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Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary...

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Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784146/
https://www.ncbi.nlm.nih.gov/pubmed/29367653
http://dx.doi.org/10.1038/s41598-017-14687-0
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collection PubMed
description Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.
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spelling pubmed-57841462018-02-07 Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients Sci Rep Article Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted. Nature Publishing Group UK 2018-01-24 /pmc/articles/PMC5784146/ /pubmed/29367653 http://dx.doi.org/10.1038/s41598-017-14687-0 Text en © The Author(s) 2017 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
Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title_full Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title_fullStr Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title_full_unstemmed Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title_short Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
title_sort investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784146/
https://www.ncbi.nlm.nih.gov/pubmed/29367653
http://dx.doi.org/10.1038/s41598-017-14687-0
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