Preclinical Evaluation of 226Ac as a Theranostic Agent: Imaging, Dosimetry, and Therapy.

226Ac (t½ = 29.37 h) has been proposed as a theranostic radioisotope leveraging both its diagnostic γ-emissions and therapeutic α-emissions. 226Ac emits 158 and 230 keV γ-photons ideal for quantitative SPECT imaging and acts as an in vivo generator of 4 high-energy α-particles. Because of these nuclear decay properties, 226Ac has potential to act as a standalone theranostic isotope. In this proof-of-concept study, we evaluated a preclinical 226Ac-radiopharmaceutical for its theranostic efficacy and present the first 226Ac-targeted α-therapy study. Methods: 226Ac was produced at TRIUMF and labeled with the chelator-peptide bioconjugate crown-TATE. [226Ac]Ac-crown-TATE was selected to target neuroendocrine tumors in male NRG mice bearing AR42J tumor xenografts for SPECT imaging, biodistribution, and therapy studies. A preclinical SPECT/CT scanner acquired quantitative images reconstructed from both the 158 and the 230 keV emissions. Mice in the biodistribution study were euthanized at 1, 3, 5, 24, and 48 h after injection, and internal radiation dosimetry was derived for the tumor and organs of interest to establish appropriate therapeutic activity levels. Mice in the therapy study were administered 125, 250, or 375 kBq treatments and were monitored for tumor size and body condition. Results: We present quantitative SPECT images of the in vivo biodistribution of [226Ac]Ac-crown-TATE, which showed agreement with ex vivo measurements. Biodistribution studies demonstrated high uptake (>30%IA/g at 5 h after injection) and retention in the tumor, with an estimated mean absorbed dose coefficient of 222 mGy/kBq. [226Ac]Ac-crown-TATE treatments significantly extended the median survival from 7 d in the control groups to 16, 24, and 27 d in the 125, 250, and 375 kBq treatment groups, respectively. Survival was prolonged by slowing tumor growth, and no weight loss or toxicities were observed. Conclusion: This study highlights the theranostic potential of 226Ac as a standalone therapeutic isotope in addition to its demonstrated diagnostic capabilities to assess dosimetry in matched 225Ac-radiopharmaceuticals. Future studies will investigate maximum dose and toxicity to further explore the therapeutic potential of 226Ac-radiopharmaceuticals.

Quantitative SPECT imaging of 155Tb and 161Tb for preclinical theranostic radiopharmaceutical development.

BACKGROUND: Element-equivalent matched theranostic pairs facilitate quantitative in vivo imaging to establish pharmacokinetics and dosimetry estimates in the development of preclinical radiopharmaceuticals. Terbium radionuclides have significant potential as matched theranostic pairs for multipurpose applications in nuclear medicine. In particular, 155Tb (t1/2 = 5.32 d) and 161Tb (t1/2 = 6.89 d) have been proposed as a theranostic pair for their respective applications in single photon emission computed tomography (SPECT) imaging and targeted beta therapy. Our study assessed the performance of preclinical quantitative SPECT imaging with 155Tb and 161Tb. A hot rod resolution phantom with rod diameters ranging between 0.85 and 1.70 mm was filled with either 155Tb (21.8 ± 1.7 MBq/mL) or 161Tb (23.6 ± 1.9 MBq/mL) and scanned with the VECTor preclinical SPECT/CT scanner. Image performance was evaluated with two collimators: a high energy ultra high resolution (HEUHR) collimator and an extra ultra high sensitivity (UHS) collimator. SPECT images were reconstructed from photopeaks at 43.0 keV, 86.6 keV, and 105.3 keV for 155Tb and 48.9 keV and 74.6 keV for 161Tb. Quantitative SPECT images of the resolution phantoms were analyzed to report inter-rod contrast, recovery coefficients, and contrast-to-noise metrics. RESULTS: Quantitative SPECT images of the resolution phantom established that the HEUHR collimator resolved all rods for 155Tb and 161Tb, and the UHS collimator resolved rods ≥ 1.10 mm for 161Tb and ≥ 1.30 mm for 155Tb. The HEUHR collimator maintained better quantitative accuracy than the UHS collimator with recovery coefficients up to 92%. Contrast-to-noise metrics were also superior with the HEUHR collimator. CONCLUSIONS: Both 155Tb and 161Tb demonstrated potential for applications in preclinical quantitative SPECT imaging. The high-resolution collimator achieves < 0.85 mm resolution and maintains quantitative accuracy in small volumes which is advantageous for assessing sub organ activity distributions in small animals. This imaging method can provide critical quantitative information for assessing and optimizing preclinical Tb-radiopharmaceuticals.

