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A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After ¹⁷⁷Lu-DOTATATE.

ABSTRACT: 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.

SUBMITTER: Devasia TP

PROVIDER: S-EPMC8833869 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After <sup>177</sup>Lu-DOTATATE.

Devasia Theresa P TP Dewaraja Yuni K YK Frey Kirk A KA Wong Ka Kit KK Schipper Matthew J MJ

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20201218 8

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. <b>Methods:</b> Data were available for 10 patients with neuroendocrine tumors treat ...[more]

PMID: 33443063

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Project description:PurposeFractionated peptide receptor radionuclide therapy (PRRT) with 177Lu-DOTATATE is increasingly applied as an effective treatment for patients with disseminated neuroendocrine tumors. In parallel to dose planning before external beam radiation therapy, dosimetry is also needed to optimize PRRT to the individual patient. Accordingly, absorbed doses to organs at risk need to be calculated during PRRT, based on serial measurements of radioactivity distribution utilizing SPECT/CT. The dosimetry should be based on as few measurements as possible, while still retaining reliable results. The main aim of the present work was to calculate the fractional contribution of the extrapolations of the curve fits for the absorbed dose calculations to the kidneys. The secondary aim was to study agreement between absorbed dose (AD) and the effective half-life (teff) for the kidneys, estimated by means of measurements at one or two time points, in comparison to our current method employing three time points.MethodsIn 777 patients with disseminated neuroendocrine tumors undergoing PRRT, SPECT/CT over the abdomen was acquired at 1, 4, and 7 days after 177Lu-DOTATATE infusion. The absorbed dose to the kidneys was calculated from SPECT/CT radioactivity distribution data, and the teff and fractional contributions of the extrapolations were estimated, utilizing data from one, two, and three time points, respectively.ResultsThe fractional contributions from extrapolations before day 1 measurement and after day 7 measurement were approximately 26% and 11%, respectively. The mean differences in absorbed dose, based on one, two, and three time points were small, but with high method dependence for individual patients. The differences in estimated teff were small when it was based on measurements at days 1 and 7, but high for days 1 and 4 time points.ConclusionWhen assessing simplifications of methods for calculation of the absorbed dose to the kidneys, it was of the uttermost importance to incorporate the fractional contribution for the extrapolations included in the reference method. Measurements at an early and a late time point were found most important. An intermediate measurement contributes with an idea of the goodness of the fit.

Project description:PurposeAccurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study.MethodsWe used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions).ResultsThe DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses.ConclusionA hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

Project description:BackgroundDead-time correction is required for accurate quantitative SPECT-based dosimetry in the context of personalised 177Lu radiopharmaceutical therapy. We aimed to evaluate the impact of applying dead-time correction on the reconstructed SPECT image versus on the acquisition projections before reconstruction.MethodsData from 16 SPECT/CT acquisitions of a decaying 177Lu-filled phantom (up to 20.75 GBq) and dual-timepoint SPECT/CT in 14 patients treated with personalised 177Lu peptide receptor radionuclide therapy were analysed. Dead time was determined based on the acquisition wide-spectrum count rate for each projection and averaged for the entire acquisition. Three dead-time correction methods (DTCMs) were used: the per-projection correction, where each projection was individually corrected before reconstruction (DTCM1, the standard of reference), and two per-volume methods using the average dead-time correction factor of the acquisition applied to all projections before reconstruction (DTCM2) or to the SPECT image after reconstruction (DTCM3). Relative differences in quantification were assessed for various volumes of interest (VOIs) on the phantom and patient SPECT images. In patients, the resulting dosimetry estimates for tissues of interest were also compared between DTCMs.ResultsBoth per-volume DTCMs (DTCM2 and DTCM3) were found to be equivalent, with VOI count differences not exceeding 0.8%. When comparing the per-volume post-reconstruction DTCM3 versus the per-projection pre-reconstruction DTCM1, differences in VOI counts and absorbed dose estimates did not exceed 2%, with very few exceptions. The largest absorbed dose deviation was observed for a kidney at 3.5%.ConclusionWhile per-projection dead-time correction appears ideal for QSPECT, post-reconstruction correction is an acceptable alternative that is more practical to implement in the clinics, and that results in minimal deviations in quantitative accuracy and dosimetry estimates, as compared to the per-projection correction.

Project description:BackgroundIndividualized dosimetry is recommended for [177Lu]Lu-PSMA radioligand therapy (RLT) which is resource-intensive and protocols are often not optimized. Therefore, a simulation study was performed focusing on the determination of efficient optimal sampling schedules (OSS) for renal and tumour dosimetry by investigating different numbers of time points (TPs).MethodsSampling schedules with 1-4 TPs were investigated. Time-activity curves of the kidneys and two tumour lesions were generated based on a physiologically based pharmacokinetic (PBPK) model and biokinetic data of 13 patients who have undergone [177Lu]Lu-PSMA I&T therapy. Systematic and stochastic noise of different ratios was considered when modelling time-activity data sets. Time-integrated activity coefficients (TIACs) were estimated by simulating the hybrid planar/SPECT method for schedules comprising at least two TPs. TIACs based on one single SPECT/CT measurement were estimated using an approximation for reducing the number of fitted parameters. For each sampling schedule, the root-mean-squared error (RMSE) of the deviations of the simulated TIACs from the ground truths for 1000 replications was used as a measure for accuracy and precision.ResultsAll determined OSS included a late measurement at 192 h p.i., which was necessary for accurate and precise tumour TIACs. OSS with three TPs were identified to be 3-4, 96-100 and 192 h with an additional SPECT/CT measurement at the penultimate TP. Kidney and tumour RMSE of 6.4 to 7.7% and 6.3 to 7.8% were obtained, respectively. Shortening the total time for dosimetry to e.g. 96 h resulted in kidney and tumour RMSE of 6.8 to 8.3% and 9.1 to 11%, respectively. OSS with four TPs showed similar results as with three TPs. Planar images at 4 and 68 h and a SPECT/CT shortly after the 68 h measurement led to kidney and tumour RMSE of 8.4 to 12% and 12 to 16%, respectively. One single SPECT/CT measurement at 52 h yielded good approximations for the kidney TIACs (RMSE of 7.0%), but led to biased tumour TIACs.ConclusionOSS allow improvements in accuracy and precision of renal and tumour dosimetry for [177Lu]Lu-PSMA therapy with potentially less effort. A late TP is important regarding accurate tumour TIACs.

