Department of Biostatistics, University of Michigan, Ann Arbor, Michigan;
Department of Radiology, University of Michigan, Ann Arbor, Michigan; and.
J Nucl Med. 2021 Aug 1;62(8):1118-1125. doi: 10.2967/jnumed.120.256255. Epub 2020 Dec 18.
Multiple-time-point SPECT/CT imaging for dosimetry is burdensome for patients and lacks statistical efficiency. A novel method for joint kidney time-activity estimation based on a statistical mixed model, a prior cohort of patients with complete time-activity data, and only 1 or 2 imaging points for new patients was compared with previously proposed single-time-point methods in virtual and clinical patient data. Data were available for 10 patients with neuroendocrine tumors treated with Lu-DOTATATE and imaged up to 4 times between days 0 and 7 using SPECT/CT. Mixed models using 1 or 2 time points were evaluated retrospectively in the clinical cohort, using the multiple-time-point fit as the reference. Time-activity data for 250 virtual patients were generated using parameter values from the clinical cohort. Mixed models were fit using 1 (∼96 h) and 2 (4 h, ∼96 h) time points for each virtual patient combined with complete data for the other patients in each dataset. Time-integrated activities (TIAs) calculated from mixed model fits and other reduced-time-point methods were compared with known values. : All mixed models and single-time-point methods performed well overall, achieving mean bias < 7% in the virtual cohort. Mixed models exhibited lower bias, greater precision, and substantially fewer outliers than did single-time-point methods. For clinical patients, 1- and 2-time-point mixed models resulted in more accurate TIA estimates for 94% (17/18) and 72% (13/18) of kidneys, respectively. In virtual patients, mixed models resulted in more than a 2-fold reduction in the proportion of kidneys with |bias| > 10% (6% vs. 15%). Mixed models based on a historical cohort of patients with complete time-activity data and new patients with only 1 or 2 SPECT/CT scans demonstrate less bias on average and significantly fewer outliers when estimating kidney TIA, compared with popular reduced-time-point methods. Use of mixed models allows for reduction of the imaging burden while maintaining accuracy, which is crucial for clinical implementation of dosimetry-based treatment.
多次点 SPECT/CT 成像进行剂量测定对患者来说负担很重，而且缺乏统计学效率。一种新的基于统计混合模型、具有完整时间活动数据的先前队列患者和新患者仅 1 或 2 个成像点的联合肾脏时间活动估计新方法与先前提出的单次点方法在虚拟和临床患者数据中进行了比较。10 名神经内分泌肿瘤患者接受 Lu-DOTATATE 治疗，在第 0 天至第 7 天之间使用 SPECT/CT 进行了多达 4 次成像，可获得这些患者的数据。在临床队列中，使用多次点拟合作为参考，对使用 1 个或 2 个时间点的混合模型进行了回顾性评估。使用来自临床队列的参数值为 250 个虚拟患者生成时间活动数据。对于每个虚拟患者，使用 1 个（约 96 小时）和 2 个（4 小时，约 96 小时）时间点拟合混合模型，并与每个数据集的其他患者的完整数据相结合。从混合模型拟合和其他减少时间点的方法计算的时间积分活动（TIA）与已知值进行比较。所有混合模型和单点方法总体上表现良好，在虚拟队列中平均偏差<7%。混合模型的偏差较小，精度较高，异常值明显少于单点方法。对于临床患者，1 点和 2 点混合模型分别导致 94%（17/18）和 72%（13/18）的肾脏 TIA 估计值更准确。在虚拟患者中，混合模型导致具有|偏差|>10%（6%比 15%）的肾脏比例降低了两倍多。基于具有完整时间活动数据的历史队列患者和仅进行 1 或 2 次 SPECT/CT 扫描的新患者的混合模型在估计肾脏 TIA 时平均偏差较小，异常值明显较少，与流行的减少时间点方法相比。使用混合模型可以减少成像负担，同时保持准确性，这对于基于剂量测定的治疗的临床实施至关重要。
