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.

Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies.

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes. To address this limitation, we propose the development of theranostic digital twins (TDTs) to personalize RPTs based on actual patient data. Our proposed roadmap outlines the steps needed to create and refine TDTs that can optimize radiation dose to tumors while minimizing toxicity to organs at risk. The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, which are additionally linked to a radiobiological optimizer and an immunological modulator, taking into account factors that influence RPT response. By using TDT models, we envisage the ability to perform virtual clinical trials, selecting therapies towards improved treatment outcomes while minimizing risks associated with secondary effects. This framework could empower practitioners to ultimately develop tailored RPT solutions for subgroups and individual patients, thus improving the precision, accuracy, and efficacy of treatments while minimizing risks to patients. By incorporating TDT models into RPTs, we can pave the way for a new era of precision medicine in cancer treatment.

In vivoquantitative SPECT imaging of actinium-226: feasibility and proof-of-concept.

Objective.225Ac radiopharmaceuticals have tremendous potential for targeted alpha therapy, however,225Ac (t1/2= 9.9 d) lacks direct gamma emissions forin vivoimaging.226Ac (t1/2= 29.4 h) is a promising element-equivalent matched diagnostic radionuclide for preclinical evaluation of225Ac radiopharmaceuticals.226Ac has two gamma emissions (158 keV and 230 keV) suitable for SPECT imaging. This work is the first feasibility study forin vivoquantitative226Ac SPECT imaging and validation of activity estimation.Approach.226Ac was produced at TRIUMF (Vancouver, Canada) with its Isotope Separator and Accelerator (ISAC) facility. [226Ac]Ac3+was radiolabelled with the bioconjugate crown-TATE developed for therapeutic targeting of neuroendocrine tumours. Mice with AR42J tumour xenografts were injected with either 2 MBq of [226Ac]Ac-crown-TATE or 4 MBq of free [226Ac]Ac3+activity and were scanned at 1, 2.5, 5, and 24 h post injection in a preclinical microSPECT/CT. Quantitative SPECT images were reconstructed from the 158 keV and 230 keV photopeaks with attenuation, background, and scatter corrections. Image-based226Ac activity measurements were assessed from volumes of interest within tumours and organs of interest. Imaging data was compared withex vivobiodistribution measured via gamma counter.Main results. We present, to the best of our knowledge, the first everin vivoquantitative SPECT images of226Ac activity distributions. Time-activity curves derived from SPECT images quantify thein vivobiodistribution of [226Ac]Ac-crown-TATE and free [226Ac]Ac3+activity. Image-based activity measurements in the tumours and organs of interest corresponded well withex vivobiodistribution measurements.Significance. Here in, we established the feasibility ofin vivo226Ac quantitative SPECT imaging for accurate measurement of actinium biodistribution in a preclinical model. This imaging method could facilitate more efficient development of novel actinium labelled compounds by providing accurate quantitativein vivopharmacokinetic information essential for estimating toxicities, dosimetry, and therapeutic potency.

DosePatch: physics-inspired cropping layout for patch-based Monte Carlo simulations to provide fast and accurate internal dosimetry.

BACKGROUND: Dosimetry-based personalized therapy was shown to have clinical benefits e.g. in liver selective internal radiation therapy (SIRT). Yet, there is no consensus about its introduction into clinical practice, mainly as Monte Carlo simulations (gold standard for dosimetry) involve massive computation time. We addressed the problem of computation time and tested a patch-based approach for Monte Carlo simulations for internal dosimetry to improve parallelization. We introduce a physics-inspired cropping layout for patch-based MC dosimetry, and compare it to cropping layouts of the literature as well as dosimetry using organ-S-values, and dose kernels, taking whole-body Monte Carlo simulations as ground truth. This was evaluated in five patients receiving Yttrium-90 liver SIRT. RESULTS: The patch-based Monte Carlo approach yielded the closest results to the ground truth, making it a valid alternative to the conventional approach. Our physics-inspired cropping layout and mosaicking scheme yielded a voxel-wise error of < 2% compared to whole-body Monte Carlo in soft tissue, while requiring only ≈  10% of the time. CONCLUSIONS: This work demonstrates the feasibility and accuracy of physics-inspired cropping layouts for patch-based Monte Carlo simulations.

