Ferrocene probe-assisted fluorescence quenching of PEI-carbon dots for NO detection and the logic gates based sensing of NO enabled by trimodal detection

Our research demonstrates the effectiveness of fluorescence quenching between polyethyleneimine functionalised carbon dots (PEI-CDs) and cyclodextrin encapsulated ferrocene for fluorogenic detection of nitric oxide (NO). We confirmed that ferrocene can be used as a NO probe by observing its ability to quench the fluorescence emitted from PEI-CDs, with NO concentrations ranging from 1 × 10-6 M to 5 × 10-4 M. The photoluminescence intensity (PL) of PEI-CDs decreased linearly, with a detection limit of 500 nM. Previous studies have shown that ferrocene is a selective probe for NO detection in biological systems by electrochemical and colorimetric methods. The addition of fluorogenic NO detection using ferrocene as a probe enables the development of a three-way sensor probe for NO. Furthermore, the triple mode NO detection (electrochemical, colorimetric, and fluorogenic) with ferrocene aids in processing sensing data in a controlled manner similar to Boolean logic operations. This work presents key findings on the mechanism of fluorescence quenching between ferrocene hyponitrite intermediate and PEI-CDs, the potential of using ferrocene for triple channel NO detection as a single molecular entity, and the application of logic gates for NO sensing.

Computed Tomography Angiography for Aortic Diseases

Aortic pathologies encompass a heterogeneous group of disorders, including acute aortic syndrome, traumatic aortic injury , aneurysm, aortitis, and atherosclerosis. The clinical manifestations of these disorders can be varied and non-specific, ranging from acute presentations in the emergency department to chronic incidental findings in an outpatient setting. Given the non-specific nature of their clinical presentations, the reliance on non-invasive imaging for screening, definitive diagnosis, therapeutic strategy planning, and post-intervention surveillance has become paramount. Commonly used imaging modalities include ultrasound, computed tomography (CT), and MR imaging. Among these modalities, computed tomography angiography (CTA) has emerged as a first-line imaging modality owing to its excellent anatomic detail, widespread availability, established imaging protocols, evidence-proven indications, and rapid acquisition time.

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation

RATIONALE AND OBJECTIVES

Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem.

MATERIALS AND METHODS

T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development. Subsequent external validation was performed on 59 cases (27 GB and 32 BM) from another institution. Four different mask-sequence combinations were evaluated using area under the curve (AUC), precision, recall and F1-scores. Diagnostic performance of a neuroradiologist and a general radiologist, both without and with the model output available, was also assessed.

RESULTS

3D-model using the T1-CE tumor mask (TM) showed the highest performance [AUC 0.93 (95% CI 0.858-0.995)] on the external test set, followed closely by the model using T1-CE TM and FLAIR mask of peri-tumoral region (PTR) [AUC of 0.91 (95% CI 0.834-0.986)]. Models using T2WI masks showed robust performance on the internal dataset but lower performance on the external set. Both neuroradiologist and general radiologist showed improved performance with model output provided [AUC increased from 0.89 to 0.968 (p = 0.06) and from 0.78 to 0.965 (p = 0.007) respectively], the latter being statistically significant.

CONCLUSION

3D-CNNs showed robust performance for differentiating GB from BMs, with T1-CE TM, either alone or combined with FLAIR-PTR masks. Availability of model output significantly improved the accuracy of the general radiologist.

Pulmonary Artery Stenosis After an Orthotopic Heart Transplantation: A Case Report With Cardiac Imaging Findings and a Literature Review

Pulmonary artery stenosis is a rare complication of heart transplantation. It is typically a congenital condition or can be secondary to rheumatic fever, systemic vasculitis like Behcet's disease, or Takayasu's arteritis. It can also occur as a rarity of a delayed complication post-heart transplant. In this report, we describe the imaging findings of pulmonary artery stenosis in a patient who underwent an orthotopic heart transplant more than 10 years prior. Dynamic cardiac magnetic resonance imaging (MRI), phase contrast imaging, and MR angiography in the management of pulmonary artery stenosis helped in heart and pulmonary circulation. Functional evaluation can be achieved with current multichannel transmit-receive coils. Cardiac gated pre- and dynamic contrast-enhanced MR was performed with phase-contrast imaging for further evaluation confirming the diagnosis of pulmonary artery stenosis.

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults

RATIONALE AND OBJECTIVES

The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA).

