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使用机器学习即时单个时间点剂量计有效半衰期的表征

Carlos Vinícius Gomes^1,2,3, Yizhou Chen¹, Isabel Rauscher⁴

¹Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine

|March 20, 2025

概括

这项研究引入了即时单个时间点 (iSTP) 剂量测量,使用机器学习预测放射性药物治疗 (RPT) 治疗前数据的有效半衰期. 这种灵活的方法可以实现更快的剂量测量,从而有可能扩大RPT的可访问性.

科学领域:

核医学就是核医学.
医疗成像医学成像
放射性药物疗法是一种放射性药物疗法.

背景情况:

在放射性药物治疗 (RPT) 中,单次时间点 (STP) 剂量测量是方便的,但受到严格的时间要求的限制.
目前的STP方法在常规的临床环境中面临挑战,因为数据采集需要迟到.

研究的目的:

为RPT引入一种新的即时STP (iSTP) 剂量测量方法.
预测器官有效半衰期 (Teff) 使用机器学习对治疗前PET和临床数据.
为了使治疗后灵活和早期的剂量计获得.

主要方法:

开发了一种机器学习模型,使用治疗前PET/CT和22名患者的临床数据来预测脏,肝脏和脏的Teff.
应用模型来估计 [177Lu]Lu-PSMA I&T RPT 的时间整合活性和吸收剂量.
通过使用两个预测场景,比较 iSTP 剂量测量结果与多个时间点和 Hänscheid 方法.

主要成果:

该iSTP方法允许早期的治疗后时间点 (2,20,43,69小时) 用于剂量测量.
在第一个场景中,将2小时和20小时的数据汇总起来,与汉赛德方法相比,所有器官的时间整合活动的平均差异低于27%.
在仅使用初始PET/CT数据预测Teff后续周期时,没有观察到显著差异 (P>0.05).

结论:

初步结果表明,使用治疗前数据预测Teff的可行性,用于RPT后快速灵活的STP剂量测量.
拟议的iSTP方法显示了在更广泛的临床环境中加快剂量测量应用的潜力,包括门诊RPT.
这种方法可以提高RPT个性化剂量计的常规临床应用.

关键词:

[177Lu]Lu-PSMA I&T 公司剂量测量方法剂量测量方法机器学习是机器学习.放射性药物疗法是一种放射性药物疗法.一个单一的时间点.

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