Frontiers in nuclear medicine (Lausanne, Switzerland)最新文献

Radiocobalt-based theranostics has emerged as a promising platform in nuclear medicine that offers dual capabilities for both diagnostic imaging and targeted radionuclide therapy. ⁵⁵Co (t_1/2 = 17.53 h, β⁺ = 77%, E _γ  = 931.1 keV, I _γ  = 75%) and ^58mCo (t_1/2 = 9.10 h, IC = 100%) serve as an elementally matched pair for positron emission tomography and targeted Auger electron therapy, respectively, that enable a more personalized approach to cancer management, where imaging with ⁵⁵Co can help to guide and predict therapeutic outcomes for ^58mCo therapy. The unique coordination chemistry of cobalt allows for stable complexation with various chelators, enhancing in vivo stability and targeting efficacy when conjugated to biomolecules such as peptides, antibodies, and small molecules. Recent developments in radiolabeling techniques, chelator design, and preclinical evaluations have significantly improved the pharmacokinetic profiles and tumor specificity of radiocobalt-based radiopharmaceuticals. The aim of this mini review is to provide an overview of the recent advancements and applications of radiocobalt isotopes with a particular focus on the production, chelation chemistry, and in vivo targeting of ⁵⁵Co- and ^58mCo-labelled radiopharmaceuticals over the last 5 years. While challenges still exist in production scalability, dosimetry optimization, and clinical translations, the current trajectory suggests a growing role for radiocobalt-based theranostics in precision oncology.

Purpose: Long axial field-of-view (LAFOV) PET systems like the Siemens Biograph Vision Quadra offer unprecedented sensitivity and imaging capabilities, but compliance with EARL standards across all acquisition modes remains unexplored. This study aimed to identify reconstruction parameters meeting EARL 1 and 2 compliance for static and continuous bed motion (CBM) acquisitions in High Sensitivity (HS) and Ultra-High Sensitivity (UHS) modes on the Quadra. The research focused on optimising image quality while maintaining compliance with quantitative standards.

Methods: The International Electrotechnical Commission (IEC) body phantom was filled with ¹⁸F-FDG in a 10:1 sphere-to-background activity ratio and scanned at five positions across the field of view (FOV) using static and CBM acquisitions in HS and UHS modes. Reconstructions used standard clinical parameters, varied with Gaussian filters (1-7 mm) and matrix sizes (440, 220, 128). EARL compliance was assessed with the EARL tool to evaluate SUV recovery coefficients (RCSUVmean, RCSUVmax, RCSUVpeak). Patient images were reconstructed using standard and EARL-compliant parameters for comparison.

Results: Reconstruction parameters achieving EARL compliance were identified for all acquisition modes, with no differences between static and CBM reconstructions. Achieving EARL compliance required significant image quality reductions, especially for EARL 1, with greater degradation in UHS mode. Patient images reconstructed with EARL-compliant parameters appeared smoother and had reduced contrast compared to clinical reconstructions.

Conclusion: While EARL compliance ensures quantitative standardisation, it significantly reduces image quality, especially on advanced LAFOV PET systems. An updated "EARL 3" standard is needed to reflect the capabilities of modern systems.

Introduction: Ventilation-perfusion (V/Q) nuclear scintigraphy remains a vital diagnostic tool for assessing pulmonary embolism (PE) and other lung conditions. Interpretation of these images requires specific expertise which may benefit from recent advances in artificial intelligence (AI) to improve diagnostic accuracy and confidence in reporting. Our study aims to develop a multi-center dataset combining imaging and clinical reports to aid in creating AI models for PE diagnosis.

Methods: We established a comprehensive imaging registry encompassing patient-level V/Q image data along with relevant clinical reports, CTPA images, DVT ultrasound impressions, D-dimer lab tests, and thrombosis unit records. Data extraction was performed at two hospitals in Canada and at multiple sites in the United States, followed by a rigorous de-identification process. We utilized the V7 Darwin platform for crowdsourced annotation of V/Q images including segmentation of V/Q mismatched vascular defects. The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation.

Results: A query of The Ottawa Hospital's data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. Additional contributions included 600 studies from University Health Toronto, and 385 studies by private company Segmed Inc. resulting in a total of 3,122 V/Q planar and SPECT images. The majority of studies were acquired using Siemens, Philips, and GE scanners, adhering to standardized local imaging protocols. After annotating 1,500 studies from The Ottawa Hospital, the analysis identified 138 high-probability, 168 intermediate-probability, 266 low-probability, 244 very low-probability, and 669 normal, and 15 normal perfusion with reversed mismatched ventilation defect studies. In 1,500 patients were 3,511 segmented vascular perfusion defects.