Assessing Response to PSMA Radiopharmaceutical Therapies with Single SPECT Imaging at 24 Hours After Injection.

Understanding the relationship between lesion-absorbed dose and tumor response in 177Lu-PSMA-617 radiopharmaceutical therapies (RPTs) remains complex. We aimed to investigate whether baseline lesion-absorbed dose can predict lesion-based responses and to explore the connection between lesion-absorbed dose and prostate-specific antigen (PSA) response. Methods: In this retrospective study, we evaluated 50 patients with 335 index lesions undergoing 177Lu-PSMA-617 RPT, who had dosimetry analysis performed on SPECT/CT at 24 h after cycles 1 and 2. First, we identified the index lesions for each patient and measured the lesion-based absorbed doses. Lesion-based response was calculated after cycle 2. Additionally, PSA50 response (a decline of 50% from baseline PSA) after cycle 2 was also calculated. The respective responses for mean and maximum absorbed doses and prostate-specific membrane antigen (PSMA) volumetric intensity product (VIP-PSMA) at cycles 1 and 2 were termed SPECTmean, SPECTmaximum, and SPECTVIP-PSMA, respectively. Results: Of the 50 patients reviewed, 46% achieved a PSA50 response after cycle 2. Of the 335 index lesions, 58% were osseous, 32% were lymph nodes, and 10% were soft-tissue metastatic lesions. The SPECT lesion-based responses were higher in PSA responders than in nonresponders (SPECTmean response of 46.8% ± 26.1% vs. 26.2% ± 24.5%, P = 0.007; SPECTmaximum response of 45% ± 25.1% vs. 19% ± 27.0%, P = 0.001; SPECTVIP-PSMA response of 49.2% ± 30.3% vs. 14% ± 34.7%, P = 0.0005). An association was observed between PSA response and SPECTVIP-PSMA response (R 2 = 0.40 and P < 0.0001). A limited relationship was found between baseline absorbed dose measured with a 24-h single time point and SPECT lesion-based response (R 2 = 0.05, P = 0.001, and R 2 = 0.03, P = 0.007, for mean and maximum absorbed doses, respectively). Conclusion: In this retrospective study, quantitative lesion-based response correlated with patient-level PSA response. We observed a limited relationship between baseline absorbed dose and lesion-based responses. Most of the variance in response remains unexplained solely by baseline absorbed dose. Establishment of a dose-response relationship in RPT with a single time point at 24 h presented some limitations.

Heterogeneous PSMA ligand uptake inside parotid glands.

The purpose of this investigation is to quantify the spatial heterogeneity of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) uptake within parotid glands. We aim to quantify patterns in well-defined regions to facilitate further investigations. Furthermore, we investigate whether uptake is correlated with computed tomography (CT) texture features. METHODS: Parotid glands from [18F]DCFPyL PSMA PET/CT images of 30 prostate cancer patients were analyzed. Uptake patterns were assessed with various segmentation schemes. Spearman's rank correlation coefficient was calculated between PSMA PET uptake and feature values of a Grey Level Run Length Matrix using a long and short run length emphasis (GLRLML and GLRLMS) in subregions of the parotid gland. RESULTS: PSMA PET uptake was significantly higher (p < 0.001) in lateral/posterior regions of the glands than anterior/medial regions. Maximum uptake was found in the lateral half of parotid glands in 50 out of 60 glands. The difference in SUVmean between parotid halves is greatest when parotids are divided by a plane separating the anterior/medial and posterior/lateral halves symmetrically (out of 120 bisections tested). PSMA PET uptake was significantly correlated with CT GLRLML (p < 0.001), and anti-correlated with CT GLRLMS (p < 0.001). CONCLUSION: Uptake of PSMA PET is heterogeneous within parotid glands, with uptake biased towards lateral/posterior regions. Uptake within parotid glands was strongly correlated with CT texture feature maps.

Evaluating Outcome Prediction via Baseline, End-of-Treatment, and Delta Radiomics on PET-CT Images of Primary Mediastinal Large B-Cell Lymphoma.

