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Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer

Pancreatic cancer is a lethal disease, and resistance to chemotherapy is a critical factor influencing the postoperative prognosis. Tumour heterogeneity is an important indicator of chemoresistance. Therefore, we analysed tumour heterogeneity in preoperative computed tomography scans by performing t...

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Autores principales: Kim, Hyung Sun, Kim, Young Jae, Kim, Kwang Gi, Park, Joon Seong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874598/
https://www.ncbi.nlm.nih.gov/pubmed/31757989
http://dx.doi.org/10.1038/s41598-019-53831-w
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author Kim, Hyung Sun
Kim, Young Jae
Kim, Kwang Gi
Park, Joon Seong
author_facet Kim, Hyung Sun
Kim, Young Jae
Kim, Kwang Gi
Park, Joon Seong
author_sort Kim, Hyung Sun
collection PubMed
description Pancreatic cancer is a lethal disease, and resistance to chemotherapy is a critical factor influencing the postoperative prognosis. Tumour heterogeneity is an important indicator of chemoresistance. Therefore, we analysed tumour heterogeneity in preoperative computed tomography scans by performing texture analysis using the grey-level run-length matrix and analysed the correlation of survival with the value obtained in these analyses. We analysed 116 consecutive patients who underwent curative resection and had preoperative contrast-enhanced computed tomography data available for analysis. A region of interest was drawn on all slices with a visible tumour and normal pancreas on the arterial phase computed tomography scans; the correlation of pathological characteristics with grey-level run-length matrix features was analysed. We then performed Kaplan–Meier survival curve analysis among pancreatic cancer patients. The grey-level non-uniformity values in grey-level run-length matrix features for tumours were higher than those for normal pancreas. High grey-level non-uniformity values represent a non-uniform texture, i.e., heterogeneity. Grey-level run-length matrix features showed that recurrence-free survival was shorter in the group with high grey-level non-uniformity 135 values (p = 0.025). Our analyses of the correlation between pathological outcomes and grey-level run-length matrix features in pancreatic cancer patients showed that grey-level non-uniformity values were powerful prognostic indicators.
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spelling pubmed-68745982019-12-04 Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer Kim, Hyung Sun Kim, Young Jae Kim, Kwang Gi Park, Joon Seong Sci Rep Article Pancreatic cancer is a lethal disease, and resistance to chemotherapy is a critical factor influencing the postoperative prognosis. Tumour heterogeneity is an important indicator of chemoresistance. Therefore, we analysed tumour heterogeneity in preoperative computed tomography scans by performing texture analysis using the grey-level run-length matrix and analysed the correlation of survival with the value obtained in these analyses. We analysed 116 consecutive patients who underwent curative resection and had preoperative contrast-enhanced computed tomography data available for analysis. A region of interest was drawn on all slices with a visible tumour and normal pancreas on the arterial phase computed tomography scans; the correlation of pathological characteristics with grey-level run-length matrix features was analysed. We then performed Kaplan–Meier survival curve analysis among pancreatic cancer patients. The grey-level non-uniformity values in grey-level run-length matrix features for tumours were higher than those for normal pancreas. High grey-level non-uniformity values represent a non-uniform texture, i.e., heterogeneity. Grey-level run-length matrix features showed that recurrence-free survival was shorter in the group with high grey-level non-uniformity 135 values (p = 0.025). Our analyses of the correlation between pathological outcomes and grey-level run-length matrix features in pancreatic cancer patients showed that grey-level non-uniformity values were powerful prognostic indicators. Nature Publishing Group UK 2019-11-22 /pmc/articles/PMC6874598/ /pubmed/31757989 http://dx.doi.org/10.1038/s41598-019-53831-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kim, Hyung Sun
Kim, Young Jae
Kim, Kwang Gi
Park, Joon Seong
Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title_full Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title_fullStr Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title_full_unstemmed Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title_short Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer
title_sort preoperative ct texture features predict prognosis after curative resection in pancreatic cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874598/
https://www.ncbi.nlm.nih.gov/pubmed/31757989
http://dx.doi.org/10.1038/s41598-019-53831-w
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