MATERIALS AND METHODS

A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.6 ± 3.9 years. CMR examinations were conducted using 1.5T scanners, and strain analysis was performed using TrufiStrain research prototype software (Siemens Healthineers, Erlangen, Germany). Global and layer-specific strain parameters were extracted from balanced Steady-state free precession cine images. Statistical analyses were conducted to evaluate the impact of demographic variables on strain measurements.

RESULTS

The peak global longitudinal strain (LS) was -16.0 ± 3.0%, peak global radial strain (RS) was 29.9 ± 6.3%, and peak global circumferential strain (CS) was -17.0 ± 1.8%. Global LS differed significantly between males and females. Transmural strain analysis showed a consistent pattern of decreasing LS and CS from endocardium to epicardium, while radial strain increased. Basal-to-apical strain distribution exhibited decreasing LS and increasing CS in both global and layer-specific analysis.

CONCLUSION

This study uses DRA to provide reference values for global and layer-specific strain in healthy children and young adults. The study highlights the impact of sex and age on LS and body mass index on RS. These insights are vital for future cardiac assessments in children, particularly for early detection of heart diseases.

Super-resolution deep neural network (SRDNN) based multi-image steganography for highly secured lossless image transmission

Information exchange and communication through the Internet are one of the most crucial aspects of today's information technology world. The security of information transmitted online has grown to be a critical concern, particularly in the transfer of medical data. To overcome this, the data must be delivered securely without being altered or lost. This can be possibly done by combining the principles of cryptography and steganography. In the recent past, steganography is used with simpler methods like the least significant bit manipulation technique, in order to encode a lower-resolution image into a higher-resolution image. Here, we attempt to use deep neural networks to combine many two-dimensional colour images of the same resolution into a single cover image with the same resolution. In this technique, many secret images are concealed inside a single cover image using deep neural networks. The embedded cover image is then encrypted using a 3D chaotic map for diffusion and elliptic curve cryptography (ECC) for confusion to increase security.Supporting the fact that neural networks experience losses, the proposed system recovers up to 93% of the hidden image concealed in the original image. As the secret image features are identified and combined along with the cover image, the time complexity involved in the security process is minimized by 78% compared to securing the original data.

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification

RATIONALE AND OBJECTIVES

Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex.

MATERIALS AND METHODS

In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots).

RESULTS

The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation.

CONCLUSION

Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation.

"From Vision to Reality: Virtual Reality's Impact on Baffle Planning in Congenital Heart Disease"

This study aims to evaluate the feasibility and utility of virtual reality (VR) for baffle planning in congenital heart disease (CHD), specifically by creating patient-specific 3D heart models and assessing a user-friendly VR interface. Patient-specific 3D heart models were created using high-resolution imaging data and a VR interface was developed for baffle planning. The process of model creation and the VR interface were assessed for their feasibility, usability, and clinical relevance. Collaborative and interactive planning within the VR space were also explored. The study findings demonstrate the feasibility and usefulness of VR in baffle planning for CHD. Patient-specific 3D heart models generated from imaging data provided valuable insights into complex spatial relationships. The developed VR interface allowed clinicians to interact with the models, simulate different baffle configurations, and assess their impact on blood flow. The VR space's collaborative and interactive planning enhanced the baffle planning process. This study highlights the potential of VR as a valuable tool in baffle planning for CHD. The findings demonstrate the feasibility of using patient-specific 3D heart models and a user-friendly VR interface to enhance surgical planning and patient outcomes. Further research and development in this field are warranted to harness the full benefits of VR technology in CHD surgical management.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging

INTRODUCTION

Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS).

METHODOLOGY

A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models.

RESULTS

For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case.

CONCLUSIONS

When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

Motion Compensated Unsupervised Deep Learning for 5D MRI

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases. The proposed algorithm is a more data-efficient alternative to current motion-resolved reconstructions. This motion-compensated approach models the data in each cardiac/respiratory bin as Fourier samples of the deformed version of a 3D image template. The deformation maps are modeled by a convolutional neural network driven by the physiological phase information. The deformation maps and the template are then jointly estimated from the measured data. The cardiac and respiratory phases are estimated from 1D navigators using an auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired from two subjects.

Factors associated with unexplained sudden deaths among adults aged 18-45 years in India - A multicentric matched case-control study

BACKGROUND OBJECTIVES

In view of anecdotal reports of sudden unexplained deaths in India's apparently healthy young adults, linking to coronavirus disease 2019 (COVID-19) infection or vaccination, we determined the factors associated with such deaths in individuals aged 18-45 years through a multicentric matched case-control study.

