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Radiomics in hepatic metastasis by colorectal cancer

BACKGROUND: Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most...

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Autores principales: Granata, Vincenza, Fusco, Roberta, Barretta, Maria Luisa, Picone, Carmine, Avallone, Antonio, Belli, Andrea, Patrone, Renato, Ferrante, Marilina, Cozzi, Diletta, Grassi, Roberta, Grassi, Roberto, Izzo, Francesco, Petrillo, Antonella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173908/
https://www.ncbi.nlm.nih.gov/pubmed/34078424
http://dx.doi.org/10.1186/s13027-021-00379-y
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author Granata, Vincenza
Fusco, Roberta
Barretta, Maria Luisa
Picone, Carmine
Avallone, Antonio
Belli, Andrea
Patrone, Renato
Ferrante, Marilina
Cozzi, Diletta
Grassi, Roberta
Grassi, Roberto
Izzo, Francesco
Petrillo, Antonella
author_facet Granata, Vincenza
Fusco, Roberta
Barretta, Maria Luisa
Picone, Carmine
Avallone, Antonio
Belli, Andrea
Patrone, Renato
Ferrante, Marilina
Cozzi, Diletta
Grassi, Roberta
Grassi, Roberto
Izzo, Francesco
Petrillo, Antonella
author_sort Granata, Vincenza
collection PubMed
description BACKGROUND: Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. METHODS: The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. RESULTS: We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. CONCLUSIONS: In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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spelling pubmed-81739082021-06-03 Radiomics in hepatic metastasis by colorectal cancer Granata, Vincenza Fusco, Roberta Barretta, Maria Luisa Picone, Carmine Avallone, Antonio Belli, Andrea Patrone, Renato Ferrante, Marilina Cozzi, Diletta Grassi, Roberta Grassi, Roberto Izzo, Francesco Petrillo, Antonella Infect Agent Cancer Research Article BACKGROUND: Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. METHODS: The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. RESULTS: We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. CONCLUSIONS: In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment. BioMed Central 2021-06-02 /pmc/articles/PMC8173908/ /pubmed/34078424 http://dx.doi.org/10.1186/s13027-021-00379-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Research Article
Granata, Vincenza
Fusco, Roberta
Barretta, Maria Luisa
Picone, Carmine
Avallone, Antonio
Belli, Andrea
Patrone, Renato
Ferrante, Marilina
Cozzi, Diletta
Grassi, Roberta
Grassi, Roberto
Izzo, Francesco
Petrillo, Antonella
Radiomics in hepatic metastasis by colorectal cancer
title Radiomics in hepatic metastasis by colorectal cancer
title_full Radiomics in hepatic metastasis by colorectal cancer
title_fullStr Radiomics in hepatic metastasis by colorectal cancer
title_full_unstemmed Radiomics in hepatic metastasis by colorectal cancer
title_short Radiomics in hepatic metastasis by colorectal cancer
title_sort radiomics in hepatic metastasis by colorectal cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173908/
https://www.ncbi.nlm.nih.gov/pubmed/34078424
http://dx.doi.org/10.1186/s13027-021-00379-y
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