Conclusion: The VQ4PEDB comprised 8 unique ventilation agents and 11 unique scanners. The VQ4PEDB database is unique in its depth and breadth in the domain of V/Q nuclear scintigraphy for PE, comprising clinical reports, imaging studies, and annotations. We share our experience in addressing challenges associated with data retrieval, de-identification, and annotation. VQ4PEDB will be a valuable resource to development and validate AI models for diagnosing PE and other pulmonary diseases.

[This corrects the article DOI: 10.3389/fnume.2025.1575026.].

Background: The characterization of solitary pulmonary nodules (SPNs) as malignant or benign remains a diagnostic challenge using conventional imaging parameters. The literature suggests using combined Positron Emission Tomography (PET) and Computed Tomography (CT) to characterise a SPN. Radiomics and machine learning are other promising technologies which can be utilised to characterise the SPN.

Purpose: This study explores the potential of PET radiomics signatures and machine learning algorithms to characterise the SPN.

Methods: This retrospective study aimed to characterize solitary pulmonary nodules (SPNs) using PET radiomics. A total of 163 patients who underwent PET/CT imaging were included in this study. A total of 1,098 features were extracted from PET images using PyRadiomics. To optimize model performance two strategies i.e., (a) feature selection and (b) feature reduction techniques were employed, including hierarchical clustering, RFE in feature selection, and PCA in feature reduction. To address outcome class imbalance, the dataset was statistically resampled (SMOTE). A random forest models was developed using original training set (RF-Model-O & RF-PCA-Model-O) and balanced training dataset (RF-Model-B & RF-PCA-Model-B) and validated on the test datasets. Additionally, 5-fold cross-validation and bootstrap validation was also performed. The model's performance was assessed using various metrics, such as accuracy, AUC, precision, recall, and F1-score.

Results: Of the 163 patients (aged 36-76 years, mean age 58 ± 7), 117 had malignant disease and 46 had granulomatous or benign conditions. In Strategy (a), five radiomic features were identified as optimal using hierarchical clustering and RFE. In Strategy (b), five principal components were deemed optimal using PCA. The model accuracy of RF-Model-O and RF-Model-B in the train-test validation, 5-fold cross-validation and bootstrap validation were found to be 0.8, 0.80 ± 0.07, 0.84 ± 1.11 and 0.8, 0.83 ± 0.10, 0.80 ± 0.07 in Strategy (a). Similarly, the model accuracy of RF-PCA-Model-O and RF-PCA-Model-B in the train-test validation, 5-fold cross-validation and bootstrap validation were found to be 0.84, 0.80 ± 0.07, 0.84 ± 07 and 0.74, 0.80 ± 0.08, 0.75 ± 0.08 in Strategy (b).

Conclusion: The PET radiomics demonstrated excellent performance in characterizing SPNs as benign or malignant.

Introduction: The COVID-19 pandemic has profoundly affected mental health, with lockdown periods particularly exacerbating negative emotions such as fear, sadness, and uncertainty. This study examines brain metabolic changes associated with the psychological context of the first French COVID-19 lockdown in vulnerable individuals.

Methods: As a proxy measure of the psychological context, we used a composite negative-emotion score derived from an open-source X/Twitter dataset ("The First French COVID-19 Lockdown Twitter Dataset"), designed to capture public sentiment over the 55-day lockdown. This score was day-by-day correlated with whole-brain voxel-based [18F]FDG PET imaging in 95 patients with neurological conditions, using statistical parametric mapping (SPM) (p-voxel < 0.001, k > 108).

Results: A significant negative correlation was found between daily negative-emotion scores and metabolism in the right ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC), key regions of the brain's fear circuit. Inter-regional correlation analysis (IRCA) of metabolic connectivity from the right vmPFC/ACC further revealed a right limbic-dominant network including the amygdala, hippocampus, thalamus, and basal ganglia.

Discussion: These findings highlight the sensitivity of the right vmPFC/ACC to societal emotional stressors, suggesting a potential cerebral substrate for the increase in psychological and psychiatric disorders observed during the pandemic. Further research is needed to validate these results in larger populations and to explore their longitudinal implications, to better understand the neurological impact of collective stress.

