用于[177Lu]Lu-DOTATATE后剂量测定的单时间点成像:降低时间点选择敏感性的现有方法和新型数据驱动模型的准确性。

IF 9.1 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Nuclear Medicine Pub Date : 2023-09-01 Epub Date: 2023-07-27 DOI:10.2967/jnumed.122.265338
Chang Wang, Avery B Peterson, Ka Kit Wong, Molly E Roseland, Matthew J Schipper, Yuni K Dewaraja
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引用次数: 0

摘要

通过单个时间点(STP)的成像估算剂量测定的时间积分活动(TIA)有助于剂量测定引导的放射性药物治疗的临床转化。然而,根据时间点的选择,STP 方法估算 TIA 的准确性各不相同。我们构建了患者数据驱动的回归模型,以降低对时间点选择的敏感性,并将这些新模型与常用的 STP 方法进行比较。方法:在[177Lu]Lu-DOTATATE治疗第一周期后的时间段(TP)1(3-5 h)、TP2(第1-2天)、TP3(第3-5天)和TP4(第6-8天)进行SPECT/CT,共涉及27名患者,100个分段肿瘤和54个肾脏。受 Madsen 等人和 Hänscheid 等人之前基于物理学的 STP 模型的影响,我们以 SPECT 数据驱动的方式构建了 STP 预测表达式 TIA = A(t) × g(t)(模型 1),其中 A(t) 是成像时间 t 时观察到的活动度,而曲线 g(t) 是用非参数广义加法模型估算的,方法是最小化相对于 4 时间点 SPECT 得出的 TIA(参考 TIA)的归一化均方误差。此外,我们还拟合了一个广义加法模型,该模型除了单次活动测量外,还将基线生物标志物作为辅助数据(模型 2)。我们使用预测 TIA 与参考 TIA 之间的平均绝对误差 (MAE) 和均方误差对 STP 模型进行了一出交叉验证。结果:在第 3-5 天,所有经过评估的 STP 方法都表现出色,MAE 均小于 7%(患者之间的均方误差为结论的 7%):与 Hänscheid 方法相比,Madsen 方法和建议的数据驱动模型对 TP 选择的敏感性较低。在最实用的最早 TP 阶段,包含基线生物标记物的模型优于其他仅依赖单一活动测量的方法。
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Single-Time-Point Imaging for Dosimetry After [177Lu]Lu-DOTATATE: Accuracy of Existing Methods and Novel Data-Driven Models for Reducing Sensitivity to Time-Point Selection.

Estimation of the time-integrated activity (TIA) for dosimetry from imaging at a single time point (STP) facilitates the clinical translation of dosimetry-guided radiopharmaceutical therapy. However, the accuracy of the STP methods for TIA estimation varies on the basis of time-point selection. We constructed patient data-driven regression models to reduce the sensitivity to time-point selection and to compare these new models with commonly used STP methods. Methods: SPECT/CT performed at time period (TP) 1 (3-5 h), TP2 (days 1-2), TP3 (days 3-5), and TP4 (days 6-8) after cycle 1 of [177Lu]Lu-DOTATATE therapy involved 27 patients with 100 segmented tumors and 54 kidneys. Influenced by the previous physics-based STP models of Madsen et al. and Hänscheid et al., we constructed an STP prediction expression, TIA = A(t) × g(t), in a SPECT data-driven way (model 1), in which A(t) is the observed activity at imaging time t, and the curve, g(t), is estimated with a nonparametric generalized additive model by minimizing the normalized mean square error relative to the TIA derived from 4-time-point SPECT (reference TIA). Furthermore, we fit a generalized additive model that incorporates baseline biomarkers as auxiliary data in addition to the single activity measurement (model 2). Leave-one-out cross validation was performed to evaluate STP models using mean absolute error (MAE) and mean square error between the predicted and reference TIA. Results: At days 3-5, all evaluated STP methods performed very well, with an MAE of less than 7% (between-patient SD of <10%) for both kidneys and tumors. At other TPs, the Madsen method and data-driven models 1 and 2 performed reasonably well (MAEs < 17% for kidneys and < 32% for tumors), whereas the error with the Hänscheid method was substantially higher. The proof of concept of adding baseline biomarkers to the prediction model was demonstrated and showed a moderate enhancement at TP1, especially for estimating kidney TIA (MAE ± SD from 15.6% ± 1.3% to 11.8% ± 1.0%). Evaluations on 500 virtual patients using clinically relevant time-activity simulations showed a similar performance. Conclusion: The performance of the Madsen method and proposed data-driven models is less sensitive to TP selection than is the Hänscheid method. At the earliest TP, which is the most practical, the model incorporating baseline biomarkers outperforms other methods that rely only on the single activity measurement.

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来源期刊
Journal of Nuclear Medicine
Journal of Nuclear Medicine 医学-核医学
CiteScore
13.00
自引率
8.60%
发文量
340
审稿时长
1 months
期刊介绍: The Journal of Nuclear Medicine (JNM), self-published by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), provides readers worldwide with clinical and basic science investigations, continuing education articles, reviews, employment opportunities, and updates on practice and research. In the 2022 Journal Citation Reports (released in June 2023), JNM ranked sixth in impact among 203 medical journals worldwide in the radiology, nuclear medicine, and medical imaging category.
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