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基于人工智能的美因PET的虚拟合成来自对比度增强的MRI:开发和外部验证研究

Hirotaka Takita¹, Toshimasa Matsumoto¹, Hiroyuki Tatekawa¹

¹From 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.).

|August 1, 2023

概括

人工智能 (AI) 现在可以从MRI扫描中生成合成氨酸PET图像,通过克服传统PET成像的局限性,为质瘤管理提供了一个有希望的替代方案.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
在瘤学瘤学.

背景情况:

11C-氨酸PET对于质瘤的治疗有价值,但由于辐射暴露和设施的可用性,它面临着限制.
开发替代成像方法对于更广泛的临床应用至关重要.

研究的目的:

使用基于AI的模型从对比度增强 (CE) 的MRI创建合成氨酸PET图像.
与真实PET相比,评估这些合成PET图像对质瘤分级和预后的性能.

主要方法:

一个人工智能图像到图像翻译模型被开发和验证,使用的是氨酸PET和CEMRI患者的数据集.
皮尔森相关系数计算了瘤与背景比率 (TBR_max,TBR_mean) 和病变体积.
对开源的质瘤数据集进行了外部验证,使用接收器运行特征曲线分析进行分级和生存分析进行预后.

主要成果:

在内部测试组中,合成PET图像与真实PET图像的TBR_max (0.68),TBR_mean (0.76) 和损伤体积 (0.92) 显示出强烈的相关性.
在外部测试组中,人工智能模型实现了0.81的AUC,用于在外部测试组中分类高级与低级质瘤.
从合成PET图像中获得的高TBR_max和低TBR_max组之间观察到总生存时间的显著差异.

结论:

人工智能生成的合成氨酸PET图像密切模仿真实PET发现.
人工智能模型在质瘤分级和预后方面表现强,为传统PET成像的局限性提供了潜在的解决方案.

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