COVID-19 Computed tomography patterns in renal replacement therapy patients / Padrões de tomografia computadorizada da COVID-19 em pacientes em terapia renal substitutiva

ABSTRACT Introduction: Lung diseases are common in patients with end stage kidney disease (ESKD), making differential diagnosis with COVID-19 a challenge. This study describes pulmonary chest tomography (CT) findings in hospitalized ESKD patients on renal replacement therapy (RRT) with clinical suspicion of COVID-19. Methods: ESKD individuals referred to emergency department older than 18 years with clinical suspicion of COVID-19 were recruited. Epidemiological baseline clinical information was extracted from electronic health records. Pulmonary CT was classified as typical, indeterminate, atypical or negative. We then compared the CT findings of positive and negative COVID-19 patients. Results: We recruited 109 patients (62.3% COVID-19-positive) between March and December 2020, mean age 60 ± 12.5 years, 43% female. The most common etiology of ESKD was diabetes. Median time on dialysis was 36 months, interquartile range = 12-84. The most common pulmonary lesion on CT was ground glass opacities. Typical CT pattern was more common in COVID-19 patients (40 (61%) vs 0 (0%) in non-COVID-19 patients, p < 0.001). Sensitivity was 60.61% (40/66) and specificity was 100% (40/40). Positive predictive value and negative predictive value were 100% and 62.3%, respectively. Atypical CT pattern was more frequent in COVID-19-negative patients (9 (14%) vs 24 (56%) in COVID-19-positive, p < 0.001), while the indeterminate pattern was similar in both groups (13 (20%) vs 6 (14%), p = 0.606), and negative pattern was more common in COVID-19-negative patients (4 (6%) vs 12 (28%), p = 0.002). Conclusions: In hospitalized ESKD patients on RRT, atypical chest CT pattern cannot adequately rule out the diagnosis of COVID-19.

RESUMO Introdução: Doenças pulmonares são comuns em pacientes com doença renal em estágio terminal (DRET), dificultando o diagnóstico diferencial com COVID-19. Este estudo descreve achados de tomografia computadorizada de tórax (TC) em pacientes com DRET em terapia renal substitutiva (TRS) hospitalizados com suspeita de COVID-19. Métodos: Indivíduos maiores de 18 anos com DRET, encaminhados ao pronto-socorro com suspeita de COVID-19 foram incluídos. Dados clínicos e epidemiológicos foram extraídos de registros eletrônicos de saúde. A TC foi classificada como típica, indeterminada, atípica, negativa. Comparamos achados tomográficos de pacientes com COVID-19 positivos e negativos. Resultados: Recrutamos 109 pacientes (62,3% COVID-19-positivos) entre março e dezembro de 2020, idade média de 60 ± 12,5 anos, 43% mulheres. A etiologia mais comum da DRET foi diabetes. Tempo médio em diálise foi 36 meses, intervalo interquartil = 12-84. A lesão pulmonar mais comum foi opacidades em vidro fosco. O padrão típico de TC foi mais comum em pacientes com COVID-19 (40 (61%) vs. 0 (0%) em pacientes sem COVID-19, p < 0,001). Sensibilidade 60,61% (40/66), especificidade 100% (40/40). Valores preditivos positivos e negativos foram 100% e 62,3%, respectivamente. Padrão atípico de TC foi mais frequente em pacientes COVID-19-negativos (9 (14%) vs. 24 (56%) em COVID-19-positivos, p < 0,001), enquanto padrão indeterminado foi semelhante em ambos os grupos (13 (20%) vs. 6 (14%), p = 0,606), e padrão negativo foi mais comum em pacientes COVID-19-negativos (4 (6%) vs. 12 (28%), p = 0,002). Conclusões: Em pacientes com DRET em TRS hospitalizados, um padrão atípico de TC de tórax não pode excluir adequadamente o diagnóstico de COVID-19.

