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基于人工智能的对比增强 MRI 甲硫氨酸 PET 虚拟合成:开发和外部验证研究。

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study.

机构信息

From the Department of Diagnostic and Interventional Radiology, Graduate School of Medicine (H. Takita, T.M., H. Tatekawa, Y. Mitsuyama, S.L.W., Y. Miki, D.U.), Smart Life Science Laboratory, Center for Health Science Innovation (T.M., D.U.), and Department of Neurosurgery, Graduate School of Medicine (K.N., T.U.), Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; and Department of Radiology, Osaka Metropolitan University Hospital, Osaka, Japan (Y.K.).

出版信息

Radiology. 2023 Aug;308(2):e223016. doi: 10.1148/radiol.223016.

Abstract

Background Carbon 11 (C)-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 (TBR and TBR, 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, = 294; validation, = 34; test, = 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 TBR, TBR, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, = 269). The AUC for TBR 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%; < .001) TBR 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.

摘要

背景

碳 11(C)-蛋氨酸是一种用于管理神经胶质瘤患者的有用的正电子发射断层扫描(PET)放射性示踪剂,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)的图像到图像翻译模型从对比增强(CE)磁共振成像(MRI)生成合成蛋氨酸 PET 图像,并将其用于分级和预测神经胶质瘤的性能与真实 PET 进行比较。材料和方法:从 2007 年 1 月至 2018 年 12 月在一家大学医院接受蛋氨酸 PET 和 CE MRI 检查的患者中开发和验证了一种基于 AI 的模型,以从 CE MRI 生成合成蛋氨酸 PET 图像(机构数据集)。计算了摄取蛋氨酸的最大和平均肿瘤与背景比(TBR 和 TBR)和合成与真实 PET 之间病变体积的 Pearson 相关系数。使用两个额外的无蛋氨酸 PET 的术前 CE MRI 的开源神经胶质瘤数据库作为外部测试集。使用 TBRs,评估了用于分类高级别和低级别神经胶质瘤以及总体生存的受试者工作特征曲线(AUC)下的面积。结果:机构数据集包括 362 名患者(平均年龄,49 岁±19[SD];195 名女性,167 名男性;训练, = 294;验证, = 34;测试, = 34)。在内部测试集中,Pearson 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),用于 TBR、TBR 和病变体积。外部测试集包括 344 名神经胶质瘤患者(平均年龄,53 岁±15;192 名男性,152 名女性;高级别, = 269)。TBR 的 AUC 为 0.81(95%CI:0.75,0.86),总体生存分析显示 TBR 较高(2 年生存率为 27%)和较低(2 年生存率为 71%; <.001)两组之间存在显著差异。结论:基于 AI 的模型生成的合成蛋氨酸 PET 图像与真实 PET 图像具有很强的相关性,在神经胶质瘤分级和预后预测方面表现出良好的性能。根据 CC BY 4.0 许可发布。

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