METHODS

This study was conducted through participation of 47 tertiary care hospitals across India. Cases were apparently healthy individuals aged 18-45 years without any known co-morbidity, who suddenly (<24 h of hospitalization or seen apparently healthy 24 h before death) died of unexplained causes during 1 st October 2021-31 st March 2023. Four controls were included per case matched for age, gender and neighborhood. We interviewed/perused records to collect data on COVID-19 vaccination/infection and post-COVID-19 conditions, family history of sudden death, smoking, recreational drug use, alcohol frequency and binge drinking and vigorous-intensity physical activity two days before death/interviews. We developed regression models considering COVID-19 vaccination ≤42 days before outcome, any vaccine received anytime and vaccine doses to compute an adjusted matched odds ratio (aOR) with 95 per cent confidence interval (CI).

RESULTS

Seven hundred twenty nine cases and 2916 controls were included in the analysis. Receipt of at least one dose of COVID-19 vaccine lowered the odds [aOR (95% CI)] for unexplained sudden death [0.58 (0.37, 0.92)], whereas past COVID-19 hospitalization [3.8 (1.36, 10.61)], family history of sudden death [2.53 (1.52, 4.21)], binge drinking 48 h before death/interview [5.29 (2.57, 10.89)], use of recreational drug/substance [2.92 (1.1, 7.71)] and performing vigorous-intensity physical activity 48 h before death/interview [3.7 (1.36, 10.05)] were positively associated. Two doses lowered the odds of unexplained sudden death [0.51 (0.28, 0.91)], whereas single dose did not.

INTERPRETATION CONCLUSIONS

COVID-19 vaccination did not increase the risk of unexplained sudden death among young adults in India. Past COVID-19 hospitalization, family history of sudden death and certain lifestyle behaviors increased the likelihood of unexplained sudden death.

Proposed competencies for the performance of cardiovascular computed tomography in pediatric and adult congenital heart disease

Cardiovascular computed tomography (CCT) is rated appropriate by published guidelines for the initial evaluation and follow up of congenital heart disease (CHD) and is an essential modality in cardiac imaging programs for patients of all ages. However, no recommended core competencies exist to guide CCT in CHD imaging training pathways, curricula development, or establishment of a more formal educational platform. To fill this gap, a group of experienced congenital cardiac imagers, intentionally inclusive of adult and pediatric cardiologists and radiologists, was formed to propose core competencies fundamental to the expert-level performance of CCT in pediatric acquired and congenital heart disease and adult CHD. The 2020 SCCT Guideline for Training Cardiology and Radiology Trainees as Independent Practitioners (Level II) and Advanced Practitioners (Level III) in Cardiovascular Computed Tomography (1) for adult imaging were used as a framework to define pediatric and CHD-specific competencies. Established competencies will be immediately relevant for advanced cardiac imaging fellowships in both cardiology and radiology training pathways. Proposed future steps include radiology and cardiology society collaboration to establish provider certification levels, training case-volume recommendations, and continuing medical education (CME) requirements for expert-level performance of CCT in pediatric and adult CHD.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods

PURPOSE

To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

METHODOLOGY

Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC.

RESULTS

Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]).

CONCLUSION

Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters

Purpose

To investigate the effect of ComBat harmonization methods on the robustness of cardiac MRI-derived radiomic features to variations in imaging parameters.

Materials and Methods

This Health Insurance Portability and Accountability Act-compliant retrospective study used a publicly available data set of 11 healthy controls (mean age, 33 years ± 16 [SD]; six men) and five patients (mean age, 52 years ± 16; four men). A single midventricular short-axis section was acquired with 3-T MRI using cine balanced steady-state free precision, T1-weighted, T2-weighted, T1 mapping, and T2 mapping imaging sequences. Each sequence was acquired using baseline parameters and after variations in flip angle, spatial resolution, section thickness, and parallel imaging. Image registration was performed for all sequences at a per-individual level. Manual myocardial contouring was performed, and 1652 radiomic features per sequence were extracted using baseline and variations in imaging parameters. Radiomic feature stability to change in imaging parameters was assessed using Cohen d sensitivity. The stability of radiomic features was assessed both without and after ComBat harmonization of radiomic features. Three ComBat methods were studied: parametric, nonparametric, and Gaussian mixture model (GMM).

