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Articles Published Processes
3/25/2025 4:18:19 PM | Browse: 10 | Download: 30
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Received |
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2024-10-14 16:18 |
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Peer-Review Started |
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2024-10-14 16:18 |
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To Make the First Decision |
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Return for Revision |
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2025-01-10 09:34 |
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Revised |
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2025-01-10 12:47 |
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Second Decision |
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2025-01-16 02:35 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-01-16 08:40 |
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Articles in Press |
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2025-01-16 08:40 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-01-21 10:25 |
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Publish the Manuscript Online |
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2025-03-25 16:18 |
| ISSN |
1948-5204 (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) 2025. 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 |
Oncology |
| Manuscript Type |
Letter to the Editor |
| Article Title |
Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Arunkumar Krishnan |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Arunkumar Krishnan, Assistant Professor, MD, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com |
| Key Words |
Rectal cancer; Liver metastases; Neoplasm; Metastasis; Machine learning; Magnetic resonance imaging; Radiomics |
| Core Tip |
In a recent study by Long et al, multiparametric magnetic resonance imaging and radiomics were utilized to anticipate the occurrence of metachronous liver metastasis in individuals newly diagnosed with rectal cancer. The random forest model, a predictive model component, demonstrated significant accuracy, achieving area under the curve values of 0.919 in the training cohort and 0.901 in the validation cohort, highlighting its potential for non-invasive risk assessment. By integrating radiomic features with clinical data, the model can support tailored treatment strategies and improve patient care. Nevertheless, it is important for future research to address methodological limitations, such as the exclusion of genomic markers, potential biases from the retrospective design, and the necessity for external validation across varied patient populations. Expanding the model to integrate multi-omic data and advanced imaging techniques has the potential to further its clinical significance and practicality. |
| Publish Date |
2025-03-25 16:18 |
| Citation |
<p>Krishnan A. Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer. <i>World J Gastrointest Oncol</i> 2025; 17(4): 102324</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i4/102324.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i4.102324 |
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