在NET患者的SiTATE PET/CT成像中,不同的淋巴结转移划分方法的可行性[18F]
Sophie Carina Siegmund1,2, Magdalena Schöll3, Vera U Wenter3
1Department of Nuclear Medicine, LMU University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany. sophie.siegmund@med.uni-muenchen.de.
EJNMMI reports
|October 30, 2025
概括
45%的异界面值方法提供了最准确的瘤体积评估,用于胃肠道瘤神经内分泌瘤 (GEPNETs) 使用 somatostatin受体PET/CT扫描. 这种强大的方法改善了瘤划分,以更好地监测治疗.
科学领域:
- 核医学就是核医学.
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
背景情况:
- 精确的瘤划分对于使用体静止素受体 (SSTR) PET/CT 治疗胃肠道胰腺神经内分泌瘤 (GEPNETs) 至关重要.
- 目前用于量化瘤负担和评估治疗反应的方法需要基于SSTR PET的强大瘤划分.
研究的目的:
- 评估和确定在GEPNET中基于SSTR PET的瘤划分最可靠的方法.
- 为了比较不同的划界技术与淋巴结转移的参考标准.
主要方法:
- 从[18F]SiTATE PET/CT扫描中包括单一的,不庞大的淋巴结转移 (短轴直径≥1.0厘米).
- 应用各种划分方法:固定SUV值,与SUVmax相对的单边形,以及个别的向后值.
- PET衍生和CT衍生 (参考标准) 瘤体积之间的相关性分析.
主要成果:
- 与SUVmax相对的45%的异界面方法显示了与CT衍生体积的最强相关性 (r=0.874,r2=0.764,p<0.001).
- 这种方法的性能优于固定的SUV值 (例如,SUV 4.0:r=0.727,r2=0.529,p<0.001).
- 反向值方法表现出显著的变化 (CoV:固定SUV的59.6%,异界面的31.3%).
结论:
- 与损伤SUVmax相对的45%的同界面值是基于SSTR PET的GEPNET瘤划分的强大和临床适用的方法.
- 这种方法无论在肝脏等参考组织中SSTR热度如何,都是有效的.
- 需要进一步验证,以确认其在总瘤体积评估和治疗监测中的有用性.
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