Results

For all sequences combined, 51.4% of features were robust to changes in imaging parameters when no ComBat method was applied. ComBat harmonization substantially increased the number of stable features to 95.1% (95% CI: 94.9, 95.3) when parametric ComBat was used and 90.9% (95% CI: 90.6, 91.2) when nonparametric ComBat was used. GMM combat resulted in only 52.6% stable features.

Conclusion

ComBat harmonization improved the stability of radiomic features to changes in imaging parameters across all cardiac MRI sequences.Keywords: Cardiac MRI, Radiomics, ComBat, Harmonization Supplemental material is available for this article. © RSNA, 2023.

A Study to Assess Etiology and Prevalence of Signs and Symptoms of Temporomandibular Disorder

Aim

The purpose of this epidemiological research was to determine how common temporomandibular disorder (TMD) symptoms are.

Materials and Methods

Among the outpatient population, a sample of 100 people was chosen at random. A patient survey was presented to each person, and points were allotted based on their responses. Participants were categorized as having no symptoms of TMD, mild TMD, moderate TMD, or severe TMD based on their total score.

Results

One hundred adults aged 18 and above were selected from the general public to take part in the research. Temporomandibular disorder was present in no more than 30% of those studied, in the mild range in 50%, in the moderate range in 15%, and in the severe range in 5%. 0.602 was determined to be the dependability of Fonseca's questionnaire.

Conclusion

The Fonseca questionnaire is a useful instrument for gauging the extent to which TMD symptoms are present in a given population. The screening questionnaire may be received in a short amount of time and for little money, making it a good option for public health services.

Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80-100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human.

Self-negligence and awareness among oral precancerous and cancer patients - A cross-sectional questionnaire study

Background

The National Institute of Health and Family Welfare (NIHFW) reports that India has the highest global prevalence of oral cancers. The incidence is significantly more in developing countries when compared to the developed countries. Early detection is key to increasing the survival rate of the patients. Important causes for this late diagnosis could be self-negligence, lack of patient awareness about the causes and asymptomatic and subtle clinical presentation of the lesions.

Aim

To assess the causes of self-neglect and awareness levels among oral cancer and pre-cancerous patients.

Settings and Design

A cross-sectional questionnaire study was conducted among pre-cancerous and cancerous patients.

Methods and Material

A questionnaire with 16 closed-ended questions was framed relating to the causes of self-neglect and awareness of the patients. A total of 45 patients were selected by convenient sampling technique from the Institutional Tumour Board register of which 62 per cent were male patients and 38 per cent were female patients.

Statistical Analysis

Data analysis for demographic data, patients' awareness, and causes of self-neglect about precancer and cancer was done using SPSS Version 10.

Results and Conclusions

The present study concluded that the patients had adequate awareness that deleterious habits could lead to cancer but had a low level of awareness about the other causes of cancer, symptoms and management options available to treat cancer. The study result emphasizes that the government should plan for more cancer-screening camps in order to prevent the progression of cancer and to increase the awareness. (Reference I.D.: 2015-05006 for funding the project. ICMR).

Subclinical cardiac involvement in student athletes after COVID-19 infection - Evaluation using feature tracking cardiac MRI strain analysis

OBJECTIVES

To evaluate subclinical cardiac dysfunction in student athletes after COVID-19 infection using feature tracking cardiac MRI strain analysis.

METHODS

Student athletes with history of COVID-19 infection underwent cardiac MRI as part of screening before return to competitive play. Subjects were enrolled if they had no or mild symptoms, normal cardiac MRI findings with no imaging evidence of myocarditis. Feature tracking strain analysis was performed using short and long axis cine MRI images of athletes and a separate cohort of healthy controls. Differences between the cardiac strain parameters were statistically analyzed by Mann-Whitney U test.

RESULTS

The study cohort included 122 athletes (49 females, mean age 20 years ± 1.5 standard deviations) who had a history of COVID-19, and 35 healthy controls (24 females, mean age 34 years ± 18 standard deviations). COVID-19 positive athletes had normal physiologic cardiac adaptations, including significantly higher left and right ventricle end-diastolic volumes (p = 0.00001) when compared to healthy controls. There was no significant difference between biventricular ejection fraction between athletes and control subjects (p > 0.05). Cardiac MRI parameters, including left ventricle global longitudinal strain (LV-GLS), global circumferential strain (LV-GCS), and global radial strain (LV-GRS) values were normal but slightly lower in athletes compared to controls. LV-GCS and LV-GRS were significantly lower in athletes compared to controls (p = 0.007 and p = 0.005 respectively), but there was no significant difference for LV-GLS (p = 0.088).

