A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with...

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. Methods: Data were available for 10 patients with neuroendocrine tumors treated with ¹⁷⁷Lu-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. Results: 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%). Conclusion: 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.