Rest Tremor in Parkinson's Disease Is Associated with Ipsilateral Striatal Dopamine Transporter Binding.

BACKGROUND: The cardinal motor symptoms of Parkinson's disease (PD) include rigidity, bradykinesia, and rest tremor. Rigidity and bradykinesia correlate with contralateral nigrostriatal degeneration and striatal dopamine deficit, but association between striatal dopamine function and rest tremor has remained unclear. OBJECTIVE: The aim of this study was to investigate the possible link between dopamine function and rest tremor using Parkinson's Progression Markers Initiative dataset, the largest prospective neuroimaging cohort of patients with PD. METHODS: Clinical, [123I]N-ω-fluoropropyl-2ß-carbomethoxy-3ß-(4-iodophenyl)nortropane ([123I]FP-CIT) single photon emission computed tomography (SPECT), and structural magnetic resonance imaging data from 354 early PD patients and 166 healthy controls were included in this study. We employed a novel approach allowing nonlinear registration of individual scans accurately to a standard space and voxelwise analyses of the association between motor symptoms and striatal dopamine transporter (DAT) binding. RESULTS: Severity of both rigidity and bradykinesia was negatively associated with contralateral striatal DAT binding (PFWE < 0.05 [FWE, family-wise error corrected]). However, rest tremor amplitude was positively associated with increased ipsilateral DAT binding (PFWE < 0.05). The association between rest tremor and binding remained the same controlling for Hoehn & Yahr stage, Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score, bradykinesia-rigidity score, or motor phenotype. The association between rest tremor and binding was independent of bradykinesia-rigidity and replicated using 2-year follow-up data (PFWE < 0.05). CONCLUSION: In agreement with the existing literature, we did not find a consistent association between rest tremor and contralateral dopamine defect. However, our results demonstrate a link between rest tremor and increased or less decreased ipsilateral DAT binding. Our findings provide novel information about the association between dopaminergic function and parkinsonian rest tremor. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study.

PURPOSE: This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93 min dynamic PET data. METHODS: Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N = 360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t* ≥ 12 $ \ge 12$ -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93 min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93 min iIFs and tissue time activity curves from all reconstructed WB passes 12-93 min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. RESULTS: High correlations (>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p < 0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol). CONCLUSION: Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

Model-Informed Radiopharmaceutical Therapy Optimization: A Study on the Impact of PBPK Model Parameters on Physical, Biological, and Statistical Measures in 177Lu-PSMA Therapy.

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time-activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing.

BACKGROUND: The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations. METHODS: Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV. RESULTS: Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing. CONCLUSION: Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

Mathematical modeling of18F-Fluoromisonidazole (18F-FMISO) radiopharmaceutical transport in vascularized solid tumors.

18F-Fluoromisonidazole (18F-FMISO) is a highly promising positron emission tomography radiopharmaceutical for identifying hypoxic regions in solid tumors. This research employs spatiotemporal multi-scale mathematical modeling to explore how different levels of angiogenesis influence the transport of radiopharmaceuticals within tumors. In this study, two tumor geometries with heterogeneous and uniform distributions of capillary networks were employed to incorporate varying degrees of microvascular density. The synthetic image of the heterogeneous and vascularized tumor was generated by simulating the angiogenesis process. The proposed multi-scale spatiotemporal model accounts for intricate physiological and biochemical factors within the tumor microenvironment, such as the transvascular transport of the radiopharmaceutical agent, its movement into the interstitial space by diffusion and convection mechanisms, and ultimately its uptake by tumor cells. Results showed that both quantitative and semi-quantitative metrics of18F-FMISO uptake differ spatially and temporally at different stages during tumor growth. The presence of a high microvascular density in uniformly vascularized tumor increases cellular uptake, as it allows for more efficient release and rapid distribution of radiopharmaceutical molecules. This results in enhanced uptake compared to the heterogeneous vascularized tumor. In both heterogeneous and uniform distribution of microvessels in tumors, the diffusion transport mechanism has a more pronounced than convection. The findings of this study shed light on the transport phenomena behind18F-FMISO radiopharmaceutical distribution and its delivery in the tumor microenvironment, aiding oncologists in their routine decision-making processes.

