Reactive axillary lymph nodes after COVID-19 mRNA vaccination: comparison of mRNA vs. attenuated whole-virus vaccines.

OBJECTIVE: To compare the incidence and natural course of reactive axillary lymph nodes (RAL) between mRNA and attenuated whole-virus vaccines using Deauville criteria. METHODS: In this multi-institutional PET-CT study comprising multiple vaccine types (Pfizer-BioNTech/Comirnaty, Moderna/Spikevax, Sinovac/CoronaVac and Janssen vaccines), we evaluated the incidence and natural course of RAL in a large cohort of oncological patients utilizing a standardized Deauville scaling system (n=522; 293 Female, Deauville 3-5 positive for RAL). Univariate and multivariate analyses were conducted to evaluate the predictive value of clinical parameters (absolute neutrophil count [ANC], platelets, age, sex, tumor type, and vaccine-to-PET interval) for PET positivity. RESULTS: Pfizer-BioNTech/Comirnaty and Moderna vaccines revealed similar RAL incidences for the first 20 days after the second dose of vaccine administration (44% for the first 10 days for both groups, 26% vs. 20% for 10-20 days, respectively for Moderna and Pfizer). However, Moderna recipients revealed significantly higher incidences of RAL after 20 days compared to Pfizer-BioNTech/Comirnaty, with nodal reactivity spanning up to the 9th week post-vaccination (15% vs. 4%, respectively P  < 0.001). No RAL was observed in patients who received either a single dose of J&J vaccine or two doses of CroronaVac. Younger patients showed increased likelihood of RAL, otherwise, clinical/demographic parameters were not predictive of RAL ( P  = 0.014 for age, P  > 0.05 for additional clinical/demographic parameters). CONCLUSION: RAL based on strict PET criteria was observed with mRNA but not with attenuated whole-virus vaccines, in line with higher immunogenicity and stronger protection offered by mRNA vaccines.

A "fishy" situation, rare pathogen and presentation in prosthetic valve infective endocarditis.

Lactococcus garviae (L. garviae) is a gram-positive coccus belonging to the Streptococcaceae family. While primarily a pathogen in fish farms causing hemorrhagic sepsis, it can act as a rare opportunistic pathogen in humans. A 2021 case report by Bravo et al. documented less than 30 cases of infective endocarditis caused by L. garviae worldwide at that time [1]. This case report describes the 27th documented case globally and 7th documented case in the USA of L. garviae causing infective endocarditis of a prosthetic valve [1]. L. garviae is found in unpasteurized dairy products, raw fish, and meat (pork, beef, and poultry), but the route of human transmission remains unclear [3]. It seems to have a predilection for individuals with prosthetic valves, immunocompromised states, prior gastrointestinal surgery, gastrointestinal disorders (colon polyps and diverticulosis), and the use of acid-reducing medications [1-3]. Infective endocarditis is the most common systemic disease caused by L. garviae [1-4]. This report details the case of a 75-year-old male, with multiple comorbidities and risk factors for L. garviae infection who was admitted for "symptomatic anemia". High clinical suspicion, coupled with an inadequate hemoglobin response to transfusion, a normal anemia workup, and blood cultures positive for L. garviae, promoted a transesophageal echocardiogram (TEE). However, the results were negative. Consequently, an 18F-fluorodeoxyglucose positron emission tomography/computed tomography scan (18FDG PET/CT) was performed. The scan revealed increased uptake in the aortic valve replacement consistent with prosthetic valve endocarditis in the setting of Lactococcus garviae bacteremia.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

The role of imaging and sentinel lymph node biopsy in patients with T3b-T4b melanoma with clinically negative disease.

Background: Previous studies demonstrate minimal utility of pre-operative imaging for low-risk melanoma; however, imaging may be more critical for patients with high-risk disease. Our study evaluates the impact of peri-operative cross-sectional imaging in patients with T3b-T4b melanoma. Methods: Patients with T3b-T4b melanoma who underwent wide local excision were identified from a single institution (1/1/2005 - 12/31/2020). Cross-sectional imaging was defined as body CT, PET and/or MRI in the perioperative period, with the following findings: in-transit or nodal disease, metastatic disease, incidental cancer, or other. Propensity scores were created for the odds of undergoing pre-operative imaging. Recurrence free survival was analyzed using the Kaplan-Meier method and log-rank test. Results: A total of 209 patients were identified with a median age of 65 (IQR 54-76), of which the majority were male (65.1%), with nodular melanoma (39.7%) and T4b disease (47.9%). Overall, 55.0% underwent pre-operative imaging. There were no differences in imaging findings between the pre- and post-operative cohorts. After propensity-score matching, there was no difference in recurrence free survival. Sentinel node biopsy was performed in 77.5% patients, with 47.5% resulting in a positive result. Conclusion: Pre-operative cross-sectional imaging does not impact the management of patients with high-risk melanoma. Careful consideration of imaging use is critical in the management of these patients and highlights the importance of sentinel node biopsy for stratification and decision making.

