A Multimodal fusion model for predicting nasopharyngeal necrosis after re-irradiation in recurrent nasopharyngeal carcinoma.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China; School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518107, China; Cancer Hospital Chinese Academy of Medical Sciences, ShenZhen Center, Shenzhen, 518109, China.
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
- Manteia Technologies Co., Ltd, Xiamen, 361008, China.
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, 350000, China.
- Department of Radiotherapy, First Medical Center of PLA General Hospital, BeiJing, 100853, China. Electronic address: [email protected].
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. Electronic address: [email protected].
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. Electronic address: [email protected].
Abstract
To construct a multimodal model integrating computed tomography (CT) imaging, radiotherapy dose, and clinical features to address the clinical challenge of accurately predicting the risk of nasopharyngeal necrosis following re-irradiation for recurrent nasopharyngeal carcinoma (NPC). This retrospective study included 126 patients with recurrent NPC (44 with necrosis and 82 without necrosis). Clinical baseline characteristics, radiomic features from planning CT images, and three-dimensional radiotherapy dose-derived radiomic features were collected and extracted. A two-stage feature selection strategy was employed to identify relevant features. Three single-modality and four multimodal fusion machine learning models were developed based on the selected features. Model performance was evaluated using internal (n = 26) and external (n = 9) test sets, with assessment metrics including the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The multimodal fusion model demonstrated overall superior performance compared to all single-modality models. The CT-dose fusion model achieved AUCs of 0.869 and 0.972 in the internal and external test sets, respectively, outperforming the single CT, single-dose, and single clinical models. An artificial intelligence model integrating CT radiomic features and radiotherapy dose-derived characteristics demonstrated robust performance in predicting nasopharyngeal necrosis following re-irradiation for recurrent NPC, exhibiting stable predictive ability and strong generalizability. This model provides a powerful tool for the early identification of high-risk patients, with considerable promise for clinical application.