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.

Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs).

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.

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.

Design and implementation of continuous bed motion (CBM) in Xtrim preclinical PET scanner for whole-body Imaging: MC simulation and experimental measurements.

PURPOSE: Preclinical PET scanners often have limited axial field-of-view for whole-body (WB) scanning of the small-animal. Step-and-shoot(S&S) acquisition mode requires multiple bed positions (BPs) to cover the scan length. Alternatively, in Continuous Bed Motion(CBM) mode, data acquisition is performed while the bed is continuously moving. In this study, to reduce acquisition time and enhance image quality, the CBM acquisition protocol was optimized and implemented on the Xtrim-PET preclinical scanner for WB imaging. METHODS: The over-scan percentage(OS%) in CBM mode was optimized by Monte Carlo simulation. Bed movement speed was optimized considering ranges from 0.1 to 2.0 mm s-1, and absolute system sensitivities with the optimal OS% were calculated. The performance of the scanner in CBM mode was measured, and compared with S&S mode based on the NEMA-NU4 standard. RESULTS: The optimal trade-off between absolute sensitivity and uniformity of sensitivity profile was achieved at OS-50 %. In comparison to S&S mode with maximum ring differences (MRD) of 9 and 23, the calculated equivalent speeds in CBM(OS-50 %) mode were 0.3 and 0.14 mm s-1, respectively. In terms of data acquisition with equal sensitivity in both CBM(OS-50 %) and S&S(MRD-9) modes, the total scan time in CBM mode decreased by 25.9 %, 47.7 %, 54.7 %, and 58.2 % for scan lengths of 1 to 4 BPs, respectively. CONCLUSION: The CBM mode enhances WB PET scans for small-animals, offering rapid data acquisition, high system sensitivity, and uniform axial sensitivity, leading to improved image quality. Its efficiency and customizable scan length and bed speed make it a superior alternative.

Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation.

Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5-4 resolution units, corresponding to 8.7-10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response.

Contralateral Hypertrophy Post Yttrium-90 Transarterial Radioembolization in Patients With Hepatocellular Carcinoma and Portal Vein Tumor Thrombus.

Objectives Contralateral hypertrophy of non-irradiated liver following Yttrium-90 (90Y) transarterial radioembolization (TARE) is increasingly recognized as an option to facilitate curative surgical resection in patients that would otherwise not be surgical candidates due to a small future liver remnant (FLR). This study aimed to investigate the correlation between patient features and liver hypertrophy and identify potential predictors for liver growth in patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT) undergoing TARE. Methodology Twenty-three patients with HCC and PVTT were included. Contralateral liver hypertrophy was assessed at six months posttreatment based on CT or MRI imaging. Thirteen patient features were selected for statistical and prediction analysis. Univariate Spearman correlation and analysis of variance (ANOVA) tests were performed. Subsequently, four feature-selection methods based on multivariate analysis were used to improve model generalization performance. The selected features were applied to train linear regression models, with fivefold cross-validation to assess the performance of the predicted models. Results The ratio of disease-free target liver volume to spared liver volume and total liver volume showed the highest correlations with contralateral hypertrophy (P-values = 0.03 and 0.05, respectively). In three out of four feature-selection methods, the feature of disease-free target liver volume to total liver volume ratio was selected, having positive correlations with the outcome and suggesting that more hypertrophy may be expected when more volume of disease-free liver is irradiated. Conclusions Contralateral hypertrophy post-90Y TARE can be an option for facilitating surgical resection in patients with otherwise small FLR.

Dual time-point [18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method.

PURPOSE: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy. METHODS: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data. RESULTS: In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method. CONCLUSIONS: Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Development of novel low-cost readout electronics for large field-of-view gamma camera detectors.

PURPOSE: Large scintillation crystals-based gamma cameras play a crucial role in nuclear medicine imaging. In this study, a large field-of-view (FOV) gamma detector consisting of 48 square PMTs developed using a new readout electronics, reducing 48 (6 × 8) analog signals to 14 (6 + 8) analog sums of each row and column, with reduced complexity and cost while preserving image quality. METHODS: All 14 analog signals were converted to digital signals using AD9257 high-speed analog to digital (ADC) converters driven by the SPARTAN-6 family of field-programmable gate arrays (FPGA) in order to calculate the signal integrals. The positioning algorithm was based on the digital correlated signal enhancement (CSE) algorithm implemented in the acquisition software. The performance characteristics of the developed gamma camera were measured using the NEMA NU 1-2018 standards. RESULTS: The measured energy resolution of the developed detector was 8.7 % at 140 keV, with an intrinsic spatial resolution of 3.9 mm. The uniformity was within 0.6 %, while the linearity was within 0.1 %. CONCLUSION: The performance evaluation demonstrated that the developed detector has suitable specifications for high-end nuclear medicine imaging.

