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甲状腺成像中的数字与模拟PET:PET指标和基于机器学习的高功能病变特征的比较 (DIGI-PET研究)

Luca Filippi¹, Francesco Bianconi², Cristina Ferrari³

¹Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier, 1, Rome, 00133, Italy. luca.filippi@uniroma2.it.

European journal of nuclear medicine and molecular imaging

|August 22, 2025

概括

数字PET/CT成像检测到比模拟系统更小的甲状腺病变. 对PET指标的机器学习分析可以帮助非侵入性地区分甲状腺瘤和增生.

科学领域:

核医学
医学成像
在医疗保健中的机器学习

背景情况:

甲状腺增生症的诊断依赖于对甲状腺腺瘤 (PA) 和甲状腺增生症 (PH) 的区别.
阳离子发射断层扫描/计算机断层扫描 (PET/CT) 提供了定量成像指标.
与模拟系统相比,数字PET/CT系统可以提供更好的病变检测.

研究的目的:

在超甲状腺症中比较数字和模拟PET/CT的定量PET衍生指标.
评估机器学习 (ML) 对PET定量参数的有用性,以区分PA和PH.

主要方法:

分析了PET的定量参数 (SUVmax,MTV,TBR,直径) 和生物化学标记.
使用曼·惠特尼U测试对数字和模拟PET/CT进行比较.
ML模型 (LASSO,决策树,Gaussian naive Bayes) 经过训练,可以使用协调的定量特征来区分PA和PH.

主要成果:

与模拟PET/CT相比,数字PET/CT检测出明显较小的病变 (MTV,直径).
甲状腺瘤 (PA) 病变的SUVmax和TBR明显高于甲状腺腺瘤 (PH) 病变.
鉴定PA的最佳值是SUVmax> 5. 89和TBR> 11. 5.
在LASSO ML模型中,AUC达到0. 811,准确度为79. 7%.

结论:

数字PET/CT在检测小甲状腺病变方面表现出卓越的性能.
除了PTH水平之外,对PET衍生指标的ML分析显示,对PA与PH的非侵入性差异化有希望.

关键词:

18F-胆人工智能自动分类数字PET/CT 甲状腺功能障碍代谢成像

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