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Articles Published Processes
1/18/2025 8:12:09 AM | Browse: 40 | Download: 96
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Received |
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2024-09-29 18:27 |
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Peer-Review Started |
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2024-09-29 18:27 |
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To Make the First Decision |
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Return for Revision |
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2024-10-30 07:59 |
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Revised |
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2024-11-07 17:25 |
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Second Decision |
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2024-12-02 02:41 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-12-02 08:09 |
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Articles in Press |
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2024-12-02 08:09 |
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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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2024-12-17 09:09 |
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Publish the Manuscript Online |
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2025-01-18 08:12 |
| 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) 2024. 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 |
Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver 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, MD, MS, Assistant Professor, Research Scientist, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Suite 70100, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com |
| Key Words |
Liver cancer; Radiotherapy dosage; Dose prediction; Machine learning; Stereotactic body radiotherapy |
| Core Tip |
A study by Zhang et al developed a neural network-based predictive model for estimating doses to uninvolved liver tissue during stereotactic body radiation therapy (RT), representing a significant advancement in personalizing RT for liver cancer patients. The model demonstrated high predictive accuracy, with R-values exceeding 0.8, highlighting its potential to standardize dose estimation and improve patient safety by reducing biases. The study's relatively small patient cohort (114 patients) raises concerns about selection bias and limits the model's generalizability. Future research should involve larger multicenter cohorts and a more comprehensive cohort of patient characteristics to improve the generalizability of models and clinical relevance. Interdisciplinary collaboration among oncologists, data scientists, and radiation technologists is vital for improving predictive models and the efficacy and precision of cancer treatment. |
| Publish Date |
2025-01-18 08:12 |
| Citation |
<p>Krishnan A. Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer. <i>World J Gastrointest Oncol</i> 2025; 17(2): 101888</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i2/101888.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i2.101888 |
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