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使用F-FDG PET/CT衍生的身体组成特征来预测非小细胞肺癌中淋巴血管入侵

Zewen Jiang^1,2, David Haberl^1,2, Clemens Spielvogel¹

¹Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.

European journal of nuclear medicine and molecular imaging

|July 23, 2025

概括

这项研究开发了一种非侵入性模型,用于预测非小细胞肺癌 (NSCLC) 中的淋巴血管入侵 (LVI),使用身体组成和PET/CT成像. 该模型改善了早期NSCLC患者的风险分层.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
医疗成像医学成像

背景情况:

淋巴血管侵袭 (LVI) 是非小细胞肺癌 (NSCLC) 的关键预后因素,与转移和复发风险增加有关.
准确预测LVI对于有效的治疗计划和患者风险分层至关重要.

研究的目的:

开发和验证早期NSCLC患者LVI的非侵入性预测模型.
评估从18F-FDG PET/CT成像中得出的身体构成特征在预测LVI时的有用性.

主要方法:

对248名NSCLC患者的回顾性分析 (153名培训,95名验证).
从18F-FDG PET/CT扫描中评估瘤代谢参数 (SUVmax,SUVmean,MTV,TLG) 和身体组成 (脂肪组织,骨肌肉).
开发和验证一个包含临床,代谢和身体组成特征的逻辑回归名录.

主要成果:

开发的nomogram,整合MTV,N阶段和身体组成,实现AUC为0.839 (训练) 和0.790 (验证).
诺米图显著优于仅使用临床或成像变量的模型.
预测的LVI与明显较差的无复发生存率和整体生存率有关.

结论:

将身体成分与PET/CT代谢和临床数据相结合,可以准确地在手术前预测NSCLC中的LVI.
这种方法增强了早期NSCLC患者的风险分层,可能引导治疗决策.

关键词:

18F-FDG 关于PET/CT 的研究身体组成身体组成淋巴血管侵入淋巴血管侵入在NSCLCLC中,我们可以看到.

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