PET Analysis Research Articles

Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics

Urban public space comfort is essential for improving quality of life, particularly as climate change affects outdoor thermal environments. This study utilizes ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors such as age and gender. The findings indicate significant disparities in thermal stress vulnerability among demographic groups. On the hottest day of July 2023, at 10 a.m., children’s PET values were approximately 2 °C higher than those of other groups. By 3 p.m., females experienced slightly higher upper-range thermal stress than males. Elderly individuals aged 80 exhibited a broad range of PET values, from 38.14 °C to 62.39 °C, with prevailing values above 56.9 °C, indicating greater vulnerability to extreme heat. Children aged 8 showed PET values ranging from 40.20 °C to 59.34 °C, with prevailing estimates between 54.2 °C and 55.7 °C. Minimum PET values for children were significantly higher than those for adults, suggesting a greater baseline level of thermal stress. Despite cooling effects in the evening, children remained exposed to more pronounced stress than elderly individuals, males, and females. The UTCI values recorded indicate a period of extreme heat stress for all demographic groups assessed. While individuals aged 35 may encounter considerable discomfort, the severity of the impact is notably more pronounced for both older adults and children. This study underscores the need for tailored management strategies and advocates for expanding ENVI-met’s capabilities to enhance urban resilience and well-being amid rising temperatures.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18 F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence-driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant. In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies.

Combination of FDG PET/CT radiomics and clinical parameters for outcome prediction in patients with non-Hodgkin's lymphoma.

The purposes was to build model incorporating PET + computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas. Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical. Male (P = 0.030), abnormal lymphocytes (P = 0.030), lower value of PET entropy (P = 0.030), higher value of SHAPE sphericity (P = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P = 0.013), abnormal lymphocytes (P = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean (P = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment (P < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean (P < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte (P = 0.033), lower value of CT NGLDM coarseness (P = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean (P = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, respectively.

Perspectives on obesity imaging: [18F]2FNQ1P a specific 5-HT6 brain PET radiotracer.

Estimates suggest that approximatively 25% of the world population will be overweight in 2025. Better understanding of the pathophysiology of obesity will help to develop future therapeutics. Serotonin subtype 6 receptors (5-HT6) have been shown to be critically involved in appetite reduction and weight loss. However, it is not known if the pathological cascade triggered by obesity modifies the density of 5-HT6 receptors in the brain. Influence of diet-induced obesity (DIO) in Wistar rats was explored using MRI (whole-body fat) and PET ([18F]2FNQ1P as a specific 5-HT6 radiotracer). The primary goal was to monitor the 5-HT6 receptor density before and after a 10-week diet (DIO group). The secondary goal was to compare 5-HT6 receptor densities between DIO group, Wistar control diet group, Zucker rats (with genetic obesity) and Zucker lean strain rats. Wistar rats fed with high-fat diet showed higher body fat gain than Wistar control diet rats on MRI. [18F]2FNQ1P PET analysis highlighted significant clusters of voxels (located in hippocampus, striatum, cingulate, temporal cortex and brainstem) with increased binding after high-fat diet (p < 0.05, FWE corrected). This study sheds a new light on the influence of high-fat diet on 5-HT6 receptors. This study also positions [18F]2FNQ1P PET as an innovative tool to explore neuronal consequences of obesity or eating disorder pathophysiology.

PET/MRI in paediatric inflammatory bowel disease – a prospective accuracy study

AbstractCross‐sectional imaging supplements endoscopy in detecting disease manifestations in inflammatory bowel diseases (IBD). This study aimed to evaluate the accuracy of PET/MRI in a paediatric population suspected of IBD. This prospective study consecutively included patients aged 8–17 years under diagnostic evaluation for IBD. Forty‐three patients underwent a PET/MRI scan and subsequent ileocolonoscopy, of whom 26 patients diagnosed with IBD participated in a follow‐up scan, hereof 19 with Crohn's disease (CD), five with Ulcerative colitis and two with unclassified IBD. The results of PET alone, MRI alone, and PET/MRI combined were compared to a reference standard of endoscopy and histopathology. Of the 208 intestinal segments analysed, 109 showed inflammation, and 99 had no inflammation. In the per‐segment analysis PET had a sensitivity of 0.83 (95% CI 0.73–0.93), specificity of 0.59 (95% CI 0.47–0.71), and area under the receiver operating characteristic curve (AUROC) of 0.73 (95% CI 0.67–0.80). MRI had a sensitivity of 0.52 (95% CI 0.41–0.64), specificity 0.89 (95% CI 0.82–0.96), and AUROC of 0.72 (95% CI 0.66–0.77). PET/MRI had a sensitivity of 0.83 (95% CI 0.74–0.94), specificity of 0.57 (95% CI 0.44–0.69), and AUROC of 0.77 (95% CI 0.71–0.84). At follow‐up, PET and MRI scores decreased, and the change in MRI was able to identify patients with a clinical response. The accuracy of the PET/MRI scan in detecting inflammation in the terminal ileum and colon was moderate and not superior to either modality alone. With technological advances and combined reading, PET/MRI may still be valuable in selected cases.

