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The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomic...

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Autores principales: Masokano, Ismail Bilal, Liu, Wenguang, Xie, Simin, Marcellin, Dama Faniriantsoa Henrio, Pei, Yigang, Li, Wenzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510095/
https://www.ncbi.nlm.nih.gov/pubmed/32962762
http://dx.doi.org/10.1186/s40644-020-00341-y
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author Masokano, Ismail Bilal
Liu, Wenguang
Xie, Simin
Marcellin, Dama Faniriantsoa Henrio
Pei, Yigang
Li, Wenzheng
author_facet Masokano, Ismail Bilal
Liu, Wenguang
Xie, Simin
Marcellin, Dama Faniriantsoa Henrio
Pei, Yigang
Li, Wenzheng
author_sort Masokano, Ismail Bilal
collection PubMed
description Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.
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spelling pubmed-75100952020-09-24 The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges Masokano, Ismail Bilal Liu, Wenguang Xie, Simin Marcellin, Dama Faniriantsoa Henrio Pei, Yigang Li, Wenzheng Cancer Imaging Review Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies. BioMed Central 2020-09-22 /pmc/articles/PMC7510095/ /pubmed/32962762 http://dx.doi.org/10.1186/s40644-020-00341-y Text en © The Author(s) 2020 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Masokano, Ismail Bilal
Liu, Wenguang
Xie, Simin
Marcellin, Dama Faniriantsoa Henrio
Pei, Yigang
Li, Wenzheng
The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title_full The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title_fullStr The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title_full_unstemmed The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title_short The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges
title_sort application of texture quantification in hepatocellular carcinoma using ct and mri: a review of perspectives and challenges
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510095/
https://www.ncbi.nlm.nih.gov/pubmed/32962762
http://dx.doi.org/10.1186/s40644-020-00341-y
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