Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method

doi:10.4251/wjgo.v16.i10.4146

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Oct 15, 2024 (publication date) through Feb 13, 2026

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World Journal of Gastrointestinal Oncology

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1948-5204

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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.

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

Abstract

BACKGROUND

The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.

AIM

To predict the uninvolved liver dose in stereotactic body radiotherapy (SBRT) for liver cancer using a neural network-based method.

METHODS

A total of 114 SBRT plans for liver cancer were used to test the neural network method. Sub-organs of the uninvolved liver were automatically generated. Correlations between the volume of each sub-organ, uninvolved liver dose, and neural network prediction model were established using MATLAB. Of the cases, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression R-value and mean square error (MSE) were used to evaluate the model.

RESULTS

The volume of the uninvolved liver was related to the volume of the corresponding sub-organs. For all sets of R-values of the prediction model, except for D_n0 which was 0.7513, all R-values of D_n10-D_n100 and D_nmean were > 0.8. The MSE of the prediction model was also low.

CONCLUSION

We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer. It is simple and easy to use and warrants further promotion and application.

Keywords: Dose prediction; Sub-organ; Machine learning; Stereotactic body radiotherapy; Liver cancer

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 D_n10-D_n100 and D_nmean were > 0.8, except for D_n0, which was 0.7513, respectively. The MSE of the prediction model was also very low.