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Objectives To assess the frequency, intensity, and clinical impact of [ 18 F]FDG-avidity of axillary lymph nodes after vaccination with COVID-19 vaccines BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) in patients referred for oncological FDG PET/CT. Methods One hundred forty patients referred for FDG PET/CT during February and March 2021 after first or second vaccination with Pfizer-BioNTech or Moderna were retrospectively included. FDG-avidity of ipsilateral axillary lymph nodes was measured and compared. Assuming no knowledge of prior vaccination, metastatic risk was analyzed by two readers and the clinical impact was evaluated. Results FDG PET/CT showed FDG-avid lymph nodes ipsilateral to the vaccine injection in 75/140 (54%) patients with a mean SUV max of 5.1 (range 2.0 – 17.3). FDG-avid lymph nodes were more frequent in patients vaccinated with Moderna than Pfizer-BioNTech (36/50 [72%] vs. 39/90 [43%] cases, p < 0.001). Metastatic risk of unilateral FDG-avid axillary lymph nodes was rated unlikely in 52/140 (37%), potential in 15/140 (11%), and likely in 8/140 (6%) cases. Clinical management was affected in 17/140 (12%) cases. Conclusions FDG-avid axillary lymph nodes are common after COVID-19 vaccination. The avidity of lymph nodes is more frequent in Moderna compared to that in Pfizer-BioNTech vaccines. To avoid relatively frequent clinical dilemmas, we recommend carefully taking the history for prior vaccination in patients undergoing FDG PET/CT and administering the vaccine contralateral to primary cancer. Key Points • PET/CT showed FDG-avid axillary lymph nodes ipsilateral to the vaccine injection site in 54% of 140 oncological patients after COVID-19 vaccination. • FDG-avid lymphadenopathy was observed significantly more frequently in Moderna compared to patients receiving Pfizer-BioNTech-vaccines. • Patients should be screened for prior COVID-19 vaccination before undergoing PET/CT to enable individually tailored recommendations for clinical management.

Immune checkpoint inhibitor treatment has the potential to prolong survival in non-small cell lung cancer (NSCLC), however, some of the patients develop resistance following initial response. Here, we analyze the immune phenotype of matching tumor samples from a cohort of NSCLC patients showing good initial response to immune checkpoint inhibitors, followed by acquired resistance at later time points. By using imaging mass cytometry and whole exome and RNA sequencing, we detect two patterns of resistance¨: One group of patients is characterized by reduced numbers of tumor-infiltrating CD8 +  T cells and reduced expression of PD-L1 after development of resistance, whereas the other group shows high CD8 +  T cell infiltration and high expression of PD-L1 in addition to markedly elevated expression of other immune-inhibitory molecules. In two cases, we detect downregulation of type I and II IFN pathways following progression to resistance, which could lead to an impaired anti-tumor immune response. This study thus captures the development of immune checkpoint inhibitor resistance as it progresses and deepens our mechanistic understanding of immunotherapy response in NSCLC. Acquired resistance to immune checkpoint inhibitors limits therapeutic success in non-small-cell lung cancer, however, the underpinning immune parameters are largely unknown. Here authors distinguish resistance types based on immune cell infiltration, immune checkpoint molecule and cytokine expression level, using paired samples from patients in the sensitive and in the resistant disease phase.

ObjectivesTo evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction.MethodsFifty-seven patients with 92 18F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUVmax)–based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed.ResultsThe AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772–0.869), and 0.848 (CI 95%; 0.828–0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM.ConclusionsOur results suggest that machine learning algorithms may aid detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM.Key Points• The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed.• BSREM yields higher SUVmaxof small pulmonary nodules as compared to OSEM reconstruction.• The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

To evaluate the diagnostic accuracy and specific imaging characteristics of positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (PET/CT), contrast enhanced CT (CE-CT), and a combined imaging approach (CE-PET/CT) in patients with infectious/mycotic (MAA), inflammatory (IAA), and non-infected, non-inflammatory abdominal aortic aneurysm (AAA). In this single-center retrospective cohort study, all imaging data sets of 29 consecutive patients with clinically suspected MAA or IAA were anonymised with different, reshuffled identification numbers and retrospectively and independently analysed by two experienced readers, blinded to all clinical patient data. Readers determined the presence or absence and MAA, IAA and AAA and of predefined imaging characteristics (e.g. fluid collection), and measured metabolic activity and wall thickness of all aneurysms. A multidisciplinary team of specialists served as standard of reference and re-evaluated every clinical case, considering all clinical, laboratory, microbiological, histopathological and imaging results, including all follow-up examinations. Diagnostic accuracy was higher in PET/CT as compared to CE-CT in differentiating AAA from MAA and IAA: area under the receiver operating characteristic curve (AUC-ROC) 0.81 (95% confidence intervals 0.69-0.92) and 0.63 (0.52-0.74) (P = 0.027). Specific imaging characteristics were significantly associated with different types of aneurysms (P<0.05), i.e. very high metabolic activity and dorsal sparing of metabolic activity in PET/CT and wall thickening in CE-CT were indicative for IAA; fat stranding and fluid collections in CE-CT were associated with MAA; while low metabolic acitivity and absence of wall thickening in PET/CT, and less fat stranding and absence of wall thickening in CE-CT were indicative for non-infected, non-inflammatory AAA. Specific imaging characteristics of PET/CT and CE-CT may be helpful in differentiating between MAA, IAA, and non-infected, non-inflammatory AAA.

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated “sufficient image quality” and 117 images rated “insufficient image quality”. The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists.

Alveolar echinococcosis is a rare parasitic disease, most frequently affecting the liver, as a slow-growing tumor-like lesion. If inoperable, long-term benzimidazole therapy is required, which is associated with high healthcare costs and occasionally with increased morbidity. The aim of our study was to determine the role 18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in staging of patients with alveolar echinococcosis and to identify quantitative imaging parameters related to patient outcome and/or duration of benzimidazole therapy. In this single-center retrospective cohort study, 47 PET/CT performed for staging in patients with confirmed alveolar echinococcosis were analysed. In 43 patients (91%) benzimidazole therapy was initiated and was successfully stopped after a median of 870 days (766–2517) in 14/43 patients (33%). In inoperable patients, tests for trend of survivor functions displayed clear trends for longer benzimidazole therapy duration (p = 0.05; n = 25), and for longer time intervals to reach non-detectable serum concentration of Em-18 antibodies (p = 0.01, n = 15) across tertiles of SUVratio (maximum standardized uptake value in the echinococcus manifestation compared to normal liver tissue). Hence, in inoperable patients with alveolar echinococcosis, PET/CT performed for staging may predict the duration of benzimidazole therapy.

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[ 18 F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[ 18 F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[ 18 F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen’s k : 0.91, CI 0.86–0.95; p -value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[ 18 F]-FDG PET/CT and SARS-CoV-2 vaccination.

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[ 18 F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[ 18 F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUV max ). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUV max was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p  = 0.0001]. In a size-based analysis, the relative increase in SUV max was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM ( p  < 0.0001). BSREM reconstruction results in a significant increase in SUV max and a significantly improved conspicuity of small 2-[ 18 F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[ 18 F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

To evaluate whether quantitative PET parameters of motion-corrected 68 Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent 68 Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM 1000 ). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM 1000 reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM 1000 reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM 1000 /OSEM + DDG/BSREM 1000  + DDG, respectively. BSREM 1000  + DDG can accurately differentiate pNET from accessory spleen. Both BSREM 1000 and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool’s capability of correctly assigning Agatston scores ≥ 1000 ( p  = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.