背景 11C-蛋氨酸是一种有用的正电子发射断层扫描（PET）放射性示踪剂，用于髓质瘤患者的管理，但辐射暴露和缺乏分子成像设备限制了其使用。目的 通过基于人工智能（AI）的图像转换模型，从增强对比度（CE）MRI生成合成蛋氨酸PET图像，并与真实PET的性能进行髓质瘤分级和预后的比较。 材料和方法 使用在2007年1月至2018年12月期间在一所大学医院接受蛋氨酸PET和CE MRI检查的患者（机构数据集）开发和验证了一个基于AI的模型，用于从CE MRI生成合成蛋氨酸PET图像。计算了合成和真实PET之间最大肿瘤与背景比（TBRmax）和平均肿瘤与背景比（TBRmean）以及病变体积的皮尔逊相关系数。还使用了两个额外的开源髓质瘤数据库，其中包含术前CE MRI但无蛋氨酸PET。使用TBR值评估高级和低级髓质瘤的分类和整体生存的受试者工作特征曲线下面积（AUC）。 结果 机构数据集包括362名患者（年龄平均值为49岁±19 [标准差]；女性195名，男性167名；训练组n = 294，验证组n = 34，测试组n = 34）。在内部测试集中，TBRmax、TBRmean和病变体积的皮尔逊相关系数分别为0.68（95％CI：0.47, 0.81），0.76（95％CI：0.59, 0.86）和0.92（95％CI：0.85, 0.95）。外部测试集共包括344名髓质瘤患者（年龄平均值为53岁±15；男性192名，女性152名；高级别n = 269）。TBRmax的AUC为0.81（95％CI：0.75, 0.86），整体生存分析显示高TBRmax组（2年生存率27％）和低TBRmax组（2年生存率71％）之间存在显着差异（P < 0.001）。结论 基于AI的模型生成的合成蛋氨酸PET图像与真实PET图像高度相关，并且在髓质瘤分级和预后方面表现良好。以CC BY 4.0许可发布。本文有附加资料可供参考。

Background Carbon 11 (11C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBRmax and TBRmean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBRmax, TBRmean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBRmax was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBRmax groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.