Dose information prediction for radiation treatment planning of nasopharyngeal carcinoma based on artificial neural networks

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成果类型：

会议论文

作者：

Chen, Yanyu;Bai, Penggang;Qiu, Xiaoping

通讯作者：

Chen, Yanyu(857739933@qq.com)

作者机构：

[Qiu, Xiaoping] School of Nuclear Science and Technology, University of South China, Hunan, Hengyang, China

Department of Radiation Oncology, Fujian Cancer Hospital, Fujian, Fuzhou, China

[Chen, Yanyu] School of Nuclear Science and Technology, University of South China, Hunan, Hengyang, China<&wdkj&>Department of Radiation Oncology, Fujian Cancer Hospital, Fujian, Fuzhou, China<&wdkj&>Department of Radiation Oncology, Fujian Cancer Hospital, Fujian, Fuzhou, China

[Bai, Penggang] Department of Radiation Oncology, Fujian Cancer Hospital, Fujian, Fuzhou, China<&wdkj&>Department of Radiation Oncology, Fujian Cancer Hospital, Fujian, Fuzhou, China

语种：

英文

关键词：

Diseases;Forecasting;Radiation;Radiotherapy;Artificial neural network modeling;Dose information prediction;Model-based OPC;Nasopharyngeal cancer;Nasopharyngeal carcinoma;Organs at risks;Prediction modelling;Radiation treatment planning;Retrospective analysis;Spatial relationships;Neural networks

期刊：

ACM International Conference Proceeding Series

年：

2021

页码：

383-387

会议名称：

2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

会议时间：

29 October 2021 through 31 October 2021

会议地点：

Virtual, Online, China

出版者：

Association for Computing Machinery

ISBN：

9781450395588

DOI：

10.1145/3500931.3500997

基金类别：

The authors would like to thank Quan Kerun of Xiangtan Central Hospital for helpful discussions related to this work. This research is supported by Natural Science Foundation of Fujian Province of China (NO.2015Y0010), Fujian Provincial Health Science and Technology Project (NO.2018-ZQN-19) and Startup Fund for scientific research, Fujian Medical University (NO.2019QH1193).

机构署名：

本校为第一机构

院系归属：

核科学技术学院

摘要：

Objective: An artificial neural network model is used to predict the dose information of radiation treatment planning for nasopharyngeal carcinoma. Methods: Retrospective analysis of 114 cases of nasopharyngeal carcinoma treated with radiation therapy in Fujian Cancer Hospital. The geometric spatial relationship features between the 25 organs at risk (OAR) and the target area were transformed from three-dimensional information to two-dimensional information by overlap volume histogram (OVH). 81 cases were used for training to obtain an dose pre...

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Dose information prediction for radiation treatment planning of nasopharyngeal carcinoma based on artificial neural networks

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