Localized radiotherapy of solid tumors using radiopharmaceutical loaded implantable system: insights from a mathematical model.

Introduction: Computational models yield valuable insights into biological interactions not fully elucidated by experimental approaches. This study investigates an innovative spatiotemporal model for simulating the controlled release and dispersion of radiopharmaceutical therapy (RPT) using 177Lu-PSMA, a prostate-specific membrane antigen (PSMA) targeted radiopharmaceutical, within solid tumors via a dual-release implantable delivery system. Local delivery of anticancer agents presents a strategic approach to mitigate adverse effects while optimizing therapeutic outcomes. Methods: This study evaluates various factors impacting RPT efficacy, including hypoxia region extension, binding affinity, and initial drug dosage, employing a novel 3-dimensional computational model. Analysis gauges the influence of these factors on radiopharmaceutical agent concentration within the tumor microenvironment. Furthermore, spatial and temporal radiopharmaceutical distribution within both the tumor and surrounding tissue is explored. Results: Analysis indicates a significantly higher total concentration area under the curve within the tumor region compared to surrounding normal tissue. Moreover, drug distribution exhibits notably superior efficacy compared to the radiation source. Additionally, low microvascular density in extended hypoxia regions enhances drug availability, facilitating improved binding to PSMA receptors and enhancing therapeutic effectiveness. Reductions in the dissociation constant (KD) lead to heightened binding affinity and increased internalized drug concentration. Evaluation of initial radioactivities (7.1×107, 7.1×108, and 7.1×109 [Bq]) indicates that an activity of 7.1×108 [Bq] offers a favorable balance between tumor cell elimination and minimal impact on normal tissues. Discussion: These findings underscore the potential of localized radiopharmaceutical delivery strategies and emphasize the crucial role of released drugs relative to the radiation source (implant) in effective tumor treatment. Decreasing the proximity of the drug to the microvascular network and enhancing its distribution within the tumor promote a more effective therapeutic outcome. The study furnishes valuable insights for future experimental investigations and clinical trials, aiming to refine medication protocols and minimize reliance on in vivo testing.

Radiopharmaceutical transport in solid tumors via a 3-dimensional image-based spatiotemporal model.

Lutetium-177 prostate-specific membrane antigen (177Lu-PSMA)-targeted radiopharmaceutical therapy is a clinically approved treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Even though common practice reluctantly follows "one size fits all" approach, medical community believes there is significant room for deeper understanding and personalization of radiopharmaceutical therapies. To pursue this aim, we present a 3-dimensional spatiotemporal radiopharmaceutical delivery model based on clinical imaging data to simulate pharmacokinetic of 177Lu-PSMA within the prostate tumors. The model includes interstitial flow, radiopharmaceutical transport in tissues, receptor cycles, association/dissociation with ligands, synthesis of PSMA receptors, receptor recycling, internalization of radiopharmaceuticals, and degradation of receptors and drugs. The model was studied for a range of values for injection amount (100-1000 nmol), receptor density (10-500 nmol•l-1), and recycling rate of receptors (10-4 to 10-1 min-1). Furthermore, injection type, different convection-diffusion-reaction mechanisms, characteristic time scales, and length scales are discussed. The study found that increasing receptor density, ligand amount, and labeled ligands improved radiopharmaceutical uptake in the tumor. A high receptor recycling rate (0.1 min-1) increased radiopharmaceutical concentration by promoting repeated binding to tumor cell receptors. Continuous infusion results in higher radiopharmaceutical concentrations within tumors compared to bolus administration. These insights are crucial for advancing targeted therapy for prostate cancer by understanding the mechanism of radiopharmaceutical distribution in tumors. Furthermore, measures of characteristic length and advection time scale were computed. The presented spatiotemporal tumor transport model can analyze different physiological parameters affecting 177Lu-PSMA delivery.

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.

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.

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.