Wuhan University Reports Findings in Head and Neck Cancer (In Vivo Epid-based Daily Treatment Error Identification for Volumetric-modulated Arc Therapy In Head and Neck Cancers With a Hierarchical Convolutional Neural Network: a Feasibility...).
In: Cancer Weekly, 2024-05-21, S. 1360-1360
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Researchers from Wuhan University in China have proposed a deep learning approach to classify errors in daily VMAT treatment of head and neck cancer patients. They used EPID dosimetry to analyze 146 arcs from 42 patients and simulated anatomical changes and setup errors in 17,820 EPID images for model training. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes, achieving high accuracy. The researchers concluded that the HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish treatment error categories, potentially aiding in head and neck cancer treatment adaptation. [Extracted from the article]
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Wuhan University Reports Findings in Head and Neck Cancer (In Vivo Epid-based Daily Treatment Error Identification for Volumetric-modulated Arc Therapy In Head and Neck Cancers With a Hierarchical Convolutional Neural Network: a Feasibility...).
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Zeitschrift: | Cancer Weekly, 2024-05-21, S. 1360-1360 |
Veröffentlichung: | 2024 |
Medientyp: | serialPeriodical |
ISSN: | 1071-7218 (print) |
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