Epithelial and stromal thickness profile and lens decentration in myopic orthokeratology / Perfil de espesor epitelial y estromal y descentrado del cristalino en ortoqueratología miope

Purpose: To study topographic epithelial and total corneal thickness changes in myopic subjects undergoing successful orthokeratology treatment in connection with the objective assessment of contact lens decentration. Methods: A prospective-observational and non-randomized study in 32 Caucasian myopic eyes undergoing Ortho-k for 3 months. Total, epithelial, and stromal thicknesses were studied before and after Ortho-k treatment, using optical coherence tomography with anterior segment application software. Central, paracentral, and mid-peripheral values are taken along 8 semi-meridians. Results: The central average total corneal thickness was 4.72 ± 1.04 μm thinner after Ortho-K. The paracentral corneal thickness showed no significant changes (p = 0.137), while the mid-peripheral corneal thickness was increased by 3.25 ± 1.6 μm associating this increase exclusively to the epithelial plot (p<0.001). When lens centration was assessed, a lens fitting decentration less than 1.0 mm was found for the whole sample, predominantly horizontal-temporal (87.5%) and vertical-inferior (50%) decentring. Corneal topographical analysis revealed a horizontal and vertical epithelial thickness asymmetric change profile with paracentral temporal thinnest values, and mid-peripheral nasal thickest values. Conclusions: The present study found a central corneal thinning induced by Ortho-k lenses in subjects with moderate myopia, only associated with a change in epithelial thickness, as well as mid-peripheral thickening, that seems to be mainly epithelial in origin. The authors also found a tendency of contact lens decentration toward temporal and inferior areas conditioning an asymmetric epithelial redistribution pattern.(AU)

Incidence of contrast-associated acute kidney injury: a prospective cohort / Incidência de lesão renal aguda associada ao contraste: uma coorte prospectiva

ABSTRACT Introduction: Contrast-associated acute kidney injury (CA-AKI) is a deterioration of kidney function that occurs after the administration of a iodinated contrast medium (ICM). Most studies that defined this phenomenon used older ICMs that were more prone of causing CA-AKI. In the past decade, several articles questioned the true incidence of CA-AKI. However, there is still a paucity of a data about the safety of newer ICM. Objective: To assess the incidence of CA-AKI in hospitalized patients that were exposed to computed tomography (CT) with and without ICM. Methods: Prospective cohort study with 1003 patients who underwent CT in a tertiary hospital from December 2020 through March 2021. All inpatients aged > 18 years who had a CT scan during this period were screened for the study. CA-AKI was defined as a relative increase of serum creatinine of ≥ 50% from baseline or an absolute increase of ≥ 0.3 mg/dL within 18 to 48 hours after the CT. Chi-squared test, Kruskal-Wallis test, and linear regression model with restricted cubic splines were used for statistical analyses. Results: The incidence of CA-AKI was 10.1% in the ICM-exposed group and 12.4% in the control group when using the absolute increase criterion. The creatinine variation from baseline was not significantly different between groups. After adjusting for baseline factors, contrast use did not correlate with worse renal function. Conclusion: The rate of CA-AKI is very low, if present at all, with newer ICMs, and excessive caution regarding contrast use is probably unwarranted.

RESUMO Introdução: Lesão renal aguda associada ao contraste (LRA-AC) é uma deterioração da função renal que ocorre após a administração de meio de contraste iodado (MCI). A maioria dos estudos que definiram esse fenômeno utilizaram MCI mais antigos, mais propensos a causar LRA-AC. Na última década, diversos artigos questionaram a verdadeira incidência de LRA-AC. Entretanto, ainda há escassez de dados sobre a segurança dos MCI mais novos. Objetivo: Avaliar a incidência de LRA-AC em pacientes hospitalizados expostos à tomografia computadorizada (TC) com e sem MCI. Métodos: Estudo de coorte prospectivo com 1.003 pacientes submetidos a TC em hospital terciário, de dezembro/2020 a março/2021. Todos os pacientes internados com idade ≥ 18 anos que realizaram TC nesse período foram selecionados. A LRA-AC foi definida como aumento relativo de creatinina sérica de ≥ 50% em relação ao valor basal ou aumento absoluto de ≥ 0,3 mg/dL dentro de 18 a 48 horas após a TC. Utilizamos o teste qui-quadrado, teste de Kruskal-Wallis e modelo de regressão linear com splines cúbicos restritos para análises estatísticas. Resultados: A incidência de LRA-AC foi 10,1% no grupo exposto ao MCI e 12,4% no grupo controle ao usar o critério de aumento absoluto. A variação da creatinina em relação ao valor basal não foi significativamente diferente entre os grupos. Após ajuste para fatores basais, o uso de contraste não se correlacionou com pior função renal. Conclusão: A taxa de LRA-AC é muito baixa, caso exista, com MCIs mais novos, e a cautela excessiva quanto ao uso de contraste provavelmente não se justifica.

Multimodality imaging in arrhythmogenic cardiomyopathy - From diagnosis to management.

