Purpose: To compare the diagnostic performance of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) and ¹⁸F-fluoroestradiol (¹⁸F-FES) positron emission tomography/computed tomography (PET/CT) for initial staging of estrogen receptor (ER) positive breast cancer. Methods: Twenty-eight patients with ER-positive breast cancer underwent ¹⁸F-FDG and ¹⁸F-FES PET/CT for initial staging. Diagnostic performance and concordance rates were analyzed for both radiotracers. Semiquantitative parameters of maximum standardized uptake value (SUVmax) and tumor-to-normal ratio (T/N ratio) were compared using Wilcoxon signed-rank test. Factors potentially affecting the degree of radiotracer uptake were analyzed by multi-level linear regression analysis. Results: The overall diagnostic performance of ¹⁸F-FES was comparable to ¹⁸F-FDG, except for higher specificity and NPV, with sensitivity, specificity, PPV, NPV, and accuracy of 87.56%, 100%, 100%, 35.14%, and 88.35%, respectively, for ¹⁸F-FES and 83.94%, 30.77%, 94.74%, 11.43%, and 95.37%, respectively, for ¹⁸F-FDG. Diagnostic performance of strong ER expression was better in ¹⁸F-FES but worse for ¹⁸F-FDG. There was a correlation of mucinous cell type and Allred score 7–8 with ¹⁸F-FES uptake, with correlation coefficients of 26.65 (19.28, 34.02), 5.90 (− 0.005, 11.81), and p-value of < 0.001, 0.05, respectively. Meanwhile, luminal B and Ki-67 were related to ¹⁸F-FDG uptake, with correlation coefficients of 2.76 (1.10, 0.20), 0.11 (0.01, 0.2), and p-value of 0.018, 0.025, respectively. Conclusion: Diagnostic performance of ¹⁸F-FES is comparable to ¹⁸F-FDG, but better for strongly ER-positive breast cancer. Combination of ¹⁸F-FES and ¹⁸F-FDG would potentially overcome the limitations of each tracer with more accurate staging.

Purpose: This study aimed to investigate functional abnormalities in the brain of patients with neurological adverse effects following COVID-19 (coronavirus disease 2019) vaccination using ¹⁸F-FDG PET/MRI and ¹⁵O-water PET. Methods: Eight patients (1 man and 7 women, aged 26–47 years [median age, 36.5 years]) who experienced neurological symptoms after the first COVID-19 vaccination underwent CT, MRI, ¹⁸F-FDG PET/MRI, and ¹⁵O-water PET of the brain. After 7 days, each patient underwent a follow-up ¹⁸F-FDG PET/MRI and ¹⁵O-water PET of the brain. Imaging data were analyzed using visual and semiquantitative analyses, which included a cluster subtraction workflow (P = 0.05). Results: There was no evidence of vascular abnormalities, acute infarction, or hemorrhage on the CT or MRI scans. On the ¹⁵O-water PET images, 1 patient had mildly significant decreases in perfusion in the bilateral thalamus and bilateral cerebellum, and another patient showed a diffuse increase in perfusion in the cerebral white matter. The visual and semiquantitative analyses showed hypometabolism in the bilateral parietal lobes in all 8 patients on both the first and follow-up ¹⁸F-FDG PET/MRI scans. Metabolic changes in the bilateral cuneus were also observed during the first visit; all patients exhibited neurological symptoms. Moreover, 6 patients showed hypometabolism, and 2 patients showed hypermetabolism. Conclusion: All regions of metabolic abnormality were part of the fear network model that has been implicated in anxiety. Our study findings support the concepts of and provide evidence for the immunization stress-related response and the biopsychosocial model.

Aims: AI-QCTischaemia is an FDA-cleared novel artificial intelligence–guided method that utilizes features from coronary computed tomography angiography (CCTA) to predict myocardial ischaemia. Objective: To identify factors associated with discrepancy between AI-QCTischaemia and positron emission tomography (PET) perfusion. Methods and results: Six hundred and sixty-two patients with suspected obstructive coronary artery disease (CAD) on CCTA and undergoing [15O]H2O PET were analysed using AI-QCTischaemia. Multivariable logistic regression identified factors associated with discrepancy. Perfusion homogeneity was measured by relative flow reserve. A total of 209 (32%) patients showed discrepancies: 62 (9%) exhibited normal AI-QCTischaemia but abnormal perfusion (false negative AI-QCTischaemia), whereas 147 (22%) had abnormal AI-QCTischaemia despite normal perfusion (false positive AI-QCTischaemia). False positive AI-QCTischaemia patients (vs. true positive) were more often females, older, with less typical angina, and less advanced CAD. In multivariable analysis, typical angina [OR 95% CI: 1.796 (1.015–3.179), P = 0.044], diameter stenosis per 1% increase [1.058 (1.036–1.080), P < 0.001], and percent atheroma volume per 1% increase [1.103 (1.051–1.158), P < 0.001] significantly predicted true positive, while age was inversely associated [0.955 (0.923–0.989), P = 0.010]. False-negative AI-QCTischaemia patients (vs. true negative) were more often males, smokers, with less good CCTA image quality, and more advanced CAD. However, none was significant in multivariable analysis. Furthermore, false-negative AI-QCTischaemia showed more homogenously reduced perfusion by relative flow reserve compared to true positive (median ± IQR: 0.68 ± 0.15 vs. 0.56 ± 0.23, P < 0.001) and 21 (34%) of false negative showed globally reduced perfusion. Conclusion: For abnormal AI-QCTischaemia, younger age, typical angina, more severe stenosis, and more extensive atherosclerosis predicted abnormal PET perfusion. With false negative AI-QCTischaemia, perfusion abnormalities were partly explained by microvascular disease.

PurposeThe aim of this study was to create and validate a normal brain template of F18-fluorodeoxyglucose (F18-FDG) uptake using the MIMneuro software to improve clinical practice.ApproachOne hundred and nine volunteers underwent an F18-FDG positron emission tomography/computed tomography scan. Sixty-three participants with normal Alzheimer’s disease (AD) biomarkers were used to create a template. A group of 23 participants with abnormal AD biomarkers and an additional group of 23 participants with normal AD biomarkers were used to validate the performance of the generated template. The MIMneuro software was used for the analysis and template creation. The performance of our newly created template was compared with that of the MIMneuro software template in the validation groups. Results were confirmed by visual analysis by nuclear medicine physicians.ResultsOur created template provided higher sensitivity, specificity, positive predictive value, and negative predictive value (NPV; 90%, 97.83%, 100%, and 100%, respectively) than did the MIMneuro template when using the positive validation group. Similarly, slightly higher performance was observed for our template than for the MIMneuro template in the negative validation group (the highest specificity and NPV were 100% and 100%, respectively).ConclusionsOur normal brain template for F18-FDG was shown to be clinically useful because it enabled more accurate discrimination between aging brain and patients with AD. Thus, the template may improve the accuracy of AD diagnoses.