Deformation-based morphometry applied to FDG PET data reveals hippocampal atrophy in Alzheimer’s disease

Cerebral atrophy is a key finding in patients with dementia and usually determined on MRI. We tested whether cerebral atrophy can be imaged with FDG PET by applying deformation-based morphometry (DBM). We retrospectively identified 26 patients with a biomarker-supported clinical diagnosis of Alzheimer’s disease (AD) who had received FDG PET on a fully-digital PET/CT system and structural MRI and compared them to 13 healthy elderly controls (HEC). We performed DBM with FDG PET data (FDG-DBM). As a reference standard for determining atrophy we used voxel-based morphometry of MRI data (MRI-VBM). For conventional analysis of hypometabolism, scaled FDG PET scans (reference: brain parenchyma) were compared between groups. Receiver operating characteristic (ROC) analyses were performed. ROI read-outs were tested for associations with cognitive test performance. FDG-DBM showed abnormalities in AD mainly in the bilateral hippocampi. Similarly, MRI-VBM showed hippocampal atrophy. By contrast, conventional FDG PET analysis revealed reduced bilateral temporo-parietal FDG uptake (all p < 0.05, FWE-corrected). FDG-DBM measures of the hippocampus significantly separated AD from HEC with an AUC of 0.81; MRI-VBM achieved an AUC of 0.87; the difference between the two ROC curves was not significant (p = 0.40). Whereas FDG uptake of the hippocampus did not separate AD from HEC, FDG uptake of the Landau Meta-ROI achieved an AUC of 0.88. Verbal memory was significantly associated with FDG-DBM measures of the hippocampus (p = 0.009), but not of the Landau Meta-ROI (p > 0.1). The opposite held true for conventional FDG uptake (p > 0.1 and p = 0.001, respectively). Hippocampal atrophy in AD can be detected by applying DBM to clinical, fully-digital FDG PET. It correlates with cognitive performance and might constitute a biomarker of neurodegeneration that is complementary to conventional FDG PET analysis of regional hypometabolism.

Enhanced detection of damaged myocardium and risk stratification in hypertrophic cardiomyopathy using integrated [68Ga]Ga-FAPI-04 PET/CMR imaging.

This study aims to explore the correlation between PET and CMR in integrated [68Ga]Ga-FAPI-04 PET/CMR multimodal imaging and its value in the diagnosis and risk assessment of hypertrophic cardiomyopathy (HCM). This study included 20 HCM patients and 11 age- and gender-matched controls. PET analysis evaluated left ventricular (LV) [68Ga]Ga-FAPI-04 uptake, including SUVmax, TBR, cardiac fibroblast activity (CFA) and volume (CFV), and total SUV of the 16 segments. CMR tissue characterization parameters included cardiac function, myocardial thickness, late gadolinium enhancement (LGE), relaxation time, extracellular volume (ECV), and peak strain parameters. The 5-year sudden cardiac death (SCD) risk score and the 2-year and 5-year atrial fibrillation (AF) risk scores were calculated for each patient. The study analyzed differences between HCM patients and controls, the correlation between [68Ga]Ga-FAPI-04 PET and concurrent CMR imaging results, and the predictive value of PET/CMR. The FAPI uptake, myocardial mass, myocardial thickness, and T1/T2 mapping values were significantly higher in HCM patients compared to controls. Twenty HCM patients and their 320 myocardial segments were discussed. Increased [68Ga]Ga-FAPI-04 uptake in the left ventricular wall was observed in 95% (19/20) of the patients, covering 48.8% (156/320) of the segments. On concurrent CMR, 80% (16/20) of the patients showed LGE, including 95 (29.7%) segments. The FAPI(+)LGE(+) segments exhibited the highest myocardial PET uptake, greatest thickness, longest T1/T2 native values, largest ECV value and the greatest loss of myocardial strain capacity (P < 0.05). There was a significant correlation between FAPI uptake and CMR parameters (P < 0.05). Higher [68Ga]Ga-FAPI-04 uptake showed a positive correlation with SCD and AF risk scores (P < 0.05). The number of LGE(+) segments, mapping parameters, and ECV values in CMR also had prognostic significance. Combining PET with CMR aided in further risk stratification of HCM. [68Ga]Ga-FAPI-04 PET/CMR multimodal imaging has potential value in the detection of damaged myocardial lesions and risk assessment of HCM patients. [68Ga]Ga-FAPI-04 PET can detect more affected myocardium compared to CMR, and segments with abnormalities in both PET and CMR show more severe myocardial damage.

