An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 1: Design and Early Results of the SNMMI Dosimetry Challenge

A study of differences in absorbed dose estimates by images used in dosimetry of Lu-177-DOTATATE therapy

In Lu-177-labeled peptide receptor radionuclide therapy, dosimetry has recently gained importance for assessing side effects and treatment responses. However, no standard method has been established yet. In this retrospective study, we compared the kidney-absorbed doses of 24 treatments with Lu-177-DOTATATE using three methods: a planar method using only planar images, a hybrid method using planar and SPECT/CT images, and a SPECT/CT method using only SPECT/CT images. In the Planar method, the ROI was defined from 2D whole-body planar images and calculated using the MIRD method. In the Hybrid method, the VOI was defined from CT images and the VOIs were placed in the 2D planar image as ROIs, which were calculated using the MIRD method. For the SPECT/CT method, the VOI was defined from CT images and the mean absorbed dose was estimated on a voxel basis. The absorbed dose estimated with the Planar method (15.2 ± 5.81 Gy) was significantly higher than the estimates with the other two methods (Hybrid: 2.93 ± 1.33 Gy, SPECT/CT: 3.81 ± 0.93 Gy) (p < 0.05). The Hybrid and SPECT/CT methods exhibited the strongest correlation. The Planar method demonstrated the highest variability in estimated values. The use of 2D planar images alone tended to overestimate the absorbed dose compared to the other methods, depending on the definition of the ROIs and the characteristics of the analysis software. This suggests that a combined approach using SPECT/CT and planar images is preferable for dosimetry.

Can 177Lu-DOTATATE Kidney Absorbed Doses be Predicted from Pretherapy SSTR PET? Findings from Multicenter Data

Before performing 177Lu-DOTATATE therapy for neuroendocrine tumors, somatostatin receptor (SSTR) PET imaging is currently used to confirm sufficient tumor SSTR expression, but it also has potential to be used to personalize treatment by predicting absorbed doses to critical organs. This study aims to validate the predictive capability of SSTR PET in anticipating renal absorbed dose in the first cycle of 177Lu-DOTATATE using a multicenter dataset to analyze and derive insights from a broader patient population. Methods: Retrospective data from 5 centers were included in this study: 1 in Canada (n = 25), 1 in Norway (n = 75), 1 in Sweden (n = 18), and 2 in the United States (n = 36 and n = 26). At each center, pretherapy SSTR PET/CT imaging and postcycle 1 177Lu imaging-based dosimetry were performed according to site-specific protocols. The mixed-effects model treating centers as random effects was developed using baseline SSTR PET renal uptake values to predict renal absorbed dose from 177Lu-DOTATATE. Additionally, leave-one-center-out cross-validation and leave-one-sample-out cross-validation were implemented for external and internal validation, respectively, measuring mean absolute error and mean relative absolute error. Results: Across all participating centers, the median cycle 1 renal absorbed dose was 0.56 Gy/GBq (range, 0.14-1.27 Gy/GBq), whereas the median pretherapy SSTR PET renal uptake was 110.7 Bq/mL/MBq (range, 28.6-287.7 Bq/mL/MBq). The differences among center means were statistically significant for both absorbed dose and PET uptake (P < 0.0001 from 1-way ANOVA). A significant (P < 0.05) correlation was observed between kidney SSTR PET uptake and 177Lu-DOTATATE absorbed dose for each center (center-specific coefficient of determination ranged from 0.14 to 0.53). When data across all centers were aggregated, the mixed-effects model achieved a coefficient of determination of 0.25 (P < 0.01), resulting in an mean absolute error of 0.15 Gy/GBq (SD, 0.11 Gy/GBq) and an mean relative absolute error of 28% (SD, 24%) for external validation and 0.12 Gy/GBq (SD, 0.10 Gy/GBq) and 22% (SD, 20%) for internal validation. Conclusion: The correlations observed between SSTR PET renal uptake and 177Lu-DOTATATE absorbed dose to kidneys across a multicenter population are statistically significant yet modest. The prediction model achieved a mean relative absolute error 28% or less for both external and internal validation of PET-predicted absorbed doses. The intercenter differences suggest the need for standardized imaging protocols and dosimetry workflows.

