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Hematologic toxicity, although often transient, is the most common limiting adverse effect during somatostatin peptide receptor radionuclide therapy. This study investigated the association between Monte Carlo-derived absorbed dose to the red marrow (RM) and hematologic toxicity in patients being treated for their neuroendocrine tumors. Methods: Twenty patients each receiving 4 treatment cycles of [ 177 Lu]Lu-DOTATATE were included. Multiple-time-point 177 Lu SPECT/CT imaging-based RM dosimetry was performed using an artificial intelligence-driven workflow to segment vertebral spongiosa within the field of view (FOV). This workflow was coupled with an in-house macroscale/microscale Monte Carlo code that incorporates a spongiosa microstructure model. Absorbed dose estimates to RM in lumbar and thoracic vertebrae within the FOV, considered as representations of the whole-body RM absorbed dose, were correlated with hematologic toxicity markers at about 8 wk after each cycle and at 3- and 6-mo follow-up after completion of all cycles. Results: The median of absorbed dose to RM in lumbar and thoracic vertebrae within the FOV ( D median,vertebrae ) ranged from 0.019 to 0.11 Gy/GBq. The median of cumulative absorbed dose across all 4 cycles was 1.3 Gy (range, 0.6-2.5 Gy). Hematologic toxicity was generally mild, with no grade 2 or higher toxicity for platelets, neutrophils, or hemoglobin. However, there was a decline in blood counts over time, with a fractional value relative to baseline at 6 mo of 74%, 97%, 57%, and 97%, for platelets, neutrophils, lymphocytes, and hemoglobin, respectively. Statistically significant correlations were found between a subset of hematologic toxicity markers and RM absorbed doses, both during treatment and at 3- and 6-mo follow-up. This included a correlation between the platelet count relative to baseline at 6-mo follow up: D median,vertebrae ( r = -0.64, P = 0.015), D median,lumbar ( r = -0.72, P = 0.0038), D median,thoracic ( r = -0.58, P = 0.029), and D average,vertebrae ( r = -0.66, P = 0.010), where D median,lumbar and D median,thoracic are median absorbed dose to the RM in the lumbar and thoracic vertebrae, respectively, within the FOV and D average,vertebrae is the mass-weighted average absorbed dose of all vertebrae. Conclusion: This study found a significant correlation between image-derived absorbed dose to the RM and hematologic toxicity, including a relative reduction of platelets at 6-mo follow up. These findings indicate that absorbed dose to the RM can potentially be used to understand and manage hematologic toxicity in peptide receptor radionuclide therapy., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

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 [ 177 Lu]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., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in 177 Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one post-therapy SPECT/CT scan. Second, local contour intensity-based SPECT--SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point 177 Lu SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2%-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment, and the voxel-level fitting had high R 2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2-10.4) for lesions, 0.49 (range, 0.24-1.02) for left kidney, 0.54 (range, 0.31-1.07) for right kidney, and 0.51 (range, 0.27-1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%-17%) for organs and 10% (range, 3%-37%) for lesions. For a typical patient, the time for the entire process was about 25 min (∼2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)