Personalized dosimetry assessment of [177Lu]Lu-PSMA-617 radioligand therapy in the management of metastatic castration-resistant prostate cancer.

INTRODUCTION: Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) is revolutionizing the treatment landscape for metastatic castration-resistant prostate cancer (mCRPC) patients. This study aimed to establish patient-specific radiation dosimetry for [177Lu]Lu-PSMA-617 RLT in Iranian patients with mCRPC. METHOD: Twelve biopsy-proven prostate cancer patients (aged 68.73 ± 5.26 yr) underwent 6.62 ± 0.36 GBq [177Lu]Lu-PSMA-617 RLT. Post-therapy whole-body planar scans were acquired approximately at 4, 48, and 72 h post-administration, alongside a single SPECT/CT around 48 h using Siemens Symbia T2 to obtain cumulated activity. An imaging protocol and dosimetry approach were designed to balance between time efficacy and accuracy in post-therapeutic dosimetry. Using accurate activity calibration, S-values were calculated by importing SPECT/CT images as the source/geometry into the Geant4 application for the tomographic emission (GATE) Monte Carlo (MC) toolkit. The Medical Internal Radiation Dose (MIRD) scheme was followed for subsequent absorbed dose (AD) calculations in organs at risk (OAR) and tumoral lesions using the dose actor and accumulated activities for precise dose estimations. RESULTS: Using the MC approach, the mean ADs to the liver, spleen, right and left kidneys, and tumor lesions were 0.11 ± 0.04 Gy/GBq, 0.08 ± 0.03 Gy/GBq, 0.34 ± 0.09 Gy/GBq, 0.34 ± 0.10 Gy/GBq, and 0.83 ± 0.54 Gy/GBq, respectively. Notably, tumoral lesions demonstrated significantly higher ADs, indicating enhanced uptake of radiopharmaceuticals by malignant cells. CONCLUSIONS: This study indicates that the ADs of OARs and tumoral lesions from [177Lu]Lu-PSMA-617 RLT in patients with mCRPC are consistent with existing literature. The dosimetry findings suggest that increasing the administered activity of [177Lu]Lu-PSMA-617 RLT is feasible and does not pose a significant risk of adverse effects on OARs, as supported by our data. However, to validate the safety and efficacy of higher doses, further clinical follow-up studies are recommended.

Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy.

Background: Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients. Methods: This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC). Results: Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV. Conclusion: The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.

Comparison of Mannequin-Based Simulation Training Method with Virtual Training Method on Nursing Students' Learning Cardiopulmonary Resuscitation: A Controlled Randomized Parallel Trial.

Background: The use of different educational methods and programs, such as simulation and virtual training, plays an important role in effective Cardiopulmonary Resuscitation (CPR) learning for nursing students. This study was conducted with the aim of comparing mannequin-based simulation training with virtual training on CPR learning among nursing students. Materials and Methods: This parallel randomized controlled trial was conducted in 2022. We selected 73 nursing undergraduate students and randomly assigned them to two groups: mannequin-based simulation and virtual training groups. The knowledge, attitude, and performance of CPR in both groups were evaluated and compared before, immediately after, and 1 month after the intervention. Data analysis was performed using independent t-test and the repeated-measure analysis of variance (ANOVA) using the Statistical Package for Social Sciences (SPSS) software. Results: Within-group differences were significant in both mannequin-based simulation and virtual training groups in terms of knowledge, attitude, and CPR performance before and after training, as well as between before and 1 month after training (p < 0.001). In addition, the mean performance of simulation group students was significantly higher than the virtual group (p < 0.001), but no significant difference was observed between the two groups in terms of knowledge and attitude dimensions before training, after training, and 1 month after training. Conclusions: Both mannequin-based simulation and virtual training methods increase CPR learning. Considering that students' knowledge and attitude increase significantly using both training methods and the performance of students in the simulation group is better than in the virtual group, the use of a multimodal approach is recommended for CPR training of nursing students.

Incidence of Guillain-Barré Syndrome (GBS) after COVID-19 Vaccination: a Systematic Review and Meta-Analysis.

BACKGROUND: Global vaccination against COVID-19 will help nations to overcome the pandemic stage as soon as possible. Guillain-Barré syndrome (GBS) is an acute immune-mediated inflammatory disease of the peripheral nerves (PNS) that is reported as a complication of both COVID-19 and vaccines. Up to now, case reports regarding the incidence of GBS have been reported after different COVID-19 vaccines worldwide. So, the aim of this systematic review and meta-analysis is to estimate the pooled incidence of GBS after COVID-19 vaccination. METHODS: Two expert researchers conducted a systematic search in PubMed, Scopus, EMBASE, Web of Science, Google Scholar as well as gray literature in order to find relevant articles published before September 2022. RESULTS: After deleting duplicates, we found 1021 articles, of which 458 studies were further evaluated. A final number of 21 studies remained for meta-analysis, with most of those being from the USA, UK and Mexico. Follow-up duration was between 21-42 days. Out of the total number of 2.35x109 patients included in the final meta-analysis, 3654 subjects developed GBS after vaccination, most of whom were males. Incidence of GBS per million ranged between 0.23 and 9.8. The pooled incidence of GBS following vaccination was 0%.

A step toward simplified dosimetry of radiopharmaceutical therapy via SPECT frame duration reduction.