Arrhythmogenic Cardiomyopathy (AC), an inherited cardiac disorder characterized by myocardial fibrofatty replacement, carries a significant risk of sudden cardiac death (SCD) due to ventricular arrhythmias. A comprehensive multimodality imaging approach, including echocardiography, cardiac magnetic resonance imaging (CMR), and cardiac computed tomography (CCT), allows for accurate diagnosis, effective risk stratification, vigilant monitoring, and appropriate intervention, leading to improved patient outcomes and the prevention of SCD. Echocardiography is primary tool ventricular morphology and function assessment, CMR provides detailed visualization, CCT is essential in early stages for excluding congenital anomalies and coronary artery disease. Echocardiography is preferred for follow-up, with CMR capturing changes over time. The strategic use of these imaging methods aids in confirming AC, differentiating it from other conditions, tracking its progression, managing complications, and addressing end-stage scenarios.

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.

BACKGROUND: Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data. METHODS: The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies. RESULTS: Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information. CONCLUSION: The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.

Variations in the Root Form and Root Canal Morphology of Permanent Mandibular canine.

BACKGROUND: Mandibular canines are recognized as usually having one root and one root canal in most cases. However, many investigators have reported the anatomical variations associated with mandibular canines. Thus; the objective of this study is to determine the number of roots and morphology of the root canal system of permanent mandibular canine in a Nepalese population. METHODS: Cone Beam Computerized Tomography images of 390 patients in a Nepalese population were selected, and a total of 780 mandibular canines were analyzed. The number of root and the canal configurations were investigated. Data were analyzed with descriptive analysis and Chi-square tests using the Statistical Package for the Social Sciences (SPSS) software version 20 (SPSS Inc, Chicago, IL, USA). RESULTS: Out of the 780 mandibular canines, 741(95%) were single-rooted canines while only 39 (5%) were double-rooted canines. The most common type of Vertucci in single-rooted canines was Type I (1-1) in the percentage of 85.6% and the least type was Type IV (1-2) in the percentage of (2.5%). The Chi-square tests showed no significant association between gender and number of roots (P = 0.87) and gender and root canal configuration in single-rooted canine (P = 0.52). CONCLUSIONS: All mandibular permanent canines were single rooted but 5.2% of the permanent mandibular canines had two roots.

Extragonadal Germ Cell Tumor with Hepatic Infiltration in a Child: Aberrant Somatostatin Receptors and the Diagnostic Conundrum.

Extragonadal germ cell tumors (GCTs) are challenging to diagnose. We present a case of suprarenal GCT, with hepatic infiltration where differential diagnosis included neuroblastoma and hepatoblastoma. The positive positron emission tomography scan further obfuscated the situation. The diagnosis was clinched by fine-needle aspiration cytology and cell block immunohistochemistry.

The Comparison of Ultrasound and Tomographic Images of Lung Involvement in Critically Ill Patients With COVID-19 Pneumonia: A Prospective Observational Study.

Introduction Computed tomography (CT) has a high sensitivity for diagnosing COVID-19 pneumonia in critically ill patients, but it has significant limitations. Lung ultrasonography (LUS) is an imaging method increasingly used in intensive care units. Our primary aim is to evaluate the relationship between LUS and CT images by scoring a critically ill patient who was previously diagnosed with COVID-19 pneumonia and underwent CT, as well as to determine their relationship with the patient's oxygenation. Methods This was a single-center, prospective observational study. The study included COVID-19 patients (positive reverse transcription polymerase chain reaction, RT-PCR) who were admitted to the intensive care unit between June 2020 and December 2020, whose oxygen saturation (SpO2) was below 92%, and who underwent a chest tomography scan within the last 12 hours. CT findings were scored by the radiologist using the COVID-19 Reporting and Data System (CO-RADS). The intensivist evaluated 12 regions to determine the LUS score. The ratio of the partial pressure of oxygen in the arterial blood to the inspiratory oxygen concentration (PaO2/FiO2) was used to assess the patient's oxygenation. Results The study included 30 patients and found a weak correlation (ICC = 0.45, 95% CI = 0.25-0.65, p < 0.05) between total scores obtained from LUS and CT scans. The correlation between the total LUS score and oxygenation (r = -0.514, p = 0.004) was stronger than that between the CT score and oxygenation (r = -0.400, p = 0.028). The most common sonographic findings were abnormalities in the pleural line, white lung, and subpleural consolidation. On the other hand, the CT images revealed dense ground-glass opacities and consolidation patterns classified as CO-RADS 5. Conclusion A weak correlation was found between LUS and CT scores in critically ill COVID-19 pneumonia patients. Also, as both scores increased, oxygenation was detected to be impaired, and such a correlation is more evident with the LUS score.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

The application value of a vendor-specific deep learning image reconstruction algorithm in "triple low" head and neck computed tomography angiography.