Comparative Analysis of Nitrate-Augmented MPI and FDG PET for Myocardial Viability Assessment

Comparative Analysis of Nitrate-Augmented MPI and FDG PET for Myocardial Viability Assessment

Exploring Thermal Discomfort during Mediterranean Heatwaves through Softscape and Hardscape ENVI-Met Simulation Scenarios

The study examines the effectiveness of various design strategies in alleviating the impacts of heatwaves in the Mediterranean region, focusing on a densely populated post-refugee urban area in Greece. By analyzing five different design scenarios, the study aims to identify the most efficient approach to mitigate thermal stress outdoors. The five design scenarios include changes in albedo values and coatings and alterations in the number and type of trees. The methodology includes a literature review, field work and microclimate simulations with the use of ENVI-met 5.6.1. The study evaluates ENVI-met data through potential air temperature, PET and UTCI analysis. The experimental results indicate that the most effective strategy is associated with urban greening. In particular, increasing tree cover considerably reduces air temperature, PET and UTCI values by 4 to 10 degrees Celsius. This finding highlights the potential of urban greening to enhance thermal comfort and combat heatwave effects. The research findings may be useful to landscape architects and urban designers, in light of a more climate-responsive urban design in the Mediterranean region. Future research may also assess the combined impact of multiple mitigation strategies on a larger scale, informing evidence-based policies for heatwave resilience.

68Ga]Ga-labeled FAPI Conjugated with Gly-Pro Sequence for PET Imaging of Malignant Tumors.

To improve tumor uptake and prolong tumor retention, a novel fibroblast activation protein (FAP) ligand based on a quinoline-based FAP inhibitor (FAPI) conjugated with the Gly-Pro sequence and 1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid (DOTA) was radiolabeled with [68Ga]GaCl3 ([68Ga]Ga-DOTA-GPFAPI-04). Due to the tumor heterogeneity, this study aimed to further validate the preclinical value of [68Ga]Ga-DOTA-GPFAPI-04 PET imaging in tumor mice models with different FAP expression levels. [68Ga]Ga-DOTA-GPFAPI-04 was synthesized and its partition coefficient was measured. The stability of [68Ga]Ga-DOTA-GPFAPI-04 was tested in phosphate-buffered saline (PBS, pH 7.4) and fetal bovine serum (FBS). Small animal PET and semi-quantitative studies were conducted in Panc-1 and A549 xenograft tumor mice models compared with [68Ga]Ga-DOTA-FAPI-04. Immunofluorescent and immunohistochemical staining and western blot assay were performed to confirm FAP expression in xenograft tumors. [68Ga]Ga-DOTA-GPFAPI-04 exhibited a radiochemical purity of > 99% and high stability in PBS and FBS. [68Ga]Ga-DOTA-GPFAPI-04 had higher hydrophilic property than [68Ga]Ga-DOTA-FAPI-04 (-4.09 ± 0.05 vs -3.45 ± 0.05). Small animal PET and semi-quantitative analysis revealed Panc-1 xenograft tumor displayed higher tumor uptake of [68Ga]Ga-DOTA-GPFAPI-04 and tumor-to-background ratios compared to A549 xenograft tumor, consistent with the results of immunofluorescence, immunohistochemistry, and western blot. Moreover, [68Ga]Ga-DOTA-GPFAPI-04 demonstrated higher tumor accumulation and longer tumor retention than [68Ga]Ga-DOTA-FAPI-04 in both Panc-1 and A549 xenograft tumors. Furthermore, the FAP-binding specificity of [68Ga]Ga-DOTA-GPFAPI-04 was confirmed in vivo by co-injection of unlabeled GPFAPI-04. [68Ga]Ga-DOTA-GPFAPI-04 showed more favorable in vivo tumor imaging and longer tumor retention compared to [68Ga]Ga-DOTA-FAPI-04, which has high potential to be a promising PET probe for detecting FAP-positive tumors.