Machine Learning-Based Dose Prediction in [177Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [68Ga]Ga-PSMA-11 PET/CT

PURPOSE

The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [177Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [68Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy.

METHODS

Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE).

RESULTS

The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77.

CONCLUSION

Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort.

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.

Methodology for comparing absorbed dose rate calculation algorithms in molecular radiotherapy dosimetry

INTRODUCTION

This study compared the results obtained using three of the most frequently proposed algorithms, Local Energy Deposition (LED), Convolution of Voxel S Values (CONV) and Monte Carlo modeling (MC).

METHODS

OpenDose3D, a free, open-source clinical dosimetry software was used to perform this comparison. The assessment focused on absorbed dose rate (ADR), thereby increasing the dataset size and avoiding potential bias associated with the time integration step. From patients treated with 90Y-microspheres, [177Lu]Lu-DOTATATE or Na[131I]I, 52 datasets were processed. Voxel-based ADR maps were computed in homogeneous (water) and heterogeneous (CT-derived) media, for a total of 312 datasets. In a heterogeneous medium, density correction was applied at the voxel level. ADR values were averaged over VOIs, and relative differences (rd) were calculated using the MC results as the reference.

RESULTS

In the homogeneous medium, LED underestimated the ADR by up to 10 % in soft tissues, particularly when cross-irradiation cannot be neglected. Conversely, the ADR obtained with CONV presentedexcellent agreement with the MC simulations (rd ≤ 1 %). In the heterogeneous medium, density correction was crucial. For example in 131I, LED underestimated the ADR values by up to 50 % in lungs and bone marrow. CONV showed excellent agreement with MC in soft tissues (rd ∼ 1 %) and good agreement in organs/tissues where self-irradiation was predominant. The implemented density correction was much less efficient for organs/tissues that experience mostly cross-irradiation.

CONCLUSIONS

Our work proposes a procedure for evaluating ADR algorithms. It also underscores the impact of the medium heterogeneity and cross-irradiation on dosimetry calculations.

Quality and Safety Considerations for Radiopharmaceutical Therapy in the Radiation Oncology Environment: An ASTRO Safety White Paper

PURPOSE

Radiopharmaceutical therapy (RPT) is the latest topic in a series of white papers published by ASTRO addressing quality processes and patient safety. The availability of radiopharmaceutical agents for therapeutic use has broadened patient treatment options; although generally administered systemically, their effects are targeted to cellular receptors or the tumor microenvironment. Radiation oncology is well suited to delivering RPT because clinicians are already experienced in radiation safety, treatment delivery, and ongoing patient care. This paper focuses on the logistics of initiating and/or maintaining an RPT program in radiation oncology and includes collaborating with other medical specialties. The white paper addresses the safety processes and workflow considerations for alpha- and beta-emitting radionuclides used for RPT.

METHODS

ASTRO convened a multidisciplinary task force, composed of experts from radiation oncology, nuclear medicine, medical and health physics, to provide consensus on key workflows and processes for RPT. Recommendations were created using a consensus-building methodology and task force members indicated their level of agreement based on a 5-point Likert scale, from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who select "strongly agree" or "agree" indicated consensus. Content not meeting this threshold was removed or revised.

SUMMARY

Establishing an RPT program in radiation oncology requires specific infrastructure for receiving, storing, preparing, and administering radiopharmaceuticals by staff with expertise in specific infusion methods. RPT cases benefit from a multidisciplinary approach led by a radiation medicine physician and authorized user with support from additional personnel trained in RPT. A comprehensive quality management program must be developed to comply with applicable regulations and standards, including the handling of radioactive materials. Participation in incident reporting and external audits of a practice's overall quality assurance processes is encouraged. Using the guidance provided, authorized users can assess the viability of starting an RPT program, develop the necessary infrastructure, and sustain a safe, high-quality RPT program that includes radiation oncology.

Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Current Tools, Techniques, and Uncharted Territories

Radiopharmaceutical therapy (RPT), with its targeted delivery of cytotoxic ionizing radiation, demonstrates significant potential for treating a wide spectrum of malignancies, with particularly unique benefits for metastatic disease. There is an opportunity to optimize RPTs and enhance the precision of theranostics by moving beyond a one-size-fits-all approach and using patient-specific image-based dosimetry for personalized treatment planning. Such an approach, however, requires accurate methods and tools for the mathematic modeling and prediction of dose and clinical outcome. To this end, the SNMMI AI-Dosimetry Working Group is promoting the paradigm of computational nuclear oncology: mathematic models and computational tools describing the hierarchy of etiologic mechanisms involved in RPT dose response. This includes radiopharmacokinetics for image-based internal dosimetry and radiobiology for the mapping of dose response to clinical endpoints. The former area originates in pharmacotherapy, whereas the latter originates in radiotherapy. Accordingly, models and methods developed in these predecessor disciplines serve as a foundation on which to develop a repurposed set of tools more appropriate to RPT. Over the long term, this computational nuclear oncology framework also promises to facilitate widespread cross-fertilization of ideas between nuclear medicine and the greater mathematic and computational oncology communities.

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.

Establishment of a practical methodology for evaluating equieffective dose of individual patients based on RT-PHITS

BACKGROUND

The RadioTherapy package based on PHITS (RT-PHITS) is an individual dosimetry system applicable to both targeted radionuclide therapy (TRT) and external radiotherapy. This study aims to establish a practical methodology for evaluating both absorbed doses and equieffective doses (EQDX) by improving RT-PHITS.

METHODS

We developed an Excel-based program, ExPORT-PHITS, which simplifies the conversion of the dose rates of specific organs and tumors calculated by RT-PHITS into corresponding absorbed doses and EQDX. ExPORT-PHITS offers two options for evaluating EQDX, each adopting a different type of microdosimetric kinetic model, to assess its dependence. The performance of the improved RT-PHITS, including ExPORT-PHITS, was evaluated using SPECT/CT images of two castration-resistant prostate cancer patients with bone metastases after the injection of 223RaCl2 and 99mTc-MDP.

RESULTS

Reasonable agreement was observed between absorbed doses calculated by RT-PHITS, IDAC Dose 2.1, and MIRDcalc, although absorbed doses in normal organs following the injection of 223RaCl2 were comparatively higher than those reported in other studies. Results for 223RaCl2 also showed that EQD2 tended to exceed the corresponding absorbed doses and RBE-weighted doses, while the relation was reversed for the injection of 99mTc-MDP.

CONCLUSIONS

These findings underscore RT-PHITS as a valuable tool for accurately modeling and optimizing individual TRT, especially for treatments involving α-ray emitters.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 4: Impact of Fitting Functions in Estimated Absorbed Doses

Individualized radiopharmaceutical therapies guided by patient-specific absorbed dose (AD) assessments using nuclear medicine imaging have the potential to improve both efficacy and safety. Understanding sources of variability in AD calculations is critical for standardization. The Society of Nuclear Medicine and Molecular Imaging Dosimetry Task Force launched the 177Lu Dosimetry Challenge to evaluate variability across steps within the dosimetry workflow. This work aimed to assess the variability in ADs due to different fitting and integration methods. Methods: Anonymized datasets from 2 patients treated with 177Lu-DOTATATE, including serial SPECT/CT scans, segmented organs and lesions, and time-integrated activity maps, were made available online. Participants were invited to perform dosimetry calculations and submit their results using standardized submission spreadsheets. Fitting approaches were categorized, and relative AD variability was analyzed using the quartile coefficient of dispersion and interquartile range. Results: The variability in AD due to the fitting step for patient A's kidneys was less than 1%. In contrast, patient B's kidneys showed higher variability, with values below 10%. Lesions exhibited more variability in fitting than did kidneys, with variability within 25%. Conclusion: The contribution of variability caused by fitting and integration is small for healthy organs. By following recommendations such as selection of appropriate functions, pharmacokinetic modeling, and sanity checks, this variability can be further reduced.

Do we need dosimetry for the optimization of theranostics in CNS tumors?

