Three-dimensional Monte Carlo-based voxel-wise tumor dosimetry in patients with neuroendocrine tumors who underwent 177Lu-DOTATOC therapy

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.

Tumor dosimetry using 177Lu: influence of background activity, measurement method and reconstruction algorithm

BACKGROUND

Image-based tumor dosimetry after radionuclide therapy, using the isotope ¹⁷⁷Lu, finds application e.g., for tumor-to-organ dose comparison and for dose response evaluation. When the tumor extent is not much larger than the image resolution, and when ¹⁷⁷Lu is found in nearby organs or other tumors, an accurate determination of tumor dose is particularly challenging. Here a quantitative evaluation of three different methods for determining the ¹⁷⁷Lu activity concentration in a phantom is performed, and the dependence on a variety of parameters is described. The phantom (NEMA IEC body phantom) has spheres of different size in a background volume, and sphere-to-background ¹⁷⁷Lu activity concentration ratios of infinity, 9.5, 5.0 and 2.7 are applied. The methods are simple to implement and well-known from the literature. They are based on (1) a large VOI encompassing the whole sphere, without background activity and with volume information from other sources, (2) a small VOI located in the sphere center, and (3) a VOI consisting of voxels with voxel value above a certain percentage of the maximum voxel value.

RESULTS

The determined activity concentration varies significantly with sphere size, sphere-to-background ratio, SPECT reconstruction method and method for determining the concentration. Based on the phantom study, criteria are identified under which the activity concentration can be determined with a maximal error of 40% even in the presence of background activity.

CONCLUSIONS

Tumor dosimetry is feasible in the presence of background activity using the above-mentioned methods, provided appropriate SPECT reconstructions are applied and tumors are selected for dosimetry analysis according to the following criteria for the three methods: (1) solitary tumor with diameter > 15 mm, (2) tumor diameter > 30 mm and tumor-to-background ratio > 2, and (3) tumor diameter > 30 mm and tumor-to-background ratio > 3.

Safety of Lutetium-177 prostate-specific membrane antigen-617 (PSMA-617) radioligand therapy in the setting of severe renal impairment: a case report and literature review

Reported here is a case of rapidly progressive metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA-617 in the setting of severe renal impairment and impending ureteric obstruction. PSMA is expressed on renal tubular cells, raising the possibility of radiation-induced nephrotoxicity, and this level of renal impairment would typically exclude the patient from [177Lu]Lu-PSMA-617 therapy. Multidisciplinary input, individualized dosimetry, and patient-specific dose reduction were used to ensure the cumulative dose to the kidneys remained within acceptable limits. He was initially planned for treatment with six cycles of [177Lu]Lu-PSMA-617. However, he had an excellent response to therapy following four cycles of treatment and the last two cycles were omitted. He has been followed for 1-year posttherapy without evidence of disease recurrence. No acute or chronic nephrotoxicity was observed. This case report highlights the utility of [177Lu]Lu-PSMA-617 therapy in severe renal impairment and provides evidence of relative safety in patients who would otherwise not be considered candidates for therapy.

Safety and Therapeutic Optimization of Lutetium-177 Based Radiopharmaceuticals

Peptide receptor radionuclide therapy (PRRT) using Lutetium-177 (¹⁷⁷Lu) based radiopharmaceuticals has emerged as a therapeutic area in the field of nuclear medicine and oncology, allowing for personalized medicine. Since the first market authorization in 2018 of [¹⁷⁷Lu]Lu-DOTATATE (Lutathera®) targeting somatostatin receptor type 2 in the treatment of gastroenteropancreatic neuroendocrine tumors, intensive research has led to transfer innovative ¹⁷⁷Lu containing pharmaceuticals to the clinic. Recently, a second market authorization in the field was obtained for [¹⁷⁷Lu]Lu-PSMA-617 (Pluvicto®) in the treatment of prostate cancer. The efficacy of ¹⁷⁷Lu radiopharmaceuticals are now quite well-reported and data on the safety and management of patients are needed. This review will focus on several clinically tested and reported tailored approaches to enhance the risk-benefit trade-off of radioligand therapy. The aim is to help clinicians and nuclear medicine staff set up safe and optimized procedures using the approved ¹⁷⁷Lu based radiopharmaceuticals.

