A Single Center Survey of Patients With Congenital Neutropenia: Report From Northwestern Iran

Neutropenia is characterized by a decrease in circulating neutrophil counts and consequent infections.  The present study was performed so as to describe the clinical and laboratory findings of patients with congenital neutropenia in northwestern Iran. The patients' records of 31 patients with congenital neutropenia out of 280 neutropenic patients who had been referred to Tabriz Children's Hospital during a 3-year period (2011-2014), were reviewed. Thirty-one cases (17 female and 14 male), with a mean age of 5.3 ± 5.7 years, were diagnosed to suffer from congenital neutropenia. The disorders associated with congenital neutropenia were combined immunodeficiency (8 cases), severe congenital neutropenia (6 cases), common variable immunodeficiency (4 cases), severe combined immunodeficiency (2 cases) and metabolic syndrome (1 case). The median age of the onset of disease was 26.2 ± 60.8 months. The most common clinical manifestations during the course of illness were otitis media (13 cases), pneumonia (12 cases), recurrent aphthous stomatitis, lymphadenopathy and gingivitis (11 cases). Four neutropenic patients died because of recurrent infections. Neutropenia may occur in the context of the primary immunodeficiency disorders. Unusual, persistent or severe infections always pose a speculation to search for an underlying immunodeficiency syndrome and neutropenia, so as to avoid further life-threatening complications as a result of any delay in diagnosis

Cytomegalovirus Infection in a Patient With Leukocyte Adhesion Deficiency Type 1

Leukocyte adhesion deficiency type 1 (LAD-1) is a rare autosomal recessive immunodeficiency disorder, characterized by recurrent bacterial and fungal infections without pus formation. Herein, we report a case of LAD-1 that developed into gastrointestinal cytomegalovirus (CMV) disease and manifested with persistent abdominal pain and bloody diarrhea. Although the presence of concurrent gastrointestinal CMV infection with LAD-1 is a rare condition, this case highlights the need for further research to evaluate the complex mechanisms between LAD-1 and CMV occurrence

Observer study-based evaluation of TGAN architecture used to generate oncological PET images

The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack of labeled samples, as well as privacy concerns regarding data sharing. To address these issues, we previously developed (Bergen et al. 2022) a synthetic PET dataset for Head and Neck (H and N) cancer using the temporal generative adversarial network (TGAN) architecture and evaluated its performance segmenting lesions and identifying radiomics features in synthesized images. In this work, a two-alternative forced-choice (2AFC) observer study was performed to quantitatively evaluate the ability of human observers to distinguish between real and synthesized oncological PET images. In the study eight trained readers, including two board-certified nuclear medicine physicians, read 170 real/synthetic image pairs presented as 2D-transaxial using a dedicated web app. For each image pair, the observer was asked to identify the real image and input their confidence level with a 5-point Likert scale. P-values were computed using the binomial test and Wilcoxon signed-rank test. A heat map was used to compare the response accuracy distribution for the signed-rank test. Response accuracy for all observers ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers did not identify the real image with statistical significance, indicating that the synthetic dataset was reasonably representative of oncological PET images. Overall, this study adds validity to the realism of our simulated H&N cancer dataset, which may be implemented in the future to train AI algorithms while favoring patient confidentiality and privacy protection

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement.

PURPOSE Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets

Immunopathogenesis of Ankylosing Spondylitis: An Updated Review

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory arthritis of unknown etiology, which belongs to a group of conditions known as spondyloarthropathies that comprises psoriatic arthritis, reactive arthritis, and enteropathic arthritis. AS causes pathologic new-bone formation in the axial skeleton, and leads to chronic pain, axial fusion, deformity, disability and skeletal fracture. Several genetic and environmental factors are known to be associated with AS. Notwithstanding the fact that a multitude of genes, such as human leukocyte antigen B27 (HLA-B27), endoplasmic reticulum-associated aminopeptidase 1 (ERAP1), and interleukin-23 receptor (IL-23R) have been previously speculated to be associated with individuals’ susceptibility to AS, no consensus about their precise role in the etiopathogenesis of AS has been reached. In the present study, we summarize the current literature on the immunogenetics of AS and contemporize the research advancement that has been made over the past decade

PSA-Stratified Performance of [18F]DCFPyL PET/CT in Biochemically Recurrent Prostate Cancer Patients under Androgen Deprivation Therapy