Inter-pass motion correction for whole-body dynamic PET and parametric imaging.

Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.

Radiotracers to Address Unmet Clinical Needs in Cardiovascular Imaging, Part 1: Technical Considerations and Perfusion and Neuronal Imaging.

The development of new radiotracers for PET and SPECT is central to addressing unmet diagnostic needs related to systemwide trends toward molecular characterization and personalized therapies in cardiovascular medicine. In the following 2-part review, we discuss select emerging radiotracers that may help address important unmet diagnostic needs in central areas of cardiovascular medicine, such as heart failure, arrhythmias, valvular disease, atherosclerosis, and thrombosis. Part 1 examines key technical considerations pertaining to cardiovascular radiotracer development and reviews emerging radiotracers for perfusion and neuronal imaging. Highlights of this work include discussions on the development of 18F-flurpiridaz, an emerging PET perfusion tracer, and the development of 18F-based radiotracers for cardiovascular neuronal imaging, such as 18F-flubrobenguane. Part 2 of this review covers emerging radiotracers for the imaging of inflammation, fibrosis, thrombosis, calcification, and cardiac amyloidosis.

Radiotracers to Address Unmet Clinical Needs in Cardiovascular Imaging, Part 2: Inflammation, Fibrosis, Thrombosis, Calcification, and Amyloidosis Imaging.

Cardiovascular imaging is evolving in response to systemwide trends toward molecular characterization and personalized therapies. The development of new radiotracers for PET and SPECT imaging is central to addressing the numerous unmet diagnostic needs that relate to these changes. In this 2-part review, we discuss select radiotracers that may help address key unmet clinical diagnostic needs in cardiovascular medicine. Part 1 examined key technical considerations pertaining to cardiovascular radiotracer development and reviewed emerging radiotracers for perfusion and neuronal imaging. Part 2 covers radiotracers for imaging cardiovascular inflammation, thrombosis, fibrosis, calcification, and amyloidosis. These radiotracers have the potential to address several unmet needs related to the risk stratification of atheroma, detection of thrombi, and the diagnosis, characterization, and risk stratification of cardiomyopathies. In the first section, we discuss radiotracers targeting various aspects of inflammatory responses in pathologies such as myocardial infarction, myocarditis, sarcoidosis, atherosclerosis, and vasculitis. In a subsequent section, we discuss radiotracers for the detection of systemic and device-related thrombi, such as those targeting fibrin (e.g., 64Cu-labeled fibrin-binding probe 8). We also cover emerging radiotracers for the imaging of cardiovascular fibrosis, such as those targeting fibroblast activation protein (e.g., 68Ga-fibroblast activation protein inhibitor). Lastly, we briefly review radiotracers for imaging of cardiovascular calcification (18F-NaF) and amyloidosis (e.g., 99mTc-pyrophosphate and 18F-florbetapir).

Ipsilateral Malignant Axillary Lymphadenopathy and Contralateral Reactive Lymph Nodes in a COVID-19 Vaccine Recipient With Breast Cancer.

Vaccine-related axillary nodal enlargement is a common benign condition that many mRNA vaccine receivers experience. However, a false attribution of axillary swelling to vaccination may result in delay in cancer care and potential disease progression, particularly in breast cancer patients presenting with ipsilateral axillary lymphadenopathy. We report the case of a 41-year-old pre-menopausal female who presented with suspicious axillary nodal enlargement and a right breast lump (triple-negative invasive ductal carcinoma) after recent administration of the second dose of Moderna mRNA coronavirus disease 2019 (COVID-19) vaccine. On imaging, bilateral axillary lymph nodes were detected. The ipsilateral right-sided node was proven to be metastatic, whereas contralateral nodes were related to a recent mRNA COVID-19 vaccination. Right-sided lymph node had intense uptake (maximum standardized uptake value [SUVmax] = 5), while the contralateral reactive nodes were mildly avid (SUVmax = 2.6). On magnetic resonance imaging, the right-sided node revealed asymmetric cortical thickening and marked cortical enhancement as opposed to normal-appearing left-sided nodes.

