RT Journal Article SR Electronic T1 A novel time-activity information sharing approach using nonlinear mixed models for patient-specific dosimetry with reduced imaging time points: application in SPECT/CT imaging post-177Lu-DOTATATE JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP jnumed.120.256255 DO 10.2967/jnumed.120.256255 A1 Theresa Devasia A1 Yuni K. Dewaraja A1 Kirk A Frey A1 Ka Kit Wong A1 Matthew J Schipper YR 2020 UL http://jnm.snmjournals.org/content/early/2020/12/18/jnumed.120.256255.abstract AB 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 one or two imaging points for new patients was compared to previously proposed single time point methods in virtual and clinical patient data. Methods: Data were available for ten patients with neuroendocrine tumors treated with Lu-177 DOTATATE and imaged up to four times between days 0 and 7 using SPECT/CT. Mixed models using one or two 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 one (~96h) and two (4h, ~96h) time points for each virtual patient combined with complete data for the other patients in each data set. Time-integrated activities (TIAs) calculated from mixed model fits and other reduced time point methods were compared to 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 compared to single time point methods. For clinical patients, one and two time point mixed models resulted in more accurate TIA estimates for 94% (17/18) and 72% (13/18) of kidneys, respectively, but should be further validated in a larger cohort. In virtual patients, mixed models resulted in more than a two-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 one or two 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.