Radiopharmaceutical theranostic treatments have grown exponentially worldwide, and internal dosimetry has attracted attention and resources. Despite some similarities with chemotherapy, radiopharmaceutical treatments are essentially radiotherapy treatments, as the release of radiation into tissues is the determinant of the observed clinical effects. Therefore, absorbed dose calculations are key to explaining dose-effect correlations and individualizing radiopharmaceutical treatments. The present article introduces the basic principles of internal dosimetry and provides an overview of available loco-regional and systemic radiopharmaceutical treatments for central nervous system (CNS) tumors. The specific characteristics of dosimetry as applied to these treatments are highlighted, along with their limitations and most relevant results. Dosimetry is performed with higher precision and better reproducibility than in the past, and dosimetric data should be systematically collected, as treatment planning and verification may help exploit the full potential of theranostic of CNS tumors.

Effect of single-photon emission computed tomography acquisition method and sampling angles on image quality and quantitative accuracy in xSPECT-reconstructed images

OBJECTIVE

The aim of this study was to evaluate the effects of the single-photon emission computed tomography (SPECT) acquisition method and sampling angles on the qualitative and quantitative interpretations of xSPECT-reconstructed images.

METHODS

The spatial resolution was evaluated using a JSP phantom, and the uniformity and quantitative accuracy were verified with a NEMA IEC Body Phantom using an SIEMENS Symbia Intevo SPECT/computed tomography system. SPECT was performed using three acquisition methods (step-and-shoot, continuous, and acquire during the step), and the sampling angles were set to 2, 3, 4, 5, and 6°. The xSPECT-reconstruction technology which is used with ordered subset-conjugated gradient minimization was used for image reconstruction.

RESULTS

Full width of half maximum, an evaluation index of spatial resolution, varied up to 2.73 mm with different sampling angles and up to 2.06 mm with different acquisition methods. Uniformity, as assessed by the coefficient of variation, improved with increasing sampling angles. The accuracy of the quantification of the hot sphere showed an error rate of approximately 10% depending on the sampling angle, and an error rate of approximately 5% depending on the different acquisition methods.

CONCLUSIONS

In xSPECT-reconstructed images, the difference in sampling angle has a greater impact on image quality and quantitativity than the difference in the acquisition method. For tests in which uniformity is important, a larger sampling angle is recommended.

Theranostics in Neuroendocrine Tumors: Updates and Emerging Technologies

Advancements in somatostatin receptor (SSTR) targeted imaging and treatment of well-differentiated neuroendocrine tumors (NETs) have revolutionized the management of these tumors. This comprehensive review delves into the current practice, discussing the use of the various FDA-approved SSTR-agonist PET tracers and the predictive imaging biomarkers, and elaborating on Lu177-DOTATATE peptide receptor radionuclide therapy (PRRT) including the evolving areas of post-therapy imaging practices, PRRT retreatment, and the potential role of dosimetry in optimizing patient treatments. The future directions sections highlight ongoing research on investigational PET imaging radiotracers, future prospects in alpha particle therapy, and combination therapy strategies.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 3: Contribution from Calculating Absorbed Dose from Time-Integrated Activity

Image-based dosimetry-guided radiopharmaceutical therapy has the potential to personalize treatment by limiting toxicity to organs at risk and maximizing the therapeutic effect. The 177Lu dosimetry challenge of the Society of Nuclear Medicine and Molecular Imaging consisted of 5 tasks assessing the variability in the dosimetry workflow. The fifth task investigated the variability associated with the last step, dose conversion, of the dosimetry workflow on which this study is based. Methods: Reference variability was assessed by 2 medical physicists using different software, methods, and all possible combinations of input segmentation formats and time points as provided in the challenge. General descriptive statistics for absorbed dose values from the global submissions from participants were calculated, and variability was measured using the quartile coefficient of dispersion. Results: For the liver, which included lesions with high uptake, variabilities of up to 36% were found. The baseline analysis showed a variability of 29% in absorbed dose results for the liver from datasets where lesions included and excluded were grouped, indicating that variation in how lesions in normal liver were treated was a significant source of variability. For other organs and lesions, variability was within 7%, independently of software used except for the local deposition method. Conclusion: The choice of dosimetry method or software had a small contribution to the overall variability of dose estimates.

