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Articles Published Processes
8/25/2021 2:11:51 AM | Browse: 432 | Download: 931
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Received |
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2021-01-27 11:20 |
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Peer-Review Started |
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2021-01-27 11:25 |
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To Make the First Decision |
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Return for Revision |
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2021-03-07 03:36 |
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Revised |
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2021-03-13 15:37 |
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Second Decision |
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2021-07-22 03:06 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2021-07-22 05:44 |
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Articles in Press |
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2021-07-22 05:44 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2021-08-09 22:03 |
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Typeset the Manuscript |
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2021-08-24 01:09 |
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Publish the Manuscript Online |
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2021-08-25 02:11 |
| 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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2021. 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 and machine learning applications in rectal cancer: Current update and future perspectives
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti and Simone Maurea |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Valeria Romeo, MD, Academic Research, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Sergio Pansini 5, Naples 80131, Italy. valeria.romeo@unina.it |
| Key Words |
Rectal cancer; Radiomics; Radiogenomics; Artificial intelligence; Machine learning; Deep learning |
| Core Tip |
Rectal cancer is a common malignancy requiring a multidisciplinary approach to ensure the best clinical management. Diagnostic imaging has contributed to increase survival rates providing crucial information in the pathway of rectal cancer patients. Artificial intelligence, and in particular radiomics and machine learning, are promising techniques that could further enhance the value of medical imaging allowing to build decision support tools based on quantitative data. We herein present and discuss the potential role of artificial intelligence in rectal cancer applied to different medical imaging modalities. |
| Publish Date |
2021-08-25 02:11 |
| Citation |
Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321 |
| URL |
https://www.wjgnet.com/1007-9327/full/v27/i32/5306.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v27.i32.5306 |
© 2004-2024 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