OBJECTIVES: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. MATERIAL AND METHODS: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [18F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). RESULTS: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). CONCLUSION: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [18F]FDG PET-CT scans.

Neural blind deconvolution for deblurring and supersampling PSMA PET.

Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical 'deblurred' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called 'neural blind deconvolution' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model's ability to predict accurate kernels by re-running the model after applying artificial 'pseudokernels' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.

Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.

TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.

PURPOSE: Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach. METHODS: Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians. RESULTS: Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded. CONCLUSION: TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Development of the quantitative PET prostate phantom (Q3P) for improved quality assurance of 18F-PSMA PET imaging in metastatic prostate cancer.

BACKGROUND: Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37 mm fillable spheres. The NEMA phantom neglects, however, to model focal (3-10-mm), high-uptake lesions that are increasingly observed in prostate-specific membrane antigen (PSMA) PET images. PSMA-targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging. PURPOSE: In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F-PSMA PET scans. METHODS: A one-piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F-PSMA PET scans. Shell-less radioactive spheres (3-16-mm) were cast using epoxy resin mixed with sodium-22 (22Na), a long half-life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3-37-mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5-min PET scans were acquired on a 5-ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations. RESULTS: The manufactured phantom is portable (5.7 kg) and scan preparation takes less than 40 min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post-reconstruction smoothing, were 130.3% (16 mm), 147.1% (10 mm), 87.2% (6 mm), and 7.0% (3 mm) for RCmax, which decreased to 91.1% (16 mm), 90.6% (10 mm), 53.2% (6 mm), and 3.6% (3 mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17 mm), 123.6% (10 mm), 106.5% (7 mm), and 23.3% (3 mm) for RCmax, which was reduced to 76.7% (17 mm), 77.7% (10 mm), 66.8% (7 mm), and 13.5% (3 mm), for RCmean. CONCLUSIONS: A standardized imaging phantom was developed for lesion quantification assessment in 18F-PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F-PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer.

Regulatory T-cell frequency and function in acute myocardial infarction patients and its correlation with ventricular dysfunction.

A high percentage of patients with acute coronary syndrome develop heart failure due to the ischemic event. Regulatory T (Treg) cells are lymphocytes with suppressive capacity that control the immune response and include the conventional CD4+ CD25hi Foxp3+ cells and the CD4+ CD25var CD69+ LAP+ Foxp3- IL-10+ cells. No human follow-up studies focus on Treg cells' behavior after infarction and their possible relationship with ventricular function as a sign of postischemic cardiac remodeling. This study aimed to analyze, by flow cytometry, the circulating levels of CD69+ Treg cells and CD4+ CD25hi Foxp3+ cells, their IL-10+ production as well as their function in patients with acute myocardial infarction (AMI), and its possible relation with ventricular dysfunction. We found a significant difference in the percentage of CD4+ CD25hi Foxp3+ cells and IL-10+ MFI in patients with AMI at 72 hours compared with the healthy control group, and the levels of these cells were reduced 6 months post-AMI. Regarding the suppressive function of CD4+ CD25+ regulatory cells, they were dysfunctional at 3 and 6 months post-AMI. The frequency of CD69+ Treg cells was similar between patients with AMI at 72 hours postinfarction and the control groups. Moreover, the frequency of CD69+ Treg cells at 3 and 6 months postischemic event did not vary over time. Treg cells play a role in regulating inflammation after an AMI, and its function may be compromised in this pathology. This work is the first report to evaluate CD69+ Foxp3- Treg cells in AMI patients.

Bruton's Tyrosine Kinase Inhibitors: Recent Updates.

Bruton's tyrosine kinase (BTK) inhibitors have revolutionized the landscape for the treatment of hematological malignancies, solid tumors, and, recently, autoimmune disorders. The BTK receptor is expressed in several hematopoietic cells such as macrophages, neutrophils, mast cells, and osteoclasts. Similarly, the BTK receptor is involved in signaling pathways such as chemokine receptor signaling, Toll-like receptor signaling, and Fc receptor signaling. Due to their unique mechanism, these agents provide a diverse utility in a variety of disease states not limited to the field of malignant hematology and are generally well-tolerated.

Exploration of commercial cyclen-based chelators for mercury-197 m/g incorporation into theranostic radiopharmaceuticals.