ISIT-QA: In Silico Imaging Trial to Evaluate a Low-Count Quantitative SPECT Method Across Multiple Scanner-Collimator Configurations for 223Ra-Based Radiopharmaceutical Therapies

Personalized dose-based treatment planning requires accurate and reproducible noninvasive measurements to ensure safety and effectiveness. Dose estimation using SPECT is possible but challenging for alpha (α)-particle-emitting radiopharmaceutical therapy (α-RPT) because of complex γ-emission spectra, extremely low counts, and various image-degrading artifacts across a plethora of scanner-collimator configurations. Through the incorporation of physics-based considerations and skipping of the potentially lossy voxel-based reconstruction step, a recently developed projection-domain low-count quantitative SPECT (LC-QSPECT) method has the potential to provide reproducible, accurate, and precise activity concentration and dose measures across multiple scanners, as is typically the case in multicenter settings. To assess this potential, we conducted an in silico imaging trial to evaluate the LC-QSPECT method for a 223Ra-based α-RPT, with the trial recapitulating patient and imaging system variabilities. Methods: A virtual imaging trial titled In Silico Imaging Trial for Quantitation Accuracy (ISIT-QA) was designed with the objectives of evaluating the performance of the LC-QSPECT method across multiple scanner-collimator configurations and comparing performance with a conventional reconstruction-based quantification method. In this trial, we simulated 280 realistic virtual patients with bone-metastatic castration-resistant prostate cancer treated with 223Ra-based α-RPT. The trial was conducted with 9 simulated SPECT scanner-collimator configurations. The primary objective of this trial was to evaluate the reproducibility of dose estimates across multiple scanner-collimator configurations using LC-QSPECT by calculating the intraclass correlation coefficient. Additionally, we compared the reproducibility and evaluated the accuracy of both considered quantification methods across multiple scanner-collimator configurations. Finally, the repeatability of the methods was evaluated in a test-retest study. Results: In this trial, data from 268 223RaCl2 treated virtual prostate cancer patients, with a total of 2,903 lesions, were used to evaluate LC-QSPECT. LC-QSPECT provided dose estimates with good reproducibility across the 9 scanner-collimator configurations (intraclass correlation coefficient > 0.75) and high accuracy (ensemble average values of recovery coefficients ranged from 1.00 to 1.02). Compared with conventional reconstruction-based quantification, LC-QSPECT yielded significantly improved reproducibility across scanner-collimator configurations, accuracy, and test-retest repeatability ([Formula: see text] Conclusion: LC-QSPECT provides reproducible, accurate, and repeatable dose estimations in 223Ra-based α-RPT as evaluated in ISIT-QA. These findings provide a strong impetus for multicenter clinical evaluations of LC-QSPECT in dose quantification for α-RPTs.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy

PURPOSE

Accurate 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.

METHODS

We 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).

RESULTS

The 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.

CONCLUSION

A 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.

Dosimetric Analysis of a Phase I Study of PSMA-Targeting Radiopharmaceutical Therapy With [177Lu]Ludotadipep in Patients With Metastatic Castration-Resistant Prostate Cancer

OBJECTIVE

177Lutetium [Lu] Ludotadipep is a novel prostate-specific membrane antigen targeting therapeutic agent with an albumin motif added to increase uptake in the tumors. We assessed the biodistribution and dosimetry of [177Lu]Ludotadipep in patients with metastatic castration-resistant prostate cancer (mCRPC).

MATERIALS AND METHODS

Data from 25 patients (median age, 73 years; range, 60-90) with mCRPC from a phase I study with activity escalation design of single administration of [177Lu]Ludotadipep (1.85, 2.78, 3.70, 4.63, and 5.55 GBq) were assessed. Activity in the salivary glands, lungs, liver, kidneys, and spleen was estimated from whole-body scan and abdominal SPECT/CT images acquired at 2, 24, 48, 72, and 168 h after administration of [177Lu]Ludotadipep. Red marrow activity was calculated from blood samples obtained at 3, 10, 30, 60, and 180 min, and at 24, 48, and 72 h after administration. Organ- and tumor-based absorbed dose calculations were performed using IDAC-Dose 2.1.

RESULTS

Absorbed dose coefficient (mean ± standard deviation) of normal organs was 1.17 ± 0.81 Gy/GBq for salivary glands, 0.05 ± 0.02 Gy/GBq for lungs, 0.14 ± 0.06 Gy/GBq for liver, 0.77 ± 0.28 Gy/GBq for kidneys, 0.12 ± 0.06 Gy/GBq for spleen, and 0.07 ± 0.02 Gy/GBq for red marrow. The absorbed dose coefficient of the tumors was 10.43 ± 7.77 Gy/GBq.

