Dose information prediction for radiation treatment planning of nasopharyngeal carcinoma based on artificial neural networks
Pages 383 - 387
Abstract
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 prediction model based on artificial neural network with OVH information as input and radiation treatment planning dose information as output, 23 cases were used to test the accuracy of the prediction model. Results: For the 11 main OAR dosimetric parameters of nasopharyngeal cancer, the overall mean difference between predicted and actual values was -0.07±4.55 Gy for dose-related and -1.06±3.80% for volume-related, with a prediction accuracy of 90%. Conclusions: Dose prediction model based on artificial neural network can predict the dose information of radiation treatment planning for nasopharyngeal cancer accurately.
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Index Terms
- Dose information prediction for radiation treatment planning of nasopharyngeal carcinoma based on artificial neural networks
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Published In
October 2021
593 pages
ISBN:9781450395588
DOI:10.1145/3500931
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Published: 22 December 2021
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ISAIMS 2021
ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences
October 29 - 31, 2021
Beijing, China
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