Voxel-Based Internal Dosimetry for 177Lu-Labeled Radiopharmaceutical Therapy Using Deep Residual Learning

Purpose

In this study, we propose a deep learning (DL)-based voxel-based dosimetry method in which dose maps acquired using the multiple voxel S-value (VSV) approach were used for residual learning.

Methods

Twenty-two SPECT/CT datasets from seven patients who underwent 177Lu-DOTATATE treatment were used in this study. The dose maps generated from Monte Carlo (MC) simulations were used as the reference approach and target images for network training. The multiple VSV approach was used for residual learning and compared with dose maps generated from deep learning. The conventional 3D U-Net network was modified for residual learning. The absorbed doses in the organs were calculated as the mass-weighted average of the volume of interest (VOI).

Results

The DL approach provided a slightly more accurate estimation than the multiple-VSV approach, but the results were not statistically significant. The single-VSV approach yielded a relatively inaccurate estimation. No significant difference was noted between the multiple VSV and DL approach on the dose maps. However, this difference was prominent in the error maps. The multiple VSV and DL approach showed a similar correlation. In contrast, the multiple VSV approach underestimated doses in the low-dose range, but it accounted for the underestimation when the DL approach was applied.

Conclusion

Dose estimation using the deep learning-based approach was approximately equal to that in the MC simulation. Accordingly, the proposed deep learning network is useful for accurate and fast dosimetry after radiation therapy using 177Lu-labeled radiopharmaceuticals.

Peptide Receptor Radionuclide Therapy with [177Lu]Lu-DOTA-TATE in Patients with Advanced GEP NENS: Present and Future Directions

Simple Summary

Neuroendocrine neoplasms have been usually described as infrequent tumors, but their incidence has been rising over time. [¹⁷⁷Lu]Lu-DOTA-TATE (PRRT-Lu) was approved by the European Medicines Agency and by the Food and Drug Administration as the first radiopharmaceutical for peptide receptor radionuclide therapy in progressive gastroenteropancreatic NET. PRRT-Lu is considered a therapeutic option in progressive SSTR-positive NETs with homogenous SSTR expression. The NETTER-1 study demonstrated that PRRT-Lu yielded a statistically and clinically significant improvement in PFS as a primary endpoint (HR: 0.18, p < 0.0001), as well as a clinical trend towards improvement in OS. These results made scientific societies incorporate PRRT-Lu into their clinical guidelines; however, some questions still remain unanswered.

Abstract

This review article summarizes findings published in the last years on peptide receptor radionuclide therapy in GEP NENs, as well as potential future developments and directions. Unanswered questions remain, such as the following: Which is the correct dose and individual dosimetry? Which is the place for salvage PRRT-Lu? Whicht is the role of PRRT-Lu in the pediatric population? Which is the optimal sequencing of PRRT-Lu in advanced GEP NETs? Which is the place of PRRT-Lu in G3 NENs? These, and future developments such as inclusion new radiopharmaceuticals and combination therapy with different agents, such as radiosensitizers, will be discussed.

Unsealed Source: Scope of Practice for Radiopharmaceuticals Among United States Radiation Oncologists

Purpose

Our purpose was to determine the utilization of and barriers to implementation of radiopharmaceutical therapy (RPT) among U.S. radiation oncologists.

Methods and Materials

An anonymous, voluntary 21-item survey directed toward attending radiation oncologists was distributed via social media platforms (Twitter, LinkedIn, Facebook, Student Doctor Network). Questions assessed practice characteristics, specific RPT prescribing patterns, RPT prescribing interest, and perceived barriers to RPT implementation. Nonparametric χ² test was used for correlation statistics.

