Elastic Antiproton-Nucleus Scattering from Chiral Forces

Elastic scattering of antiprotons off $^4$He, $^{12}$C, and $^{16,18}$O is described for the first time with a totally microscopic approach based on the calculation of an optical potential (OP) describing the antiproton-target interaction. The OP is derived using the recent antiproton-nucleon ($\bar{p}N$) chiral interaction to calculate the $\bar{p}N$ $t$ matrix, while the target densities are computed with the ab initio no-core shell model using chiral interactions as well. Our results are in a good agreement with the existing experimental data and the results computed at different chiral orders of the $\bar{p}N$ interaction display the convergence pattern expected from the theory

Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

BACKGROUND To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI

Steroid hormone bioavailability is controlled by the lymphatic system.

The steroid hormone progesterone accounts for immune tolerance in pregnancy. Enhanced progesterone metabolism to 6α-OH-pregnanolone occurs in complicated pregnancies such as in preeclampsia with preterm delivery or intrauterine growth restriction, and in cancer. As lymphatic endothelial cells (LECs) promote tumor immunity, we hypothesized that human LECs modify progesterone bioavailability. Primary human LECs and mice lymph nodes were incubated with progesterone and progesterone metabolism was analyzed by thin layer chromatography and liquid chromatography-mass spectrometry. Expression of steroidogenic enzymes, down-stream signal and steroid hormone receptors was assessed by Real-time PCR. The placental cell line HTR-8/SV neo was used as reference. The impact of the progesterone metabolites of interest was investigated on the immune system by fluorescence-activated cell sorting analysis. LECs metabolize progesterone to 6α-OH-pregnanolone and reactivate progesterone from a precursor. LECs highly express 17β-hydroxysteroid dehydrogenase 2 and are therefore antiandrogenic and antiestrogenic. LECs express several steroid hormone receptors and PIBF1. Progesterone and its metabolites reduced TNF-α and IFN-γ production in CD4+ and CD8+ T cells. LECs modify progesterone bioavailability and are a target of steroid hormones. Given the global area represented by LECs, they might have a critical immunomodulatory control in pregnancy and cancer

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

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

Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

Objective: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). Methods: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. Results: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. Conclusions: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection

Low-dose CT from myocardial perfusion SPECT/CT allows the detection of anemia in preoperative patients

BACKGROUND To assess whether low-dose CT for attenuation correction of myocardial perfusion single-photon emission computed tomography (SPECT) allows for identification of anemic patients and grading anemia severity. METHODS AND RESULTS Patients who underwent a preoperative blood-test and low-dose CT scan, as a part of a cardiac SPECT exam, between 01 January 2015 and 31 December 2017 were enrolled in this retrospective study. Hemoglobin (Hb) levels and hematocrit were derived from clinical records. CT images were visually assessed (qualitative analysis) for the detection of inter-ventricular septum sign (IVSS) and aortic rim sign (ARS) and quantitative analysis were performed. The diagnostic accuracy for detecting anemia was compared using Hb values as the standard of reference. A total of 229 patients were included (110 with anemia; 57 mild; 46 moderate; 7 severe). The AUC of IVSS and ARS were 0.830 and 0.669, respectively (p<0.0001). The quantitative analysis outperformed ARS and IVSS; (AUC of 0.893, p=0.29). The optimal anemia cut-off using Youden index was 4.5 HU. CONCLUSION Quantitative analysis derived from low-dose CT images, as a part of cardiac SPECT exams, have a diagnostic accuracy similar to that of hematocrit for the detection of anemia and may allow discriminating different anemia severities

Frequency and intensity of [18F]-PSMA-1007 uptake after COVID-19 vaccination in clinical PET

Objectives: To assess the frequency and intensity of [18F]-prostate-specific membrane antigen (PSMA)-1007 axillary uptake in lymph nodes ipsilateral to COVID-19 vaccination with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) in patients with prostate cancer referred for oncological [18F]-PSMA positron emission tomography (PET)/CT or PET/MR imaging. Methods: 126 patients undergoing [18F]-PSMA PET/CT or PET/MR imaging were retrospectively included. [18F]-PSMA activity (maximum standardized uptake value) of ipsilateral axillary lymph nodes was measured and compared with the non-vaccinated contralateral side and with a non-vaccinated negative control group. [18F]-PSMA active lymph node metastases were measured to serve as quantitative reference. Results: There was a significant difference in maximum standardized uptake value in ipsilateral and compared to contralateral axillary lymph nodes in the vaccination group (n = 63, p < 0.001) and no such difference in the non-vaccinated control group (n = 63, p = 0.379). Vaccinated patients showed mildly increased axillary lymph node [18F]-PSMA uptake as compared to non-vaccinated patients (p = 0.03). [18F]-PSMA activity of of lymph node metastases was significantly higher (p < 0.001) compared to axillary lymph nodes of vaccinated patients. Conclusion: Our data suggest mildly increased [18F]-PSMA uptake after COVID-19 vaccination in ipsilateral axillary lymph nodes. However, given the significantly higher [18F]-PSMA uptake of prostatic lymph node metastases compared to "reactive" nodes after COVID-19 vaccination, no therapeutic and diagnostic dilemma is to be expected. Advances in knowledge: No specific preparations or precautions (e.g. adaption of vaccination scheduling) need to be undertaken in patients undergoing [18F]-PSMA PET imaging after COVID-19 vaccination

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma

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