Despite being time-consuming, SPECT/CT data is necessary for accurate dosimetry in patient-specific radiopharmaceutical therapy. We investigated how reducing the frame duration (FD) during SPECT acquisition can simplify the dosimetry workflow for [177Lu]Lu-PSMA radioligand therapy (RLT). We aimed to determine the impact of shortened acquisition times on dosimetric precision. Three SPECT scans with FD of 20, 10, and 5 second/frame (sec/fr) were obtained 48 h post-RLT from one metastatic castration-resistant prostate cancer (mCRPC) patient's pelvis. Planar images at 4, 48, and 72 h post-therapy were used to calculate time-integrated activities (TIAs). Using accurate activity calibrations and GATE Monte Carlo (MC) dosimetry, absorbed doses in tumor lesions and kidneys were estimated. Dosimetry precision was assessed by comparing shorter FD results to the 20 sec/fr reference using relative percentage difference (RPD). We observed consistent calibration factors (CFs) across different FDs. Using the same CF, we obtained marginal RPD deviations less than 4% for the right kidney and tumor lesions and less than 7% for the left kidney. By reducing FD, simulation time was slightly decreased. This study shows we can shorten SPECT acquisition time in RLT dosimetry by reducing FD without sacrificing dosimetry accuracy. These findings pave the way for streamlined personalized internal dosimetry workflows.

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes.

INTRODUCTION: To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes. METHODS: 131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count. RESULTS: In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05. CONCLUSION: In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR.

BACKGROUND: Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model's encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data. METHODS: In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician. RESULTS: The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model's combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95. CONCLUSIONS: We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.

Macrophage-membrane-coated hybrid nanoparticles with self-supplied hydrogen peroxide for enhanced chemodynamic tumor therapy.

Addressing the challenges of chemodynamic therapies (CDTs) relying on Fenton reactions in malignant tumors is an active research area. Here, we report a method to develop pH-responsive hybrid nanoparticles for enhanced chemodynamic tumor treatment. Reactive CaO2 nanoparticles (core) are isolated by biocompatible ZIF-8 doped with Fe2+ (shell), and then encapsulated by macrophage membranes (symbolized as CaO2@Fe-ZIF-8@macrophage membrane or CFZM), thus endowed with high stability under normal physiological conditions. Our design features active tumor-homing by the macrophage-membrane coating, tumor microenvironment (TME)-responsive cargo release, and self-supplied hydrogen peroxide for promotion of the Fenton reaction. We demonstrate the improved delivery/tumor cell uptake of CFZM, the efficient production of toxic ˙OH with self-supplied H2O2 in CFZM, and high-efficacy tumor ablation on BALB/c mice bearing CT26 tumor cells. This offers a translational strategy to develop active tumor-targeting and TME-responsive nanotherapeutics with enhanced CDT against malignant tumors.

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.

Quantitative Evaluation of Scatter Correction in 128-slice Fan-Beam Computed Tomography Scan using Geant4 Application for Tomographic Emission Monte Carlo Simulation.

Background: Simulation of tomographic imaging systems with fan-beam geometry, estimation of scattered beam profile using Monte Carlo techniques, and scatter correction using estimated data have always been new challenges in the field of medical imaging. The most important aspect is to ensure the results of the simulation and the accuracy of the scatter correction. This study aims to simulate 128-slice computed tomography (CT) scan using the Geant4 Application for Tomographic Emission (GATE) program, to assess the validity of this simulation and estimate the scatter profile. Finally, a quantitative comparison of the results is made from scatter correction. Methods: In this study, 128-slice CT scan devices with fan-beam geometry along with two phantoms were simulated by GATE program. Two validation methods were performed to validate the simulation results. The data obtained from scatter estimation of the simulation was used in a projection-based scatter correction technique, and the post-correction results were analyzed using four quantities, such as: pixel intensity, CT number inaccuracy, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR). Results: Both validation methods have confirmed the appropriate accuracy of the simulation. In the quantitative analysis of the results before and after the scatter correction, it should be said that the pixel intensity patterns were close to each other, and the accuracy of the CT scan number reached <10%. Moreover, CNR and SNR have increased by more than 30%-65% respectively in all studied areas. Conclusion: The comparison of the results before and after scatter correction shows an improvement in CNR and SNR while a reduction in cupping artifact according to pixel intensity pattern and enhanced CT number accuracy.

Effect of Empowerment Model-Based Program on Quality of Life in Patients with Type 2 Diabetes: A Randomized Controlled Trial.

Background: Type 2 diabetes (T2D) is a chronic disease with a high prevalence globally, which is in the second place of importance for the investigation of chronic diseases. According to previous studies, Quality of Life (QOL) is low in diabetic patients. Hence, this study was conducted with the aim to evaluate the effect of the empowerment model on the QOL of patients with T2D. Materials and Methods: A randomized controlled trial was performed on 103 T2D patients over 18 years of age, with a definitive diagnosis of diabetes and medical records in a diabetic center. Patients were randomly assigned to either the intervention or the control groups. Routine education was presented to the control group, and the empowerment model was used for education in the experimental group for 8 weeks. The data collection tools used consisted of a demographic characteristics form and the diabetic clients QOL questionnaire. The one-way analysis of variance, Chi-square test, paired t-test, and independent t-test were used for data analysis. Results: After the intervention, there were significant differences between the two groups in terms of the physical (p = 0.003), mental (p = 0.002), social (p = 0.013), economic (p = 0.042), and illness and treatment dimensions of QOL (p = 0.033), as well as the total QOL score (p = 0.011). Conclusions: According to the results of this study, the training program based on empowerment significantly increased the QOL of patients with T2D. Therefore, using this method can be recommended in patients with T2D.