Results

Of the 142 respondents, 131 (92.3%) practiced in the United States and were included for this analysis. Respondents were well balanced in terms of practicing region, population size served, practice setting, and years in practice. Forty-eight percent (n = 63) reported prescribing at least 1 RPT. An additional 7% (n = 8) participate in RPT administration without billing themselves. Among those that actively prescribed RPT, the mean cumulative cases per month was 4.2 (range, 1-5). The most commonly prescribed radionuclides were radium-223 (40%; mean 2.8 cases/mo), iodine-131 (18%; mean 2.3 cases/mo), yttrium-90 (13%; mean 3.4 cases/mo), “other” (8%), samarium-153 (6%; mean 1.0 cases/mo), and strontrium-89 and phosphorous-32 (2% each; mean 1.8 and 0.4 cases/mo, respectively). Of those who answered “other,” lutetium-177 dotatate was most commonly prescribed (8%). No significant (P < .05) association was noted between practice type, practice location, years of practice, or practice volume with utilization of any RPTs. Most radiation oncologists (56%, n = 74) responded they would like to actively prescribe more RPT, although 27% (n = 35) were indifferent, and 17% (n = 22) said they would not like to prescribe more RPT. Perceived barriers to implementation were varied but broadly categorized into treatment infrastructure (44%, n = 57), interspecialty relations (41%, n = 53), lack of training (23%, n = 30), and financial considerations (16%, n = 21).

Conclusions

Among surveyed U.S. radiation oncologists, a significant number reported prescribing at least 1 RPT. The majority expressed interest in prescribing additional RPT. Wide-ranging barriers to implementation exist, most commonly interspecialty relations, treatment infrastructure, lack of training, and financial considerations.

Specific absorbed fractions and radionuclide S-values for tumors of varying size and composition

Accurate estimates of tumor absorbed dose are essential for the evaluation of treatment efficacy in radiopharmaceutical cancer therapy. Although tumor dosimetry via the MIRD schema has been previously investigated, prior studies have been limited to the consideration of soft-tissue tumors. In the present study, specific absorbed fractions (SAFs) for monoenergetic photons, electrons, and alpha particles in tumors of varying compositions were computed using Monte Carlo simulations in MCNPX after which self-irradiation S-values for 22 radionuclides (along with 14 additional alpha-emitter progeny) were generated for tumors of both varying size and tissue composition. The tumors were modeled as spheres with radii ranging from 0.10 cm to 6.0 cm and with compositions varying from 100% soft tissue (ST) to 100% mineral bone (MB). The energies of the photons and electrons were varied on a logarithm energy grid from 10 keV to 10 MeV. The energies of alpha particles were varied along a linear energy grid from 0.5 MeV to 12 MeV. In all cases, a homogenous activity distribution was assumed throughout the tumor volume. Furthermore, to assess the effect of tumor shape, several ellipsoidal tumors of different compositions were modeled and absorbed fractions were computed for monoenergetic electrons and photons. S-values were then generated using detailed decay data from the 2008 MIRD Monograph on Radionuclide Data and Decay Schemes. Our study results demonstrate that a soft-tissue model yields relative errors of 25% and 71% in the absorbed fraction assigned to uniform sources of 1.5 MeV electrons and 100 keV photons, respectively, localized within a 1 cm diameter tumor of MB. The data further show that absorbed fractions for moderate ellipsoids can be well approximated by a spherical shape of equal mass within a relative error of < 8%. S-values for 22 radionuclides (and their daughter progeny) were computed with results demonstrating how relative errors in SAFs could propagate to relative errors in tumor dose estimates as high as 86%. A comprehensive data set of radionuclide S-values by tumor size and tissue composition is provided for application of the MIRD schema for tumor dosimetry in radiopharmaceutical therapy.