Based on in vitro studies, it is known that androgen deprivation therapy (ADT) increases prostate-specific membrane antigen (PSMA) expression on prostate cancer (PCa) cells. However, ADT also has cytoreductive effects which can decrease lesion size. The present evaluation was conducted to further analyze the influence of ongoing ADT on [18F]DCFPyL positron emission tomography/computed tomography (PET/CT) performance in the setting of biochemically recurrent PCa. We retrospectively evaluated two groups of PCa patients, previously treated with radical intent, who had undergone [18F]DCFPyL PET/CT because of biochemical relapse with a minimum PSA level of 0.4 ng/mL. One group consisted of 95 patients under ADT at the time of the PET examination, and the other consisted of 445 patients not receiving ADT at the time of PET/CT. The uptake characteristics of the cardiac blood pool, liver, parotid glands, and five most active lesions were measured and compared between these two groups. The overall detection rate of [18F]DCFPyL PET/CT in patients under ADT at the time of imaging was significantly higher than patients not under ADT (91.6% vs. 80.4%, p-value = 0.007). However, the PSA-stratified differences in detection rates between patients with and without ADT did not reach statistical significance. Except for the maximal standardized uptake values corrected for lean body mass (SULmax) in the PSA range of 1 to &lt;2 ng/mL, the intensity and volume of [18F]DCFPyL accumulation were higher in patients with ADT compared to the patients without. Statistical significance was attained for the SULmax in PSA range of 0.5 to &lt;1 ng/mL (p-value = 0.0004) and metabolic tumor volume (MTV) in all PSA ranges (p-values of 0.0005 to 0.03). No significant difference was observed for radiotracer uptake in normal organs between the two groups with and without ADT. In this study population with biochemical recurrence of PCa and measurable PSA, ongoing ADT at the time of [18F]DCFPyL PET/CT imaging was associated with higher radiotracer uptake and overall lesion detection rate. This could be due in part to the more aggressive disease phenotype in patients with ongoing ADT

An Evolution of Reporting: Identifying the Missing Link

In recent years, radiologic imaging has undergone tremendous technological advances and is now a pillar of diagnostic and treatment algorithms in clinical medicine. The increased complexity and volume of medical imaging has led clinicians to become ever more reliant on radiologists to both identify and interpret patient studies. A radiologist&rsquo;s report provides key insights into a patient&rsquo;s immediate state of health, information that is vital when choosing the most appropriate next steps in management. As errors in imaging interpretation or miscommunication of results can greatly impair patient care, identifying common error sources is vital to minimizing their occurrence. Although mistakes in medical imaging are practically inevitable, changes to the delivery of imaging reporting and the addition of artificial intelligence algorithms to analyze clinicians&rsquo; communication skills can minimize the impact of these errors, keep up with the continuously evolving landscape of medical imaging, and ultimately close the communication gap

PDCD1 Single Nucleotide Polymorphisms in Iranian Patients With Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is a clinically heterogeneous cluster of complex diseases, in which both the genetic and environmental factors seem to play a role in the development of the disease. The current study aims to assess the association of programmed cell death 1 (PDCD1, also called PD-1) gene variants with JIA vulnerability in Iranian population. In this case-control association study, we investigated a group of 50 Iranian patients with JIA in comparison with 202 healthy controls and evaluated the frequency of alleles, genotypes, and haplotypes of PDCD1 single-nucleotide polymorphisms (SNPs), comprising PD-1.1 G/A, PD-1.3 G/A and PD-1.9 C/T, using PCR-RFLP method. Both the allelic and genotype frequencies of PD-1.1, PD-1.3 and PD-1.9 were similar in two groups of patients and controls. Moreover, no significant difference was observed between the two groups of patients and controls for GGC (PD-1.1 G, PD-1.3 G, PD-1.9 C), GAC (PD-1.1 G, PD-1.3 A, PD-1.9 C), and AGT (PD-1.1 A, PD-1.3 G, PD-1.9 T) haplotypes. Our results did not show any association between PDCD1 SNPs and the development of JIA in Iranian population

Differential privacy preserved federated transfer learning for multi-institutional ⁶⁸Ga-PET image artefact detection and disentanglement

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.Methods: Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients’ images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).Results: The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value &lt; 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.Conclusion: The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.</p

Differential privacy preserved federated transfer learning for multi-institutional ⁶⁸Ga-PET image artefact detection and disentanglement

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.Methods: Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients’ images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).Results: The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value &lt; 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.Conclusion: The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.</p