核磁共振成为监测鼻咽癌复发的常规检查，但其相对灵敏度较正电子发射断层扫描/计算机断层扫描（PET/CT）较低。我们旨在确定人工智能（AI）是否能成为MRI放射科医师的胜任的预检查员，并且AI辅助的MRI能否在灵敏度和特异性方面表现更好，甚至与PET/CT相当。该多中心研究于2009年9月至2020年10月在5家医院招募了6916名患者。在训练和测试队列中，开发了2.5D卷积神经网络诊断模型和nnU-Net轮廓模型，并用于独立预测和可视化内部和外部验证队列中患者的复发情况。我们评估了AI的受试者工作特征曲线下面积（AUC），并使用McNemar检验将AI与MRI和PET/CT在灵敏度和特异性方面进行比较。前瞻性队列被随机分为AI组和非AI组，并使用χ²检验比较它们的灵敏度和特异性。 AI模型在内部验证队列和外部验证队列分别实现了AUC为0.92和0.88，对应灵敏度为79.5%和74.3%，特异性为91.0%和92.8%。它的灵敏度与MRI相当（例如74.3%与74.7%，P = 0.89），但低于PET/CT的灵敏度（77.9%与92.0%，P < 0.0001），在相同的个体特异性下。AI模型具有中等精确度，中位数Dice相似系数为0.67。AI辅助的MRI提高了特异性（92.5%与85.0%，P = 0.034），在内部验证子队列中与PET/CT相当，并在外部验证子队列中增加了灵敏度（81.9%与70.8%，P = 0.021）。在1248名患者的前瞻性队列中，AI组的灵敏度高于非AI组（78.6%与67.3%，P = 0.23），尽管未达到显著性。在未来的随机对照试验中，每组需要3943名患者才能显示出统计学上的显著差异。 AI模型与经验丰富的放射科医师进行MRI相媲美，而经验丰富的放射科医师辅助的AI-辅助MRI与PET/CT相媲美。需要进行更大规模的随机对照试验以充分展示AI的益处。该研究得到了中山大学临床研究5010计划（2015020）、广东省基础与应用基础研究基金（2022A1515110356）和广州市科技计划（2023A04J1788）的支持。© 2023 The Author(s).

MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT.This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test.The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference.The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently.The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).© 2023 The Author(s).