A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion
Abstract Objectives Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume primary (GTVp) channel and parotid glands...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01848-1 |
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| author | Lin Lin Yuchen Ren Wanwei Jian Geng Yang Bailin Zhang Lin Zhu Wenhao Zhao Haoyu Meng Xuetao Wang Qiang He |
| author_facet | Lin Lin Yuchen Ren Wanwei Jian Geng Yang Bailin Zhang Lin Zhu Wenhao Zhao Haoyu Meng Xuetao Wang Qiang He |
| author_sort | Lin Lin |
| collection | DOAJ |
| description | Abstract Objectives Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume primary (GTVp) channel and parotid glands (PGs) channel. Methods Retrospective data were collected from 180 head and neck cancer patients. Xerostomia was defined as xerostomia of grade ≥ 2 occurring in the 6th month of radiation therapy. The dataset was split into 137 cases (58.4% xerostomia, 41.6% non-xerostomia) for training and 43 (55.8% xerostomia, 44.2% non-xerostomia) for testing. XeroNet was composed of GNet, PNet, and a Naive Bayes decision fusion layer. GNet processed data from the GTVp channel (CT, dose distributions corresponding and the GTVp contours). PNet processed data from the PGs channel (CT, dose distributions and the PGs contours). The Naive Bayes decision fusion layer was used to integrate the results from GNet and PNet. Model performance was evaluated using accuracy, F-score, sensitivity, specificity, and area under the receiver operator characteristic curve (AUC). Results The proposed model achieved promising prediction results. The accuracy, AUC, F-score, sensitivity and specificity were 0.779, 0.858, 0.797, 0.777, and 0.782, respectively. Features extracted from the CT and dose distributions in the GTVp and PGs regions were used to construct machine learning models. However, the performance of these models was inferior to our method. Compared with recent studies on xerostomia prediction, our method also showed better performance. Conclusions The proposed model could effectively extract features from the GTVp and PGs channels, achieving good performance in xerostomia prediction. |
| format | Article |
| id | doaj-art-dd66bd39418e4a1b94b5fe01830c32cf |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-dd66bd39418e4a1b94b5fe01830c32cf2025-08-20T03:06:31ZengBMCBMC Medical Imaging1471-23422025-07-0125111110.1186/s12880-025-01848-1A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusionLin Lin0Yuchen Ren1Wanwei Jian2Geng Yang3Bailin Zhang4Lin Zhu5Wenhao Zhao6Haoyu Meng7Xuetao Wang8Qiang He9Guangdong Pharmaceutical UniversityThe Second Clinical College of Guangzhou University of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineGuangdong Pharmaceutical UniversityThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineGuangdong Pharmaceutical UniversityThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineAbstract Objectives Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume primary (GTVp) channel and parotid glands (PGs) channel. Methods Retrospective data were collected from 180 head and neck cancer patients. Xerostomia was defined as xerostomia of grade ≥ 2 occurring in the 6th month of radiation therapy. The dataset was split into 137 cases (58.4% xerostomia, 41.6% non-xerostomia) for training and 43 (55.8% xerostomia, 44.2% non-xerostomia) for testing. XeroNet was composed of GNet, PNet, and a Naive Bayes decision fusion layer. GNet processed data from the GTVp channel (CT, dose distributions corresponding and the GTVp contours). PNet processed data from the PGs channel (CT, dose distributions and the PGs contours). The Naive Bayes decision fusion layer was used to integrate the results from GNet and PNet. Model performance was evaluated using accuracy, F-score, sensitivity, specificity, and area under the receiver operator characteristic curve (AUC). Results The proposed model achieved promising prediction results. The accuracy, AUC, F-score, sensitivity and specificity were 0.779, 0.858, 0.797, 0.777, and 0.782, respectively. Features extracted from the CT and dose distributions in the GTVp and PGs regions were used to construct machine learning models. However, the performance of these models was inferior to our method. Compared with recent studies on xerostomia prediction, our method also showed better performance. Conclusions The proposed model could effectively extract features from the GTVp and PGs channels, achieving good performance in xerostomia prediction.https://doi.org/10.1186/s12880-025-01848-1Deep learningXerostomiaDecision fusion |
| spellingShingle | Lin Lin Yuchen Ren Wanwei Jian Geng Yang Bailin Zhang Lin Zhu Wenhao Zhao Haoyu Meng Xuetao Wang Qiang He A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion BMC Medical Imaging Deep learning Xerostomia Decision fusion |
| title | A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion |
| title_full | A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion |
| title_fullStr | A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion |
| title_full_unstemmed | A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion |
| title_short | A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion |
| title_sort | deep learning model for predicting radiation induced xerostomia in patients with head and neck cancer based on multi channel fusion |
| topic | Deep learning Xerostomia Decision fusion |
| url | https://doi.org/10.1186/s12880-025-01848-1 |
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