Diabetics and Insulin Resistant Patients, Especially Post COVID-19, Have an Increased Biologic Age From Analysis of Brain FDG PET

Diabetics and Insulin Resistant Patients, Especially Post COVID-19, Have an Increased Biologic Age From Analysis of Brain FDG PET

Early FDG PET imaging and circulating T-cell repertoire biomarkers of response to chemotherapy and PD-1 checkpoint inhibitors in patients with stage IV NSCLC.

8622 Background: Chemotherapy and immune checkpoint inhibitors (chemoICI) for stage IV NSCLC without driver mutations produce variable response patterns and outcomes. Early assessment could establish selective benefit from treatment (de)escalation, consolidation, or adaptation. We evaluated FDG PET imaging and circulating T cell repertoire biomarkers of chemoICI response for clinical decision support. Methods: 35 patients with stage IV NSCLC prospectively enrolled on PET BRIGHT (NCT04151940) from 2019-2023 and received first-line carboplatin-pemetrexed-pembrolizumab. Synchronous FDG PET/CT and peripheral blood draws were performed prior to and after first dose of chemoICI at week 3. Imaging biomarkers included standardized uptake value (SUV) and total lesion glycolysis (TLG) metrics within the primary metabolic tumor volume and across lung lesions. Immunophenotyping included T cell receptor (TCR) sequencing from which TCR diversity metrics were extracted. Spearman correlation of FDG PET and TCR biomarkers with bi-dimensional size reduction after initial chemoICI was estimated. ROC analysis of PET and TCR biomarkers to discriminate chemoICI response status was benchmarked against PD-L1 testing, along with Mann-Whitney testing of groupwise differences. Results: The majority tested negative for PD-L1 (58% TPS&lt;1%). Most patients (63%) had partial response (PR) to initial chemoICI versus 33% with stable disease (SD) and 4% with progressive disease (PD). FDG PET primary tumor peak SUV decreased by a median of 15% [-82%, 63%] at week 3, while TLG decreased by 36% [-55%, 83%]. Higher baseline peak SUV and TLG, along with greater decline after first dose, were correlated with post-chemoICI size reduction (R 0.43-0.64, p=0.001-0.036). PR patients had higher baseline peak SUV (12 vs 6 g/mL, p=0.014) and greater week 3 decline in TLG (39% vs 7%, p=0.032) relative to SD/PD patients. Across lung lesions, PR-patient maximum SUV decreased while SD/PD-patient maximum SUV increased at week 3 (20% vs -19%, p=0.010). FDG PET biomarkers were stronger discriminators of chemoICI response status (AUC 0.75-0.82, p=0.009-0.046) than PD-L1 (AUC 0.60, p=0.42). Higher baseline blood TCR Simpson clonality was positively correlated with size reduction (R 0.70, p=0.004), whereas TCR Pielou evenness was negatively correlated (R -0.68, p=0.006). Higher baseline TCR clonality / lower TCR evenness discriminated chemoICI response status (AUC 0.80-0.84, p=0.028-0.049). TCR diversity changes were uncorrelated with response. Conclusions: FDG PET imaging prior to and after first dose chemoICI, along with baseline circulating T cell repertoire biomarkers, predict future response. Further investigation of these patient- and lesion-level biomarkers to guide personalized (de)escalation, consolidation, or adaptation of regimens for stage IV NSCLC is warranted. Clinical trial information: NCT04151940 .

Cognition and Amyloid-β in Older Veterans: Characterization and Longitudinal Outcomes of Data-Derived Phenotypes.