Project description:BackgroundDosimetry after radiopharmaceutical therapy with 177Lu (177Lu-RPT) relies on quantitative SPECT/CT imaging, for which suitable reconstruction protocols are required. In this study, we characterized for the first time the quantitative performance of a ring-shaped CZT-based camera using two different reconstruction algorithms: an ordered subset expectation maximization (OSEM) and a block sequential regularized expectation maximization (BSREM) combined with noise reduction regularization. This study lays the foundations for the definition of a reconstruction protocol enabling accurate dosimetry for patients treated with 177Lu-RPT.MethodsA series of 177Lu-filled phantoms were acquired on a StarGuide™ (GE HealthCare), with energy and scatter windows centred at 208 (± 6%) keV and 185 (± 5%) keV, respectively. Images were reconstructed with the manufacturer implementations of OSEM (GE-OSEM) and BSREM (Q.Clear) algorithms, and various combinations of iterations and subsets. Additionally, the manufacturer-recommended Q.Clear-based reconstruction protocol was evaluated. Quantification accuracy, measured as the difference between the SPECT-based and the radionuclide calibrator-based activity, and noise were evaluated in a large cylinder. Recovery coefficients (RCs) and spatial resolution were assessed in a NEMA IEC phantom with sphere inserts. The reconstruction protocols considered suitable for clinical applications were tested on a cohort of patients treated with [177Lu]Lu-PSMA-I&T.ResultsThe accuracy of the activity from the cylinder, although affected by septal penetration, was < 10% for all reconstructions. Both algorithms featured improved spatial resolution and higher RCs with increasing updates at the cost of noise build-up, but Q.Clear outperformed GE-OSEM in reducing noise accumulation. When the reconstruction parameters were carefully selected, similar values for noise (~0.15), spatial resolution (~1 cm) and RCs were found, irrespective of the reconstruction algorithm. Analogue results were found in patients.ConclusionsAccurate activity quantification is possible when imaging 177Lu with StarGuide™. However, the impact of septal penetration requires further investigations. GE-OSEM is a valid alternative to the recommended Q.Clear reconstruction algorithm, featuring comparable performances assessed on phantoms and patients.

Project description:177Lu-DOTATATE (Lutathera®) enables targeted radionuclide therapy of neuroendocrine tumors expressing somatostatin receptor type 2. Though patient-specific dosimetry estimates may be clinically important for predicting absorbed dose-effect relationships, there are multiple relevant dosimetry paradigms which are distinct in terms of clinical effort, numerical output and added-value. This work compares three different approaches for177Lu-DOTATATE dosimetry, including 1) an organ-level approach based on reference phantom MIRD S-values scaled to patient-specific organ masses (MIRDcalc), 2) an organ-level approach based on Monte Carlo simulation in a patient-specific mesh phantoms (PARaDIM), and 3) a 3D approach based on Monte Carlo simulation in patient-specific voxel phantoms.Method. Serial quantitative SPECT/CT images for two patients receiving177Lu-DOTATATE therapy were obtained from archive in theDeep Bluedatabase. For each patient, the serial CT images were co-registered to the first time point CT using a deformable registration technique aided by virtual landmarks placed in the kidney pelves and the lesion foci. The co-registered SPECT images were integrated voxel-wise to generate time-integrated activity maps. Lesions, kidneys, liver, spleen, lungs, compact bone, spongiosa, and rest of body were segmented at the first imaging time point and overlaid on co-registered integrated activity maps. The resultant segmentation was used for three purposes: 1) to generate patient-specific phantoms, 2) to determine organ-level time-integrated activities, and 3) to generate dose volume histograms from 3D voxel-based calculations.Results. Mean absorbed doses were computed for lesions and 48 tissues with MIRDcalc software. Mean organ absorbed doses and dose volume histograms were obtained for lesions and 6 tissues with the voxel Monte Carlo approach. Lesion- and organ-level absorbed dose estimates agreed within ±26% for the lesions and ±13% for the critical organs, among the different methods tested. Overall good agreement was observed with the dosimetry estimates from the NETTER-1 trial.Conclusions. For personalized177Lu-DOTATATE dosimetry, a combined approach was determined to be valuable, which utilized two dose calculation methods supported by a single image processing workflow. In the absence of quantitative imaging limitations, the voxel Monte Carlo method likely provides valuable information to guide treatment by considering absorbed dose non-uniformity in lesions and organs at risk. The patient-scaled reference phantom method also provides valuable information, including absorbed dose estimates for non-segmented organs, and more accurate dose estimates for complex radiosensitive organs including the active marrow.

Dataset Information

A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After ¹⁷⁷Lu-DOTATATE.

Publications

A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After <sup>177</sup>Lu-DOTATATE.

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A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After 177Lu-DOTATATE.

Publications

A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After <sup>177</sup>Lu-DOTATATE.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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A Novel Time-Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After ¹⁷⁷Lu-DOTATATE.