A comprehensive investigation of the Hg2+ coordination chemistry and 197m/gHg radiolabeling capabilities of cyclen-based commercial chelators, namely, DOTA and DOTAM (aka TCMC), along with their bifunctional counterparts, p-SCN-Bn-DOTA and p-SCN-Bn-TCMC, was conducted to assess the suitability of these frameworks as bifunctional chelators for the 197m/gHg2+ theranostic pair. Radiolabeling studies revealed that TCMC and DOTA exhibited low radiochemical yields (0%-6%), even when subjected to harsh conditions (80°C) and high ligand concentrations (10-4 M). In contrast, p-SCN-Bn-TCMC and p-SCN-Bn-DOTA demonstrated significantly higher 197m/gHg radiochemical yields (100% ± 0.0% and 70.9% ± 1.1%, respectively) under the same conditions. The [197 m/gHg]Hg-p-SCN-Bn-TCMC complex was kinetically inert when challenged against human serum and glutathione. To understand the differences in labeling between the commercial chelators and their bifunctional counterparts, non-radioactive natHg2+ complexes were assessed using NMR spectroscopy and DFT calculations. The NMR spectra of Hg-TCMC and Hg-p-SCN-Bn-TCMC suggested binding of the Hg2+ ion through the cyclen backbone framework. DFT studies indicated that binding of the Hg2+ ion within the backbone forms a thermodynamically stable product. However, competition can form between isothiocyanate binding and binding through the macrocycle, which was experimentally observed. The isothiocyanate bound coordination product was dominant at the radiochemical scale as, in comparison, the macrocycle bound product was seen at the NMR scale, agreeing with the DFT result. Furthermore, a bioconjugate of TCMC (TCMC-PSMA) targeting prostate-specific membrane antigen was synthesized and radiolabeled, resulting in an apparent molar activity of 0.089 MBq/nmol. However, the complex demonstrated significant degradation over 24 h when exposed to human serum and glutathione. Subsequently, cell binding assays were conducted, revealing a Ki value ranging from 19.0 to 19.6 nM. This research provides crucial insight into the effectiveness of current commercial chelators in the context of 197m/gHg2+ radiolabeling. It underscores the necessity for the development of specific and customized chelators to these unique "soft" radiometals to advance 197m/gHg2+ radiopharmaceuticals.

Development, preclinical evaluation and preliminary dosimetry profiling of SB03178, a first-of-its-kind benzo[h]quinoline-based fibroblast activation protein-α-targeted radiotheranostic for cancer imaging and therapy.

Fibroblast activation protein-α (FAP) is a marker of cancer-associated fibroblasts (CAFs) that constitute a significant portion of most carcinomas. Since it plays a critical role in tumor growth and metastasis, its timely detection to identify tumor lesions in early developmental stages using targeted radiopharmaceuticals has gained significant impetus. In the present work, two novel FAP-targeted precursors SB03178 and SB04033 comprising of an atypical benzo[h]quinoline construct were synthesized and either chelated to diagnostic radionuclide gallium-68 or therapeutic radionuclide lutetium-177, with ≥90% radiochemical purities and 22-76% decay-corrected radiochemical yields. natGa-labeled complexes displayed dose-dependent FAP inhibition, with binding potency of natGa-SB03178 being ∼17 times higher than natGa-SB04033. To evaluate their pharmacokinetic profiles, PET imaging and ex vivo biodistribution analyses were executed in FAP-overexpressing HEK293T:hFAP tumor-bearing mice. While both tracers displayed clear tumor visualization that was primarily FAP-arbitrated, with negligible uptake in most peripheral tissues, [68Ga]Ga-SB03178 demonstrated higher tumor uptake and superior tumor-to-background contrast ratios than [68Ga]Ga-SB04033. 177Lu-labeled SB03178 was subjected to tumor retention studies, mouse dosimetry profiling and mouse-to-human dose extrapolations also using the HEK293T:hFAP tumor model. [177Lu]Lu-SB03178 exhibited a combination of high and sustained tumor uptake, with excellent tumor-to-critical organ uptake ratios resulting in a high radiation absorbed dose to the tumor and a low estimated whole-body dose to humans. Our preliminary findings are considerably encouraging to support clinical development of [68Ga]Ga-/[177Lu]Lu-SB03178 theranostic pair for use in a vast majority of FAP-overexpressing neoplasms, particularly carcinomas.