Within older Veterans, multiple factors may contribute to cognitive difficulties. Beyond Alzheimer's disease (AD), psychiatric (e.g., PTSD) and health comorbidities (e.g., TBI) may also impact cognition. This study aimed to derive subgroups based on objective cognition, subjective cognitive decline (SCD), and amyloid burden, and then compare subgroups on clinical characteristics, biomarkers, and longitudinal change in functioning and global cognition. Cluster analysis of neuropsychological measures, SCD, and amyloid PET was conducted on 228 predominately male Vietnam-Era Veterans from the Department of Defense-Alzheimer's Disease Neuroimaging Initiative. Cluster-derived subgroups were compared on baseline characteristics as well as 1-year changes in everyday functioning and global cognition. The cluster analysis identified 3 groups. Group 1 (n = 128) had average-to-above average cognition with low amyloid burden. Group 2 (n = 72) had the lowest memory and language, highest SCD, and average amyloid burden; they also had the most severe PTSD, pain, and worst sleep quality. Group 3 (n = 28) had the lowest attention/executive functioning, slightly low memory and language, elevated amyloid and the worst AD biomarkers, and the fastest rate of everyday functioning and cognitive decline. Psychiatric and health factors likely contributed to Group 2's low memory and language performance. Group 3 was most consistent with biological AD, yet attention/executive function was the lowest score. The complexity of older Veterans' co-morbid conditions may interact with AD pathology to show attention/executive dysfunction (rather than memory) as a prominent early symptom. These results could have important implications for the implementation of AD-modifying drugs in older Veterans.

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomic analysis was performed on the whole brain after normalization to an optimized template. After feature selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, a Neural Network model was tested to find the accuracy to classify HC and demented patients. Twenty subjects not included in the training were used to test the models. The results were compared with those obtained by conventional CNN model. Four radiomic features were selected. The validation and test accuracies were 100% for both models, but the probability scores were higher with radiomics, in particular for HC group ( P = 0.0004). Radiomic features extracted from standardized PET whole brain images seem to be more accurate than CNN to distinguish patients with and without dementia.

Total and anatomically contextualized quantitative 18F-DCFPyL PET at biochemical recurrence to predict subsequent biochemical progression-free survival in patients with prostate cancer.

33 Background: PSMA PET has been shown to detect more metastasis and alter management at biochemical recurrence (BCR), but it remains to be determined that it changes oncologic outcome. We assessed the quantitative parameters on 18F-DCFPyL PET at BCR and evaluated their association with tumor volume, metastatic locations with the subsequent biochemical progression free survival (bPFS). Methods: This is a retrospective image analysis and longitudinal follow up of a prospective study evaluating 18F-DCFPyL PET in BCR. Patients were treated with androgen deprivation therapy (ADT) and/or metastatic directed therapy (MDT) based on imaging findings. The 18F-DCFPyL PET images were quantitatively analyzed by the aPROMISE application, a semi-automated software for comprehensive analysis and structured reporting of PSMA PET/CT. aPROMISE uses deep learning to segment detailed anatomical information from the CT images and uses this information in combination with the PET image to detect and quantify candidates for prostate cancer lesions. The reader works in tandem with the software to vet the final list of lesions, from which the quantitative assessments and final report is created automatically. Based on the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria. SUV mean, PSMA positive total tumor volume (PSMAttv) and aPSMA scores, a quantitative score for tumor burden measuring the interaction of tumor volume and uptake stratified by local tumors (aPSMA-miT), regional lymph nodes (aPSMA-miN) and distant metastases (aPSMA-miMa for extrapelvic metastases, miMb for bone metastases and miMc for other organ metastases) were obtained based on the miTNM classification. The association of these quantitative parameters with the subsequent bPFS was evaluated. Results: 143 patients (age 70.5 ± 7.9, range 50 -93 years; median PSA 2.8, range 0.12 – 1125.9 ng/mL) were included in the quantitative image analysis. 80 of 143 patients (56%) had nodal metastases 51/143 patients (36%) had bone metastases, only 16 of 143 patients (11%) had visceral metastases. With median follow up of 40 months, 82 of 143 patients (57%) have progressed again biochemically after MDT. The median bPFS was 26.9 months. Quantitative analysis of 18F-DCFPyL PET found that the subsequent bPFS is significantly associated with aPSMA-miMb ( P = 0.003), aPSMA-miN ( P = 0.005), aPSMA-miT ( P &lt; 0.001), and PSMAttv ( P = 0.006), but not with SUVmean, SUVmax or aPSMA-miMc. Conclusions: Quantitative image analysis of 18F-DCFPyL PET at BCR may be useful in predicting the subsequent bPFS. Given many patients were treated with MDT and/or ADT, clinical implication of the findings remains to be elucidated to guide treatment intensification based on findings on 18F-DCFPyL PET.

Definition of a Threshold for the Plasma Aβ42/Aβ40 Ratio Measured by Single-Molecule Array to Predict the Amyloid Status of Individuals without Dementia.

Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) plaques and hyperphosphorylated tau in the brain. Aβ plaques precede cognitive impairments and can be detected through amyloid-positron emission tomography (PET) or in cerebrospinal fluid (CSF). Assessing the plasma Aβ42/Aβ40 ratio seems promising for non-invasive and cost-effective detection of brain Aβ accumulation. This approach involves some challenges, including the accuracy of blood-based biomarker measurements and the establishment of clear, standardized thresholds to categorize the risk of developing brain amyloid pathology. Plasma Aβ42/Aβ40 ratio was measured in 277 volunteers without dementia, 70 AD patients and 18 non-AD patients using single-molecule array. Patients (n = 88) and some volunteers (n = 66) were subject to evaluation of amyloid status by CSF Aβ quantification or PET analysis. Thresholds of plasma Aβ42/Aβ40 ratio were determined based on a Gaussian mixture model, a decision tree, and the Youden's index. The 0.0472 threshold, the one with the highest sensitivity, was retained for general population without dementia screening, and the 0.0450 threshold was retained for research and clinical trials recruitment, aiming to minimize the need for CSF or PET analyses to identify amyloid-positive individuals. These findings offer a promising step towards a cost-effective method for identifying individuals at risk of developing AD.

An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis.

Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19. [18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann-Whitney U-tests and logistic regression. Features were retained if the P-value was <0.00053, the AUC was >0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers. The Mann-Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax ) showed a high area under the curve (AUC) and accuracy with small P-values (<0.00053), but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, the Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier to individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively). Using radiomic features may prove useful in identifying individuals with CS. Some features showed promising results in differentiating between PC and CS. By automating the analysis, the patient management process can be accelerated and improved.

Comparison of tracer kinetic models for 68Ga-PSMA-11 PET in intermediate-risk primary prostate cancer patients

Background68Ga-PSMA-11 positron emission tomography enables the detection of primary, recurrent, and metastatic prostate cancer. Regional radiopharmaceutical uptake is generally evaluated in static images and quantified as standard uptake values (SUVs) for clinical decision-making. However, analysis of dynamic images characterizing both tracer uptake and pharmacokinetics may offer added insights into the underlying tissue pathophysiology. This study was undertaken to evaluate the suitability of various kinetic models for 68Ga-PSMA-11 PET analysis. Twenty-three lesions in 18 patients were included in a retrospective kinetic evaluation of 55-min dynamic 68Ga-PSMA-11 pre-prostatectomy PET scans from patients with biopsy-demonstrated intermediate- to high-risk prostate cancer. Three kinetic models—a reversible one-tissue compartment model, an irreversible two-tissue compartment model, and a reversible two-tissue compartment model, were evaluated for their goodness of fit to lesion and normal reference prostate time-activity curves. Kinetic parameters obtained through graphical analysis and tracer kinetic modeling techniques were compared for reference prostate tissue and lesion regions of interest.ResultsSupported by goodness of fit and information loss criteria, the irreversible two-tissue compartment model optimally fit the time-activity curves. Lesions exhibited significant differences in kinetic rate constants (K1, k2, k3, Ki) and semiquantitative measures (SUV and %ID/kg) when compared with reference prostatic tissue. The two-tissue irreversible tracer kinetic model was consistently appropriate across prostatic zones.ConclusionsAn irreversible tracer kinetic model is appropriate for dynamic analysis of 68Ga-PSMA-11 PET images. Kinetic parameters estimated by Patlak graphical analysis or full compartmental analysis can distinguish tumor from normal prostate tissue.

Analysis of PET post-consumer through Thermal treatments

This research work analyzes the process of determining post-consumer contaminant material for plastics and identification of acceptable material to be reused. Burn tests were carried out on recycled material (different plastic wastes) in order to recognize, by means of color change, the type of raw material that is contaminating and thus avoid its entry into the recycling process. The burning was carried out in a thermoreactor at a temperature of 230°C for 30 minutes, with methods and equipment used in a well-known company in the region. In this study, waste from some well-known brands used in homes in the state of Guanajuato was used, considering that the separation was supported by TOMRA equipment and manual classification by the company's personnel. For the burn tests, plastic trimmings were used, elaborated in a handmade way, therefore, the work of a mechanical shredder will not be taken into account.

A ROI-based quantitative pipeline for 18F-FDG PET metabolism and pCASL perfusion joint analysis: Validation of the 18F-FDG PET line

In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.