016-2994-x
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