International journal of radiation oncology, biology, physics · 2026
Privacy-preserving Virtual Contrast-enhanced MRI for Nasopharyngeal Carcinoma: A Multicenter Study.
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Li Wen, Li Zhen, Shi Yiming, Lam Saikit, Xiao Li, Cheung Anson Ho-Yin, Liu Chenbin, Liu Shupeng, Yang Jinping, Zeng Guangping, Yang Xiaojing, Lee Shara Wee Yee, Nicol Alexander James, Liu Zhou, Zou Liyan, Yin Hang, Li Xue, Liu Xiangchun, Sun Jiali, Li Jianhao, Zhu Die, Li Xue, Sun Rui, Li Bing, Sun Xueming, Ge Hong, Chen Jiamin, Li Chunming, Wu Xiaohui, Wang Xiaohua, Zhang Jiang, Liu Chenyang, Li Tian, Ren Ge, Teng Xinzhi, Zhi Shaohua, Cheung Andy Lai-Yin, Lee Francis Kar-Ho, Lee Victor Ho-Fun, Fu Jie, Cai Jing
Аннотация
PURPOSE: Contrast-enhanced magnetic resonance image (MRI) imaging via administration of contrast agents is critical for diagnosis, staging, and treatment of nasopharyngeal carcinoma (NPC). However, gadolinium-based contrast agents can lead to severe adverse effects, especially in patients with compromised kidney function, necessitating a safer alternative for contrast enhancement. In this study, we aim to develop and assess the clinical feasibility of a federated learning model for synthesizing virtual contrast-enhanced MRI (VCE-MRI) images from contrast-free scans for patients with NPC.
METHODS AND MATERIALS: In this multicenter, retrospective study, we developed and clinically evaluated a federated learning-based VCE-MRI synthesis model (FL-VCE-MRI) using pretreatment contrast-free T1-weighted and T2-weighted MRI scans. The model was trained using data from 14 centers involving 2061 patients. External validation was performed with an independent dataset from 25 centers comprising 126 patients. Additionally, 10 clinicians from 8 centers assessed the quality of the synthetic images.
RESULTS: The FL-VCE-MRI model demonstrated high generalizability in the external validation dataset, achieving a mean absolute error of 45.85 (95% CI, 43.73-47.96). For centers facing challenges in developing well-performing single-center models, the FL-VCE-MRI improved the average mean absolute error from 53.93 (95% CI, 49.21-58.65) to 45.93 (95% CI, 41.64-50.22). Clinical evaluations indicated that the synthesized VCE-MRI images are both reliable and clinically valuable, with no significant differences compared with gadolinium-enhanced MRI in disease diagnosis, tumor staging, and delineation.
CONCLUSIONS: The FL-VCE-MRI model shows potential as a noninvasive alternative to gadolinium-enhanced MRI for patients with NPC.
METHODS AND MATERIALS: In this multicenter, retrospective study, we developed and clinically evaluated a federated learning-based VCE-MRI synthesis model (FL-VCE-MRI) using pretreatment contrast-free T1-weighted and T2-weighted MRI scans. The model was trained using data from 14 centers involving 2061 patients. External validation was performed with an independent dataset from 25 centers comprising 126 patients. Additionally, 10 clinicians from 8 centers assessed the quality of the synthetic images.
RESULTS: The FL-VCE-MRI model demonstrated high generalizability in the external validation dataset, achieving a mean absolute error of 45.85 (95% CI, 43.73-47.96). For centers facing challenges in developing well-performing single-center models, the FL-VCE-MRI improved the average mean absolute error from 53.93 (95% CI, 49.21-58.65) to 45.93 (95% CI, 41.64-50.22). Clinical evaluations indicated that the synthesized VCE-MRI images are both reliable and clinically valuable, with no significant differences compared with gadolinium-enhanced MRI in disease diagnosis, tumor staging, and delineation.
CONCLUSIONS: The FL-VCE-MRI model shows potential as a noninvasive alternative to gadolinium-enhanced MRI for patients with NPC.