CONCLUSION

[177Lu]Ludotadipep is expected to be safe at the dose of 3.7 GBq times 6 cycles planned for a phase II clinical trial with kidneys and bone marrow being the critical organs, and shows a high tumor absorbed dose.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Quality Assurance Considerations in Radiopharmaceutical Therapy Dosimetry Using PLANETDose: An International Atomic Energy Agency Study

Implementation of radiopharmaceutical therapy dosimetry varies depending on the clinical application, dosimetry protocol, software, and ultimately the operator. Assessing clinical dosimetry accuracy and precision is therefore a challenging task. This work emphasizes some pitfalls encountered during a structured analysis, performed on a single-patient dataset consisting of SPECT/CT images by various participants using a standard protocol and clinically approved commercial software. Methods: The clinical dataset consisted of the dosimetric study of a patient administered with [177Lu]Lu-DOTATATE at Tygerberg Hospital, South Africa, as a part of International Atomic Energy Agency-coordinated research project E23005. SPECT/CT images were acquired at 5 time points postinjection. Patient and calibration images were reconstructed on a workstation, and a calibration factor of 122.6 Bq/count was derived independently and provided to the participants. A standard dosimetric protocol was defined, and PLANETDose (version 3.1.1) software was installed at 9 centers to perform the dosimetry of 3 treatment cycles. The protocol included rigid image registration, segmentation (semimanual for organs, activity threshold for tumors), and dose voxel kernel convolution of activity followed by absorbed dose (AD) rate integration to obtain the ADs. Iterations of the protocol were performed by participants individually and within collective training, the results of which were analyzed for dosimetric variability, as well as for quality assurance and error analysis. Intermediary checkpoints were developed to understand possible sources of variation and to differentiate user error from legitimate user variability. Results: Initial dosimetric results for organs (liver and kidneys) and lesions showed considerable interoperator variability. Not only was the generation of intermediate checkpoints such as total counts, volumes, and activity required, but also activity-to-count ratio, activity concentration, and AD rate-to-activity concentration ratio to determine the source of variability. Conclusion: When the same patient dataset was analyzed using the same dosimetry procedure and software, significant disparities were observed in the results despite multiple sessions of training and feedback. Variations due to human error could be minimized or avoided by performing intensive training sessions, establishing intermediate checkpoints, conducting sanity checks, and cross-validating results across physicists or with standardized datasets. This finding promotes the development of quality assurance in clinical dosimetry.

A Pharmacokinetic Model Determination of Time Activity Curves in Radiopharmaceutical Therapy

Introduction and Purpose

Radiopharmaceutical therapy (RPT) dosimetry can be challenging to perform due to sparse data measurements and variations in how the time activity curve (TAC) is determined. In this work, a single system of equations was theoretically derived to estimate the TAC.

Methods

A pharmacokinetic (PK) model was developed to estimate patient specific rate constants for a given set of body compartments. The PK model and an optimizer were numerically implemented to determine the rate constants and, using these physiologic data, to generate TACs and time integrated activities (TIAs) for 3 tissue systems from clinical data gathered in 5 patients. A fourth (aggregate) tissue compartment is added using conservation of activity considerations.

Results

Feasibility of the PK model was demonstrated by successfully generating TACs and TIAs for all patients in a manner comparable to existing methods in the literature. The data are compared to smaller sampling regimes. Differences between the 3- and 4-compartment models show that conservation of activity considerations should be part of TAC estimations.

Conclusion

The results here suggest a new paradigm in RPT in using the rate constants so identified as a diagnostic tool and as a vehicle to achieving individualized tumorcidal dose and/or the maximum tolerable dose to normal tissues.

Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177 Lu and 90 Y radiopharmaceutical therapy (RPT) applications

BACKGROUND

Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time-integrated activity (TIA) distribution has not been systematically investigated.

PURPOSE

This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs.

METHODS

DVKs of 3 × 3 × 3 mm3 sampling-nine for 177 Lu, nine for 90 Y-were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with 177 Lu-DOTATATE and PET images for two patients treated with 90 Y-microspheres were used, including organs at risk (177 Lu: kidneys; 90 Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed.