Virtual high-count PET image generation using a deep learning method.

PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. METHODS: The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images. RESULTS: The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference. CONCLUSIONS: A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients.

The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (p = 0.01). After denoising, the image noise level of extremely obese subjects was similar to that of lean subjects, in terms of liver NSTD (0.08±0.03 vs. 0.08±0.02, p = 0.74). In contrast, U-Net B over-smoothed the images of extremely obese patients, resulting in blurred fine structures. In a pilot reader study comparing extremely obese patients without and with U-Net A, the difference was not significant. In conclusion, the U-Net trained by datasets from lean subjects with matched count level can provide promising denoising performance for extremely obese subjects while maintaining image resolution, though further clinical evaluation is needed.

Inhibition of type 1 immunity with tofacitinib is associated with marked improvement in longstanding sarcoidosis.

Sarcoidosis is an idiopathic inflammatory disorder that is commonly treated with glucocorticoids. An imprecise understanding of the immunologic changes underlying sarcoidosis has limited therapeutic progress. Here in this open-label trial (NCT03910543), 10 patients with cutaneous sarcoidosis are treated with tofacitinib, a Janus kinase inhibitor. The primary outcome is the change in the cutaneous sarcoidosis activity and morphology instrument (CSAMI) activity score after 6 months of treatment. Secondary outcomes included change in internal organ involvement, molecular parameters, and safety. All patients experience improvement in their skin with 6 patients showing a complete response. Improvement in internal organ involvement is also observed. CD4+ T cell-derived IFN-γ is identified as a central cytokine mediator of macrophage activation in sarcoidosis. Additional type 1 cytokines produced by distinct cell types, including IL-6, IL-12, IL-15 and GM-CSF, also associate with pathogenesis. Suppression of the activity of these cytokines, especially IFN-γ, correlates with clinical improvement. Our results thus show that tofacitinib treatment is associated with improved sarcoidosis symptoms, and predominantly acts by inhibiting type 1 immunity.

Cocaine-induced pulmonary complications: A diagnosis of waiting and watching.

Pulmonary complications of cocaine among users are common. Manifestations include lung congestion, intra-alveolar edema, and diffuse alveolar hemorrhage (DAH). Direct cellular toxicity, eosinophilia, barotrauma, and vasoactive effects of cocaine are believed to induce DAH. We present a rare case of cocaine-associated focal alveolar hemorrhage mimicking malignancy on imaging. Initially contemplated biopsy was avoided based on rapid growth of concerning lung lesion, with subsequent near resolution on follow-up. This case illustrates the importance of epidemiologic and temporal multimodality correlation when evaluating indeterminate lung lesions.

Intracystic Papillary Neoplasm of Gallbladder Mimicking Metastatic Malignancy on PET/CT.

ABSTRACT: Intracystic papillary neoplasm of the gallbladder is a rare preinvasive neoplastic lesion with similar characteristics as intraductal papillary mucinous neoplasm and other papillary neoplasms of pancreaticobiliary system. We report a case of 48-year-old woman with a history of recurrent right flank chondrosarcoma and gallbladder lesion on MRI and PET/CT interpreted as indeterminate for metastatic disease. Subsequent cholecystectomy showed intracystic papillary neoplasm. With gallbladder lesions being rare on PET/CT, this case illustrates the importance of considering both primary and secondary tumors in the differential diagnosis.

Altered FDG Biodistribution in Subcutaneous White Fat on PET/CT Following l-Asparaginase Chemotherapy.

ABSTRACT: A 13-year-old boy with mediastinal T-cell lymphoblastic lymphoma demonstrated an altered biodistribution with diffuse activity in subcutaneous white adipose tissue and decreased visceral activity on interim posttreatment FDG PET/CT. This altered biodistribution was attributed to administration of the chemotherapeutic enzyme l-asparaginase 3 hours preceding the PET/CT, altering adipocytes amino acid and glucose metabolism. Treatment response assessment was adversely affected by the altered biodistribution, emphasizing the importance of maximizing the time between chemotherapy and PET/CT during successive oncologic treatment cycles. Because adipocytes protect leukemic cells in culture from l-asparaginase, we hypothesize that white adipose tissue-altered biodistribution may be related to l-asparaginase resistance.