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Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of...

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Autores principales: Inchingolo, Riccardo, Maino, Cesare, Cannella, Roberto, Vernuccio, Federica, Cortese, Francesco, Dezio, Michele, Pisani, Antonio Rosario, Giandola, Teresa, Gatti, Marco, Giannini, Valentina, Ippolito, Davide, Faletti, Riccardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237092/
https://www.ncbi.nlm.nih.gov/pubmed/37274803
http://dx.doi.org/10.3748/wjg.v29.i19.2888
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author Inchingolo, Riccardo
Maino, Cesare
Cannella, Roberto
Vernuccio, Federica
Cortese, Francesco
Dezio, Michele
Pisani, Antonio Rosario
Giandola, Teresa
Gatti, Marco
Giannini, Valentina
Ippolito, Davide
Faletti, Riccardo
author_facet Inchingolo, Riccardo
Maino, Cesare
Cannella, Roberto
Vernuccio, Federica
Cortese, Francesco
Dezio, Michele
Pisani, Antonio Rosario
Giandola, Teresa
Gatti, Marco
Giannini, Valentina
Ippolito, Davide
Faletti, Riccardo
author_sort Inchingolo, Riccardo
collection PubMed
description The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named “radiomics”. Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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spelling pubmed-102370922023-06-03 Radiomics in colorectal cancer patients Inchingolo, Riccardo Maino, Cesare Cannella, Roberto Vernuccio, Federica Cortese, Francesco Dezio, Michele Pisani, Antonio Rosario Giandola, Teresa Gatti, Marco Giannini, Valentina Ippolito, Davide Faletti, Riccardo World J Gastroenterol Review The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named “radiomics”. Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease. Baishideng Publishing Group Inc 2023-05-21 2023-05-21 /pmc/articles/PMC10237092/ /pubmed/37274803 http://dx.doi.org/10.3748/wjg.v29.i19.2888 Text en ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
spellingShingle Review
Inchingolo, Riccardo
Maino, Cesare
Cannella, Roberto
Vernuccio, Federica
Cortese, Francesco
Dezio, Michele
Pisani, Antonio Rosario
Giandola, Teresa
Gatti, Marco
Giannini, Valentina
Ippolito, Davide
Faletti, Riccardo
Radiomics in colorectal cancer patients
title Radiomics in colorectal cancer patients
title_full Radiomics in colorectal cancer patients
title_fullStr Radiomics in colorectal cancer patients
title_full_unstemmed Radiomics in colorectal cancer patients
title_short Radiomics in colorectal cancer patients
title_sort radiomics in colorectal cancer patients
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237092/
https://www.ncbi.nlm.nih.gov/pubmed/37274803
http://dx.doi.org/10.3748/wjg.v29.i19.2888
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