RESULTS

The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the 177 Lu DVKs. For the case of 90 Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for 177 Lu, and 4%(15%) for 90 Y. The variability of the mean tumor and organ AD was up to 19% and 15% in 177 Lu-DOTATATE and 90 Y-microspheres patients, respectively.

CONCLUSIONS

This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT.

Time-Activity data fitting in molecular Radiotherapy: Methodology and pitfalls

Absorbed radiation doses are essential in assessing the effects, e.g. safety and efficacy, of radiopharmaceutical therapy (RPT). Patient-specific absorbed dose calculations in the target or the organ at risk require multiple inputs. These include the number of disintegrations in the organ, i.e. the time-integrated activities (TIAs) of the organs, as well as other parameters describing the process of radiation energy deposition in the target tissue (i.e. mean energy per disintegration, radiation dose constants, etc). TIAs are then estimated by incorporating the area under the radiopharmaceutical's time-activity curve (TAC), which can be obtained by quantitative measurements of the biokinetics in the patient (typically based on imaging data such as planar scintigraphy, SPECT/CT, PET/CT, or blood and urine samples). The process of TAC determination/calculation for RPT generally depends on the user, e.g., the chosen number and schedule of measured time points, the selection of the fit function, the error model for the data and the fit algorithm. These decisions can strongly affect the final TIA values and thus the accuracy of calculated absorbed doses. Despite the high clinical importance of the TIA values, there is currently no consensus on processing time-activity data or even a clear understanding of the influence of uncertainties and variations in personalised RPT dosimetry related to user-dependent TAC calculation. As a first step towards minimising site-dependent variability in RPT dosimetry, this work provides an overview of quality assurance and uncertainty management considerations of the TIA estimation.

Future trends for patient-specific dosimetry methodology in molecular radiotherapy

Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.

SPECT at the speed of PET: a feasibility study of CZT-based whole-body SPECT/CT in the post 177Lu-DOTATATE and 177Lu-PSMA617 setting

PURPOSE

To evaluate the feasibility of using the StarGuide (General Electric Healthcare, Haifa, Israel), a new generation multi-detector cadmium-zinc-telluride (CZT)-based SPECT/CT, for whole-body imaging in the setting of post-therapy imaging of 177Lu-labeled radiopharmaceuticals.

METHODS

Thirty-one patients (34-89 years old; mean ± SD, 65.5 ± 12.1) who were treated with either 177Lu-DOTATATE (n=17) or 177Lu-PSMA617 (n=14) as part of standard of care were scanned post-therapy with the StarGuide; some were also scanned with the standard GE Discovery 670 Pro SPECT/CT. All patients had either 64Cu-DOTATATE or 18F-DCFPyL PET/CT prior to first cycle of therapy for eligibility check. The detection/targeting rate (lesion uptake greater than blood pool uptake) of large lesions meeting RECIST 1.1 size criteria on post-therapy StarGuide SPECT/CT was evaluated and compared to the standard design GE Discovery 670 Pro SPECT/CT (when available) and pre-therapy PET by two nuclear medicine physicians with consensus read.

RESULTS

This retrospective analysis identified a total of 50 post-therapy scans performed with the new imaging protocol from November 2021 to August 2022. The StarGuide system acquired vertex to mid-thighs post-therapy SPECT/CT scans with 4 bed positions, 3 min/bed and a total scan time of 12 min. In comparison, the standard GE Discovery 670 Pro SPECT/CT system typically acquires images in 2 bed positions covering the chest, abdomen, and pelvis with a total scan time of 32 min. The pre-therapy 64Cu-DOTATATE PET takes 20 min with 4 bed positions on GE Discovery MI PET/CT, and 18F-DCFPyL PET takes 8-10 min with 4-5 bed positions on GE Discovery MI PET/CT. This preliminary evaluation showed that the post-therapy scans acquired with faster scanning time using StarGuide system had comparable detection/targeting rate compared to the Discovery 670 Pro SPECT/CT system and detected large lesions defined by RECIST criteria on the pre-therapy PET scans.

