| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
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. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Review |
| Article Title |
Radiomics in colorectal cancer patients
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Riccardo Inchingolo, Cesare Maino, Roberto Cannella, Federica Vernuccio, Francesco Cortese, Michele Dezio, Antonio Rosario Pisani, Teresa Giandola, Marco Gatti, Valentina Giannini, Davide Ippolito and Riccardo Faletti |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Riccardo Inchingolo, MD, Director, Director, Doctor, Doctor, Unit of Interventional Radiology, F. Miulli Hospital, Sp per Santeramo, Acquaviva delle Fonti 70021, Italy. riccardoin@hotmail.it |
| Key Words |
Colorectal cancer; Radiomics; Artificial intelligence; Liver metastases; Magnetic resonance imaging; Computed tomography; Positron emission tomography/computed tomography |
| Core Tip |
Stratifying colorectal cancer patients with high-risk disease and the evaluation of the overall chemotherapy benefit are a clinical challenge. Radiomics through radiological images analysis using automated computer-based techniques allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT), magnetic resonance imaging, and positron emission tomography/CT, in particular to understand tumor biology, to develop imaging biomarkers for diagnosis, staging, and prognosis, to predict treatment response and to monitor disease. |
| Publish Date |
2023-05-16 07:16 |
| Citation |
Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29(19): 2888-2904 |
| URL |
https://www.wjgnet.com/1007-9327/full/v29/i19/2888.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v29.i19.2888 |
