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Anni Gålne^1,2,3, Olof Enqvist^4,5, Anna Sundlöv⁶

¹Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden. anni.galne@med.lu.se.

European journal of hybrid imaging

|August 6, 2023

概括

开发了一种人工智能 (AI) 模型,自动从PET/CT扫描中对全身索马托斯坦素受体表达瘤体积 (SRETVwb) 和总损伤索马托斯坦素受体表达 (TLSREwb) 进行细分. 这种人工智能工具准确量化瘤负担,改善疾病评估和预后.

科学领域:

核医学是一种核医学.
无线电学 (Radiomics) 是一种辐射学.
在瘤学中使用人工智能

背景情况:

在PET/CT上对全身索马托斯坦素受体表达瘤体积 (SRETVwb) 的细分是预后性的,但耗时.
自动化SRETVwb测量可以提高疾病评估和预后.
这项研究的重点是开发一种AI方法来定量SRETVwb和总损伤索马托斯塔丁受体表达 (TLSREwb).

研究的目的:

开发和验证基于人工智能的方法,用于SRETVwb和TLSREwb的自动细分和量化.
评估人工智能模型与医生手动细分的性能.
建立一个可靠的工具,以改善PET/CT成像中的瘤负担评估.

主要方法:

使用UNet3D卷积神经网络 (CNN) 来训练AI模型.
培训数据包括来自148名患者的[68Ga]Ga-DOTA-TOC/TATE PET/CT图像.
人工智能模型的性能与来自两个医生的手动细分对30名患者的测试组进行了评估.

主要成果:

人工智能模型与SRETVwb (r=0.78) 和TLSREwb (r=0.83) 的医生细分表现出良好的相关性.
对于TLSREwb的斯皮尔曼等级相关系数也很高 (r=0.73和r=0.81).
与医生相比,损伤检测灵敏度为80-79%,积极预测值为83-84%.

结论:

人工智能模型可以有效地从高性能PET/CT图像中分割SRETVwb和TLSREwb.
完全自动化量化瘤负担是可以实现的,提供了一个潜在的更广泛适用的工具.
这种人工智能方法可以显著改善对PET/CT图像的评估,用于疾病的分期和监测.

关键词:

在这里,我们可以看到AIAIAI.聚乙烯/聚乙烯/聚乙烯索马托斯坦丁受体表达的瘤体积 [68Ga]Ga-DOTA-TATEATE的时间是68Ga的时间.[68Ga]Ga-DOTA-TOC-TOC的时间

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