CONCLUSION

Fast acquisition of whole-body post-therapy SPECT/CT is feasible with the new StarGuide system. Short scanning time improves the patients' clinical experience and compliance which may lead to increased adoption of post-therapy SPECT. This opens the possibility to offer imaged-based treatment response assessment and personalized dosimetry to patients referred for targeted radionuclide therapies.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 2: Overall Variabilities in Absorbed Dose

Dosimetry for personalized radiopharmaceutical therapy has gained considerable attention. Many methods, tools, and workflows have been developed to estimate absorbed dose (AD). However, standardization is still required to reduce variability of AD estimates across centers. One effort for standardization is the Society of Nuclear Medicine and Molecular Imaging 177Lu Dosimetry Challenge, which comprised 5 tasks (T1-T5) designed to assess dose estimate variability associated with the imaging protocol (T1 vs. T2 vs. T3), segmentation (T1 vs. T4), time integration (T4 vs. T5), and dose calculation (T5) steps of the dosimetry workflow. The aim of this work was to assess the overall variability in AD calculations for the different tasks. Methods: Anonymized datasets consisting of serial planar and quantitative SPECT/CT scans, organ and lesion contours, and time-integrated activity maps of 2 patients treated with 177Lu-DOTATATE were made available globally for participants to perform dosimetry calculations and submit their results in standardized submission spreadsheets. The data were carefully curated for formal mistakes and methodologic errors. General descriptive statistics for ADs were calculated, and statistical analysis was performed to compare the results of different tasks. Variability in ADs was measured using the quartile coefficient of dispersion. Results: ADs to organs estimated from planar imaging protocols (T2) were lower by about 60% than those from pure SPECT/CT (T1), and the differences were statistically significant. Importantly, the average differences in dose estimates when at least 1 SPECT/CT acquisition was available (T1, T3, T4, T5) were within ±10%, and the differences with respect to T1 were not statistically significant for most organs and lesions. When serial SPECT/CT images were used, the quartile coefficients of dispersion of ADs for organs and lesions were on average less than 20% and 26%, respectively, for T1; 20% and 18%, respectively, for T4 (segmentations provided); and 10% and 5%, respectively, for T5 (segmentation and time-integrated activity images provided). Conclusion: Variability in ADs was reduced as segmentation and time-integration data were provided to participants. Our results suggest that SPECT/CT-based imaging protocols generate more consistent and less variable results than planar imaging methods. Effort at standardizing segmentation and fitting should be made, as this may substantially reduce variability in ADs.

A Primer on Radiopharmaceutical Therapy

The goal of this article is to serve as a primer for the United States-based radiation oncologist who may be interested in learning more about radiopharmaceutical therapy (RPT). Specifically, we define RPT, review the data behind its current and anticipated indications, and discuss important regulatory considerations for incorporating it into clinical practice. RPT represents an opportunity for radiation oncologists to leverage 2 key areas of expertise, namely therapeutic radiation therapy and oncology, and apply them in a distinct context in collaboration with nuclear medicine and medical oncology colleagues. Although not every radiation oncologist will incorporate RPT into their day-to-day practice, it is important to understand the role for this modality and how it can be appropriately used in select patients.

Developments in Combining Targeted Radionuclide Therapies and Immunotherapies for Cancer Treatment

Targeted radionuclide therapy (TRT) and immunotherapy are rapidly growing classes of cancer treatments. Basic, translational, and clinical research are now investigating therapeutic combinations of these agents. In comparison to external beam radiation therapy (EBRT), TRT has the unique advantage of treating all disease sites following intravenous injection and selective tumor uptake and retention-a particularly beneficial property in metastatic disease settings. The therapeutic value of combining radiation therapy with immune checkpoint blockade to treat metastases has been demonstrated in preclinical studies, whereas results of clinical studies have been mixed. Several clinical trials combining TRT and immune checkpoint blockade have been initiated based on preclinical studies combining these with EBRT and/or TRT. Despite the interest in translation of TRT and immunotherapy combinations, many questions remain surrounding the mechanisms of interaction and the optimal approach to clinical implementation of these combinations. This review highlights the mechanisms of interaction between anti-tumor immunity and radiation therapy and the status of basic and translational research and clinical trials investigating combinations of TRT and immunotherapies.