CONCLUSION

In this study of 122 athletes, there was no evidence of subclinical myocardial alterations following recovery from COVID-19 found on cardiac MRI strain analysis. When compared to healthy controls, the competitive athletes had higher end-diastolic volume indices and reduced, albeit normal, strain values of LV-GLS, LV-GCS, and LV-GRS.

Utility of Cardiac Magnetic Resonance Imaging in Diagnosing Eosinophilic Myocarditis in a Patient Recently Recovered from COVID-19: A Grand Round Case Report

Background

Eosinophilic myocarditis secondary to eosinophilic granulomatosis with polyangiitis is a rare disease, for which cardiac magnetic resonance imaging is a useful non-invasive modality for diagnosis. We present a case of eosinophilic myocarditis in a patient who recently recovered from COVID-19 and discuss the role of cardiac magnetic resonance imaging and endomyocardial biopsy to differentiate between COVID-19 associated myocarditis and eosinophilic myocarditis.

Case summary

A 20-year-old Hispanic male with history of sinusitis and asthma, and recently recovered from COVID-19, presented to the emergency room with pleuritic chest pain, dyspnea on exertion, and cough. His presentation labs were pertinent for leukocytosis, eosinophilia, elevated troponin, and elevated ESR and CRP. EKG showed sinus tachycardia. Echocardiogram showed an ejection fraction of 40%. The patient was admitted, and on day two of admission, he underwent cardiac magnetic resonance imaging which showed findings of eosinophilic myocarditis and mural thrombi. On hospital day three patient underwent right heart catheterization and endomyocardial biopsy which confirmed eosinophilic myocarditis. Patient was treated with steroids and mepolizumab. He discharged on hospital day 7 and continued outpatient heart failure treatment.

Discussion

This is a unique case of eosinophilic myocarditis and heart failure with reduced ejection fraction as a presentation of eosinophilic granulomatosis with polyangiitis, in a patient recently recovered from COVID-19. In this case, cardiac magnetic resonance imaging and endomyocardial biopsy were critical to identify the cause of myocarditis and helped in optimal management of this patient.

Imaging of the Rare Association of Truncus Arteriosus in a Neonate with the Ductal Origin of the Left Subclavian Artery Using Multidetector CT Angiography and 3D Rendering

We present the unreported case of a rare association of truncus arteriosus with the ductal origin of the left subclavian artery. Understanding and preoperative identification of these aortic variations are essential to guide optimal surgical management. In this study, the role of advanced visualization 3D modeling techniques in imaging these complex anomalies is discussed.

Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke

OBJECTIVES

Automated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) images. To our knowledge this is the first study to detect LVO existence and location on raw 4D-CTA/ CT perfusion (CTP) images using neural network (NN) models.

MATERIALS AND METHODS

Retrospective study using data from a level-I stroke center was performed. A total of 306 (187 with LVO, and 119 without) patients were evaluated. Image pre-processing included co-registration, normalization and skull stripping. Five consecutive time-points for each patient were selected to provide variable contrast density in data. Additional data augmentation included rotation and horizonal image flipping. Our model architecture consisted of two neural networks, first for classification (based on hemispheric asymmetry), followed by second model for exact site of LVO detection. Only cases deemed positive by the classification model were routed to the detection model, thereby reducing false positives and improving specificity. The results were compared with expert annotated LVO detection.

RESULTS

Using a 80:20 split for training and validation, the combination of both classification and detection model achieved a sensitivity of 86.5%, a specificity of 89.5%, and an accuracy of 87.5%. A 5-fold cross-validation using the entire data achieved a mean sensitivity of 82.7%, a specificity of 89.8%, and an accuracy of 85.5% and a mean AUC of 0.89 (95% CI: 0.85-0.93).

CONCLUSION

Our findings suggest that accurate image-level LVO detection is feasible on CTP raw images.

CT Perfusion Maps Improve Detection of M2-MCA Occlusions in Acute Ischemic Stroke

OBJECTIVES

Middle cerebral artery occlusions, particularly M2 branch occlusions are challenging to identify on CTA. We hypothesized that additional review of the CTP maps will increase large vessel occlusion (LVO) detection accuracy on CTA and reduce interpretation time.

