Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2024; 16(10): 4146-4156
Published online Oct 15, 2024. doi: 10.4251/wjgo.v16.i10.4146
Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method
Huai-Wen Zhang, You-Hua Wang, Bo Hu, Hao-Wen Pang
Huai-Wen Zhang, Department of Radiotherapy, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi Province, China
You-Hua Wang, Department of Oncology, Gulin People’s Hospital, Luzhou 646500, Sichuan Province, China
Bo Hu, Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hang Kong University, Nanchang 330063, Jiangxi Province, China
Hao-Wen Pang, Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
Co-first authors: Huai-Wen Zhang and You-Hua Wang.
Co-corresponding authors: Bo Hu and Hao-Wen Pang.
Author contributions: Zhang HW, Hu B and Pang HW conceptualized and designed the research. Wang YH and Pang HW screened patients and acquired clinical data. Zhang HW, and Pang HW performed Data analysis. Zhang HW, Hu B and Pang HW wrote the paper; All the authors have read and approved the final manuscript. Zhang HW proposed, designed and conducted collection of clinical data, performed data analysis and prepared the first draft of the manuscript. Wang YH was responsible for patient screening, enrollment, collection of clinical data. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Hu B and Pang HW have played important and indispensable roles in the project design, data interpretation and manuscript preparation as the co-corresponding authors. Hu B conceptualized, designed, and supervised the whole process of the project. Pang HW applied for and obtained the funds for this research project. Pang HW was responsible for data re-analysis and re-interpretation, figure plotting, comprehensive literature search, preparation and submission of the current version of the manuscript. This collaboration between Hu B and Pang HW is crucial for the publication of this manuscript and other manuscripts still in preparation.
Supported by the Open Fund for Scientific Research of Jiangxi Cancer Hospital, No. 2021J15; the Gulin People's Hospital-The Affiliated Hospital of Southwest Medical University Science and Technology Strategic Cooperation Project, No. 2022GLXNYDFY05; and the Sichuan Provincial Medical Research Project Plan, No. S21004.
Institutional review board statement: This retrospective study was approved by the institutional review board of Jiangxi Cancer Hospital, No. 2024ky057.
Informed consent statement: Consent for publication is not applicable in this study, because there is not any individual person’s data.
Conflict-of-interest statement: Dr. Pang has a patent ZL201610529290.8 licensed.
Data sharing statement: All data generated and analyzed during this study are included in this published article.
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/
Corresponding author: Hao-Wen Pang, MA, MD, Doctor, Department of Oncology, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Luzhou 646000, Sichuan Province, China. haowenpang@foxmail.com
Received: July 12, 2024
Revised: August 19, 2024
Accepted: September 5, 2024
Published online: October 15, 2024
Processing time: 76 Days and 4.9 Hours
Core Tip

Core Tip: In this study, a neural network prediction model for the uninvolved liver dose was established using the MATLAB neural network application. The regression R-value and mean square error (MSE) were used to evaluate the model. All R-values for Dn10-Dn100 and Dnmean were > 0.8, except for Dn0, which was 0.7513, respectively. The MSE of the prediction model was also very low.