ArticleWorld journal of gastrointestinal oncology2024
Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method.
Article in World journal of gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI.Radiation oncology (London, England) · 2025Article
- Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer.World journal of gastrointestinal oncology · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.
aimTo predict the uninvolved liver dose in stereotactic body radiotherapy (SBRT) for liver cancer using a neural network-based method.
methodsA 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
resultsThe volume of the uninvolved liver was related to the volume of the corresponding sub-organs. For all sets of
conclusionWe 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.
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