Spatiotemporal modeling of radiopharmaceutical transport in solid tumors: Application to 177Lu-PSMA therapy of prostate cancer.

BACKGROUND AND OBJECTIVE: 177Lu-labeled prostate-specific membrane antigen (PSMA) radiopharmaceutical therapy (RPT) represents a pivotal advancement in addressing prostate cancer. However, existing therapies, while promising, remain incompletely understood and optimized. Computational models offer potential insights into RPTs, aiding in clinical drug delivery enhancement. In this study, we investigate the impact of various physiological parameters on the delivery of 177Lu-PSMA-617 RPT using the convection-diffusion-reaction (CDR) model. METHODS: Our investigation encompasses tumor geometry and surrounding tissue, characterized by well-defined boundaries and initial conditions. Utilizing the finite element method, we solve governing equations across a range of parameters: dissociation constant KD (1, 0.1, 0.01 [nM]), internalization rate (0.01-0.0001 [min-1]), diverse tumor shapes, and variable necrotic zone sizes. This model can provide an accurate analysis of radiopharmaceutical delivery from the injection site to the tumor cell, including drug transport in the vascular, interstitial, and intracellular spaces, and considering important parameters (e.g., drug extravasation from microvessels or to lymphatic vessels, the extracellular matrix, receptors, and intracellular space). RESULTS: Our findings reveal significant enhancements in tumor-absorbed doses as KD decreases. This outcome can be attributed to the higher affinity of radiopharmaceuticals for PSMA receptors as KD diminishes, facilitating a more efficient binding and retention of the therapeutic agent within the tumor microenvironment. Additionally, tumor-absorbed doses for KD ∼ 1 [nM] show an upward trend with higher internalization rates. This observation can be rationalized by considering that a greater internalization rate would result in a higher proportion of radiopharmaceuticals being taken up by tumor cells after binding to receptors on the cell surface. Notably, tumor shape and necrotic zone size exhibit limited influence on tumor absorbed dose. CONCLUSIONS: The present study employs the CDR model to explore the role of physiological parameters in shaping 177Lu-PSMA-617 RPT delivery. These findings provide insights for improving prostate cancer therapy by understanding radiopharmaceutical transport dynamics. This computational approach contributes to advancing our understanding of radiopharmaceutical delivery mechanisms and has implications for enhancing treatment efficacy.

Antifibrotic activity of carbon quantum dots in a human in vitro model of non-alcoholic steatohepatitis using hepatic stellate cells.

Around 33% of the global population suffers from non-alcoholic fatty liver disease (NAFLD). From these patients, 30% of them progress into non-alcoholic steatohepatitis (NASH), the critical point where lack of treatment leads to cirrhosis and hepatic failure. Moreover, to date, there are no approved therapeutic options available for NASH. It is known that hepatic stellate cell (HSC) activation contributes the most to hepatic disfunction, leading to reactive oxygen species (ROS) accumulation and chronic inflammation, and that the use of nanomaterials to deliver antioxidants may have potential to reduce the activity of activated HSCs. Therefore, we implemented a human in vitro co-culture model in which we take into consideration two factors related to NASH and fibrosis: human hepatic stellate cells from a NASH diagnosed donor (HHSC-N) and peripheral blood mononuclear cells (PBMCs), particularly lymphocytes. The co-cultures were treated with: (1) carbon quantum dots (CD) or (2) lactoferrin conjugated CD (CD-LF) for 24 h or 72 h. CD and CD-LF treatments significantly downregulated profibrotic genes' expression levels of ACTA2, COL1A1, and TIMP1 in co-cultured HHSC-N at 72 h. Also, we assayed the inflammatory response by quantifying the concentrations of cytokines IL-22, IL-10, IFN-γ and IL-4 present in the co-culture's conditioned media whose concentrations may suggest a resolution-associated response in progress. Our findings may serve as a starting point for the development of a NASH treatment using bio-nanotechnology.