Background: Head and neck computed tomography angiography (CTA) technology has become the noninvasive imaging method of choice for the diagnosis and long-term follow-up of vascular lesions of the head and neck. However, issues of radiation safety and contrast nephropathy associated with CTA examinations remain concerns. In recent years, deep learning image reconstruction (DLIR) algorithms have been increasingly used in clinical studies, demonstrating their potential for dose optimization. This study aimed to investigate the value of using a DLIR algorithm to reduce radiation and contrast doses in head and neck CTA. Methods: A total of 100 patients were prospectively enrolled and randomly divided into two groups. Group A (50 patients) consisted of those who underwent 70-kVp CTA with a low contrast volume and injection rate and who were classified according to the reconstruction algorithm into subgroups A1 [DLIR at high weighting (DLIR-H)], A2 [DLIR at low weighting (DLIR-L)], and A3 [volume-based adaptive statistical iterative reconstruction with 50% weighting (ASIR-V50%)]. Meanwhile, group B (50 patients) consisted of those who underwent standard radiation and contrast doses at 100 kVp with ASIR-V50% reconstruction. The computed tomography (CT) attenuation, background noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality score (SIQS) were statistically compared for several vessels among the four groups. Results: Group A showed significant reductions in contrast dosage, injection rate, and radiation dose of 36.09%, 20.88%, and 47.80%, respectively, compared to group B (all P<0.001). The four groups differed significantly in terms of background noise (all P<0.05) with group A1 having the lowest value. Group A1 also had significantly higher SNR and CNR values compared to group B in all vessels (all P<0.05) except the M1 of the middle cerebral artery for the SNR. Group A1 also had the highest SIQS, followed by the A2, B, and A3 groups. The SIQS showed good agreement between the two reviewers in all groups, with κ values between 0.88 and 1. Conclusions: Compared to the standard-dose protocol using 100 kVp and ASIR-V50%, a protocol of 70 kVp combined with DLIR-H significantly reduces the radiation dose, contrast dose, and injection rate in head and neck CTA while still significantly improving image quality for patients with a standard body size.

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography.

Background: It is crucial to distinguish unstable from stable intracranial aneurysms (IAs) as early as possible to derive optimal clinical decision-making for further treatment or follow-up. The aim of this study was to investigate the value of a deep learning model (DLM) in identifying unstable IAs from computed tomography angiography (CTA) images and to compare its discriminatory ability with that of a conventional logistic regression model (LRM). Methods: From August 2011 to May 2021, a total of 1,049 patients with 681 unstable IAs and 556 stable IAs were retrospectively analyzed. IAs were randomly divided into training (64%), internal validation (16%), and test sets (20%). Convolutional neural network (CNN) analysis and conventional logistic regression (LR) were used to predict which IAs were unstable. The area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the discriminating ability of the models. One hundred and ninety-seven patients with 229 IAs from Banan Hospital were used for external validation sets. Results: The conventional LRM showed 11 unstable risk factors, including clinical and IA characteristics. The LRM had an AUC of 0.963 [95% confidence interval (CI): 0.941-0.986], a sensitivity, specificity and accuracy on the external validation set of 0.922, 0.906, and 0.913, respectively, in predicting unstable IAs. In predicting unstable IAs, the DLM had an AUC of 0.771 (95% CI: 0.582-0.960), a sensitivity, specificity and accuracy on the external validation set of 0.694, 0.929, and 0.782, respectively. Conclusions: The CNN-based DLM applied to CTA images did not outperform the conventional LRM in predicting unstable IAs. The patient clinical and IA morphological parameters remain critical factors for ensuring IA stability. Further studies are needed to enhance the diagnostic accuracy.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category.

Background: The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods: A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results: The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions: The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.

Association of Retinal Neurovascular Impairment with Disease Severity in Patients with Major Depressive Disorder: An Optical Coherence Tomography Angiography Study.

