Accuracy of two dosimetry software programs for 177Lu radiopharmaceutical therapy using voxel-based patient-specific phantoms

Feasibility of individual dosimetry using RT-PHITS for patients with SPECT/CT imaging after 177Lu-DOTATATE peptide receptor radionuclide therapy

A radiotherapy package based on the Particle and Heavy Ion Transport code System (RT-PHITS) can calculate internal 3-dimensional dose distribution from SPECT/CT images of individual patients coupled with Monte Carlo radiation transport simulation. This study aims to determine the feasibility of individual dosimetry using RT-PHITS for patients after 177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT). We acquired SPECT/CT images from two patients from the 177Lu SNMMI Dosimetry Challenge (patients A and B) and one from our institute (patient C) at various time points. The images were converted to source/geometry information in the PHITS input format using RT-PHITS. The 3D dose-rate distribution in each patient was calculated using Monte Carlo radiation transport simulation. The output data from the PHITS simulation were converted to DICOM RT-dose format and analyzed using 3D Slicer to identify dose rates in lesions and organs at risk. The time variations of the calculated dose rates were linearly interpolated, considering the physical decay constant. The absorbed dose was evaluated as the integration of the time variation of the dose rate. Agreements between the absorbed doses obtained from RT-PHITS and 177Lu Dosimetry Challenge Task 4 were generally satisfactory (< 20%), although discrepancies were noted in some normal organs of patient A. This was likely due to difficulties in estimating the tail of the dose rate curve after the last imaging time point. The EQD2 was slightly, but not significantly increased compared with the absorbed dose in patient C. Individual dosimetry using RT-PHITS is feasible for assessing the effects of 177Lu-DOTATATE PRRT.

Evaluating auto-contouring accuracy in reduced CT dose images for radiopharmaceutical therapies: Denoising and evaluation of 177Lu DOTATATE therapy dataset

PURPOSE

Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in 177Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)/CT imaging during 177Lu DOTATATE therapy.

METHODS

The 177Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of 177Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PDdose) for spleen, right kidney, left kidney, and liver.

RESULTS

Before denoising, the mean ± standard deviation of PDdose for all critical organs were 2.31 ± 2.94%, 4.86 ± 9.42%, 8.39 ± 14.76%, 12.95 ± 19.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69 ± 2.25%, 2.84 ± 4.46%, 3.72 ± 4.22%, 7.98 ± 15.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.

CONCLUSION

As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of 177Lu DOTATATE therapy. Improving CT image quality through denoising could enhance 177Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT/CT imaging during treatment.

A review of 177Lu dosimetry workflows: how to reduce the imaging workloads?

177 Lu radiopharmaceutical therapy is a standardized systemic treatment, with a typical dose of 7.4 GBq per injection, but its response varies from patient to patient. Dosimetry provides the opportunity to personalize treatment, but it requires multiple post-injection images to monitor the radiopharmaceutical's biodistribution over time. This imposes an additional imaging burden on centers with limited resources. This review explores methods to lessen this burden by optimizing acquisition types and minimizing the number and duration of imaging sessions. After summarizing the different steps of dosimetry and providing examples of dosimetric workflows for177 Lu -DOTATATE and177 Lu -PSMA, we examine dosimetric workflows based on a reduced number of acquisitions, or even just one. We provide a non-exhaustive description of simplified methods and their assumptions, as well as their limitations. Next, we detail the specificities of each normal tissue and tumors, before reviewing dose-response relationships in the literature. In conclusion, we will discuss the current limitations of dosimetric workflows and propose avenues for improvement.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.

Results

Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.

Conclusion

We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.

Statement of Significance

This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.