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MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer
To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844724/ https://www.ncbi.nlm.nih.gov/pubmed/29552288 http://dx.doi.org/10.18632/oncotarget.23813 |
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author | Meng, Yankai Zhang, Chongda Zou, Shuangmei Zhao, Xinming Xu, Kai Zhang, Hongmei Zhou, Chunwu |
author_facet | Meng, Yankai Zhang, Chongda Zou, Shuangmei Zhao, Xinming Xu, Kai Zhang, Hongmei Zhou, Chunwu |
author_sort | Meng, Yankai |
collection | PubMed |
description | To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters were compared between pre- and early nCRT. Area Under receiver operating characteristic (ROC) Curves [AUCs] were used to compare the diagnostic performance of statistically significant difference parameters and logistic regression analysis predicted probabilities for discriminating responders and nonresponders. The Standard Deviation (SD), kurtosis and uniformity were statistically significantly difference between pre- and early nCRT (p = 0.0012, 0.0001, and < 0.0001, respectively). In pathological complete response (pCR) group, pre-uniformity and pre-Energy were significantly higher than that of nonresponders (p = 0.03 and p < 0.01, respectively), while the pre-entropy in nonresponder was reverse (p = 0.01). The diagnostic performance of pre-kurtosis and pre-Energy were higher in tumor regression grade (TRG) and pCR group (AUC = 0.67, 0.73, respectively). Logistic regression analysis showed that diagnostic performance for prediction responder and nonresponder did not significantly improve compared with to pre-uniformity, energy and entropy in pCR group (AUC = 0.76, p = 0.2794, 0.4222 and 0.3512, respectively). Texture parameters as imaging biomarkers have the potential to prediction and early assessment of tumoral treatment response to neoadjuvant chemoradiotherapy in patients with LARC. |
format | Online Article Text |
id | pubmed-5844724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-58447242018-03-16 MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer Meng, Yankai Zhang, Chongda Zou, Shuangmei Zhao, Xinming Xu, Kai Zhang, Hongmei Zhou, Chunwu Oncotarget Research Paper To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters were compared between pre- and early nCRT. Area Under receiver operating characteristic (ROC) Curves [AUCs] were used to compare the diagnostic performance of statistically significant difference parameters and logistic regression analysis predicted probabilities for discriminating responders and nonresponders. The Standard Deviation (SD), kurtosis and uniformity were statistically significantly difference between pre- and early nCRT (p = 0.0012, 0.0001, and < 0.0001, respectively). In pathological complete response (pCR) group, pre-uniformity and pre-Energy were significantly higher than that of nonresponders (p = 0.03 and p < 0.01, respectively), while the pre-entropy in nonresponder was reverse (p = 0.01). The diagnostic performance of pre-kurtosis and pre-Energy were higher in tumor regression grade (TRG) and pCR group (AUC = 0.67, 0.73, respectively). Logistic regression analysis showed that diagnostic performance for prediction responder and nonresponder did not significantly improve compared with to pre-uniformity, energy and entropy in pCR group (AUC = 0.76, p = 0.2794, 0.4222 and 0.3512, respectively). Texture parameters as imaging biomarkers have the potential to prediction and early assessment of tumoral treatment response to neoadjuvant chemoradiotherapy in patients with LARC. Impact Journals LLC 2017-12-22 /pmc/articles/PMC5844724/ /pubmed/29552288 http://dx.doi.org/10.18632/oncotarget.23813 Text en Copyright: © 2018 Meng et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Meng, Yankai Zhang, Chongda Zou, Shuangmei Zhao, Xinming Xu, Kai Zhang, Hongmei Zhou, Chunwu MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title | MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title_full | MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title_fullStr | MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title_full_unstemmed | MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title_short | MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
title_sort | mri texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844724/ https://www.ncbi.nlm.nih.gov/pubmed/29552288 http://dx.doi.org/10.18632/oncotarget.23813 |
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