Background: Identifying the fundus objective biomarkers for the major depressive disorders (MDD) may help promote mental health. The aim of this study was to evaluate retinal neurovascular changes and further investigate their association with disease severity in MDD. Methods: This cross-sectional study conducted in the hospital enrolled patients with MDD and healthy controls.The retinal neurovascular parameters for all subjects, including vessel density (VD), thickness of ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL), and optic nerve head (ONH) eg are automatically calculated by the software in optical coherence tomography angiography (OCTA). The severity of MDD including depressive symptoms, anxiety, cognition, and insomnia was assessed by Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), Montreal Cognitive Assessment (MoCA), and Insomnia Severity Index (ISI) respectively. Results: This study included 74 MDD patients (n=74 eyes) and 60 healthy controls (HCs) (n=60 eyes). MDD patients showed significantly decreased VD of superficial and deep capillary plexus, thickness of GCC and RNFL, and volume of ONH (all p<0.05) and increased vertical cup-to-disc ratio and global loss volume (GLV) (all p<0.05) compared to HCs. Positive associations were found between HAMD scores and cup area (r=0.30, p=0.035), cup volume (r=0.31, p=0.029), and disc area (r=0.33, p=0.020) as well as ISI scores and RNFL thickness (r=0.34, p=0.047). Conclusion: We found the retinal neurovascular impairment and its association with disease severity in MDD patients. OCTA showed promise as a potential complementary assessment tool for MDD.

A challenging differential diagnosis - Leber's Hereditary Optic Neuropathy.

Leber's hereditary optic neuropathy (LHON) is the most common maternally inherited disease linked to mitochondrial DNA (mtDNA). The patients present with subacute asymmetric bilateral vision loss. Approximately 95% of the LHON cases are caused by m.3460G>A (MTND1), m.11778G>A (MTND4), and m.14484T>C (MTND6) mutations. The hallmark of hereditary optic neuropathies determined by mitochondrial dysfunction is the vulnerability and degeneration of retinal ganglion cells (RGC). We present the case of a 28-year-old man who came to our clinic complaining of a subacute decrease in visual acuity of his left eye. From his medical history, we found out that one month before he had the same symptoms in the right eye. From the family history, we noted that an uncle has had vision problems since childhood. We carried out complete blood tests, including specific antibodies for autoimmune and infectious diseases. Laboratory tests and MRI were within normal limits. A blood test of the mtDNA showed the presence of 11778 G>A mutation on the mtND6 gene. The medical history, the fundus appearance, the OCT, and the paraclinical investigations, made us diagnose our patient with Leber's hereditary optic neuropathy. As soon as possible, we began the treatment with systemic idebenone, 900 mg/day. We examined the patient 2, 6, and 10 weeks after initiating the treatment. Abbreviations: LHON = Leber's Hereditary Optic Neuropathy, mtDNA = mitochondrial DNA, VA = visual acuity, RE = right eye, LE = left eye, OCT = Optical coherence tomography, pRNFL = peripapillary retinal nerve fiber layer, GCL = retinal ganglion cells layer, MRI = magnetic resonance imaging, VEP = visual evoked potentials, VEP IT = VEP implicit time, VEP A = VEP amplitude.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

Research trends and hotspots in the field of electrical impedance tomography for mechanical ventilation: a bibliometric analysis.

Background: Electrical impedance tomography (EIT) is a relatively recent functional imaging technique that is both noninvasive and radiation free. EIT measures the associated voltage when a weak current is applied to the surface of the human body to determine the distribution of electrical resistance within tissues. We performed a bibliometrics-based review to explore the geographic hotspots of current research and future trends developing in the field of EIT for mechanical ventilation. Methods: The Web of Science database was searched from its inception to June 25, 2023. CiteSpace software was used to visualize and analyze the relevant literature and identify the most impactful literature, trends, and hotspots. Results: 363 articles describing EIT use in mechanical ventilation were identified. A fluctuating growth in the number of publications was observed from 1998 to 2023. Germany had the highest number of articles (n=154), followed by Italy (n=53) and China (n=52). A cluster analysis of keyword co-occurrence revealed that "titration", "ventilator-related lung injury", and "oxygenation" were the most actively researched terms associated with the use of EIT in mechanically ventilated patients. Conclusions: Significant progress has been made in EIT research for mechanical ventilation. EIT research is limited to a small number of countries with a present research focus on the prevention and treatment of ventilator-related lung injury, oxygenation status, and prone ventilation. These topics are expected to remain research hotspots in the future.

Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules.

Background: The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules. Methods: This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA. Conclusions: The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.