Kinetic modelling of brain PET data is crucial for estimating quantitative biological parameters, traditionally requiring arterial sampling. This study evaluated whether arterial samples could be omitted to estimate the image-derived input function (IDIF) using a long axial field-of-view PET scanner. The use of internal carotid arteries (ICA) for IDIF estimation, along with venous samples for plasma-to-whole blood ratios and plasma parent fractions, was also assessed. Six healthy volunteers underwent [¹⁸F]MC225 scans with manual arterial sampling. IDIFs were derived from the aortic arch (IDIF_AA) and calibrated using manual arterial samples (IDIF_{AA_CAL}). ICA-derived IDIF was also calibrated (IDIF_{CA_CAL}) and compared to IDIF_{AA_CAL}. In a separate group of six volunteers, venous and arterial samples were collected to evaluate plasma-to-whole blood ratios, plasma parent fractions, and IDIF calibration (IDIF_{CA_CAL_VEN}). Volume of distribution (V_T) of different brain regions was estimated for all IDIFs techniques, corrected for plasma-to-whole blood ratio and plasma parent fraction (IDIF_AA,P, IDIF_{AA_CAL,P}, IDIF_{ICA_CAL,P} and IDIF_{ICA_CAL_VEN_P}). Our findings revealed discrepancies between IDIF_AA and arterial samples, highlighting the importance of calibration. The differences between IDIF_AA,P and IDIF_{AA_CAL,P} were 9.2% for area under the curve and 4.0% for brain V_T. IDIF_{ICA_CAL,P} showed strong agreement with IDIF_{A_CAL,P}, with 1.2% V_T difference. Venous sampling showed consistent agreement with arterial sampling for plasma parameters but was unreliable for IDIF calibration, leading to 39% V_T differences. This study emphasises that arterial samples are still required for IDIF calibration and reliable V_T estimation for [¹⁸F]MC225 PET tracer. ICA-derived IDIF, when calibrated, provides reliable V_T estimates. Venous sampling is a potential alternative for estimating plasma parameters, but it is unsuitable for IDIF calibration.

Trial registry: NCT05618119 (clinicaltrials.gov/study/NCT05618119).

Background: Calcinosis is a morbid complication of dermatomyositis (DM) and systemic sclerosis (SSc) with no effective pharmacologic treatment or validated whole-body assessment modality. ¹⁸F-NaF PET/CT may help to quantify and characterize calcinosis.

Methods: In this pilot study, we enrolled three adults with DM and three with SSc, all with new calcinosis deposits. Each underwent ¹⁸F-NaF PET/CT and clinical examination with semi-quantitative scoring of calcinosis. We described the ¹⁸F-NaF PET/CT findings and compared these to CT imaging alone as well as to clinical examination.

Results: Calcinosis was noted on ¹⁸F-NaF PET/CT in the subcutaneous tissue in all patients and the muscle in three patients, including two with SSc. The average semi-quantitative score was 23.5 by ¹⁸F-NaF PET/CT and 20 by clinical exam. Wilcoxon signed rank test indicated greater scores by ¹⁸F-NaF PET/CT than by clinical exam (p = 0.0264). ¹⁸F-NaF uptake varied among calcinosis deposits and occurred without corresponding calcifications on CT.

Conclusions: ¹⁸F-NaF PET/CT appears to be a sensitive method of detecting and characterizing calcinosis that provides both quantitative and qualitative data beyond what can be obtained by physical examination or CT alone. ¹⁸F-NaF uptake occurs in muscle in both SSc and DM, suggesting the possibility that myositis may be driving calcinosis in a subset of patients with SSc.

[This corrects the article DOI: 10.3389/fnume.2024.1469487.].

Introduction: Fluorodeoxyglucose (FDG) PET/CT is typically the reference imaging method for assessing and tracking lymphomas. However, fibroblast activation protein inhibitor (FAPI) PET is being explored as a potentially useful option, especially when Fluorodeoxyglucose (FDG) scans do not show clear results.

Methods: For this systematic review, two researchers searched PubMed/MEDLINE and Cochrane CENTRAL for studies on FAPI PET/CT in lymphoma patients.

Results: The literature search initially retrieved 249 articles. After removing duplicates and screening titles and abstracts, and full text, there was a final selection of 15 articles (3 original studies and 12 case reports), encompassing a total of 270 patients. The three original studies were judged to have a low risk of bias according to the QUADAS-2 criteria. The systematic review reveals that FAPI PET/CT exhibits lower diagnostic sensitivity than [¹⁸F]FDG PET/CT in lymphomas characterized by low FAP expression. Nevertheless, FAPI PET/CT retains potential as a complementary imaging modality.

Discussion: [¹⁸F]FDG PET/CT remains the gold standard in lymphoma imaging, but FAPI PET/CT can potentially provide supplementary information regarding the molecular characteristics of lymphomas. FAPI PET/CT may have prognostic and therapeutic implications. In particular, it could help identify lymphoma subgroups with distinct stromal environments, potentially serving as a prognostic biomarker. Further large-scale prospective studies are warranted to validate its role in lymphoma management.