MATERIALS AND METHODS

Two readers (R1 and R2) retrospectively reviewed the CT studies in 99 patients (27 normal, 26 M1-MCA, 46 M2-MCA occlusions) who presented with suspected acute ischemic stroke (AIS). The time of interpretation and final diagnosis were recorded for the CTA images (derived from CTP data), both without and with the CTP maps. The time for analysis for all vascular occlusions was compared using McNemar tests. ROC curve analysis and McNemar tests were performed to assess changes in diagnostic performance with the addition of CTP maps.

RESULTS

With the addition of the CTP maps, both readers showed increased sensitivity (p = 0.01 for R1 and p = 0.04 for R2), and accuracy (p = 0.02 for R1 and p = 0.004 for R2) for M2-MCA occlusions. There was a significant improvement in diagnostic performance for both readers for detection of M2-MCA occlusions (AUC R1 = 0.86 to 0.95, R2 = 0.84 to 0.95; p < 0.05). Both readers showed reduced interpretation time for all cases combined, as well as for normal studies (p < 0.001) when CTP images were reviewed along with CTA. Both readers also showed reduced interpretation time for M2-MCA occlusions, which was significant for one of the readers (p < 0.02).

CONCLUSION

The addition of CTP maps improves accuracy and reduces interpretation time for detecting LVO and M2-MCA occlusions in AIS. Incorporation of CTP in acute stroke imaging protocols may improve detection of more distal occlusions.

Segmental testicular infarct with an associated testicular artery aneurysm: Case report of a rare clinical entity

Segmental testicular infarct is a rare clinical entity and can be a diagnostic challenge. Although cases are often idiopathic, underlying etiologies can include testicular torsion, epididymo-orchitis, trauma, vasculitis, and hypercoagulable states. Once suspected, an underlying testicular neoplasm should be excluded. We present a case of a 43-year-old male who developed acute onset left sided scrotal pain. A diagnostic scrotal ultrasound showed a focal, heterogeneous region in left testicle with absent focal Doppler signal, concerning for a segmental testicular infarction. There was no history of trauma, urinary symptoms, sexually transmitted diseases, or constitutional symptoms. Work up for associated underlying etiologies was negative. A computed tomography angiogram scan of the abdomen and pelvis revealed an incidental left testicular artery aneurysm. The patient's consulting multidisciplinary care teams included urology and vascular surgery. Urology deemed surgical intervention inappropriate for the segmental testicular infarct, and vascular surgery elected not to intervene on the testicular artery aneurysm due to risk of completing testicular infarct and damaging blood supply to the testis. The patient was discharged after achieving adequate pain control, and completion of inpatient work up. No underlying malignancy was diagnosed on follow up, and pain symptoms resolved. To the authors’ knowledge, no literature exists describing the concurrent incidence of a segmental testicular infarct and an ipsilateral testicular artery aneurysm. In this report, we aim to further describe both diagnoses, and explore the association between the 2 entities.

Factors influencing the acquisition of COVID infection among high-risk contacts of COVID-19 patients in Madurai district-A case control study

Introduction

COVID is a new disease; understanding the transmission dynamics and epidemiological characteristics may help in developing the effective control measures. The study is done 1. To determine the various factors influencing the acquisition of COVID-19 infection among high-risk contacts 2. To estimate the secondary attack rate among high-risk contacts 3. To determine the factors in COVID index cases influencing their secondary attack rate.

Methodology

Unmatched case control study was conducted from March to August 2020 among 139 COVID index cases in Madurai district from March-May (Reference period) and their 50 COVID positive (cases), 551 COVID negative (controls) high-risk contacts. Case investigation form* and contact tracing Proforma*were used to collect data. Chi-square test and independent sample t test were used to find out the association. Univariate* and Multivariate logistic regression* were used to predict the risk of various factors in acquisition of COVID infection with the help of adjusted and unadjusted odds ratio. P value < 0.05 was considered statistically significant.

Results

Male contacts (P = 0.005, OR = 2.520), overcrowding (P = 0.007, OR = 3.810), and duration of exposure to index case (for 4-7 days P = 0.014, OR = 2.902, for >7 days P = 0.001, OR = 6.748 and for > 12 hours/day P = 0.000, OR = 5.543) were significant factors predicted to be associated with acquisition of COVID infection among high-risk contacts. Reproductive number (R0)* estimated was 1.3. Secondary attack rate (SAR)* estimated among high-risk contacts was 8.32%. Index cases whose outcome was death (P = 0.026); symptomatic index cases (P = 0.000), cases with fever (P = 0.001); sorethroat (P = 0.019); breathlessness (P = 0.010); cough (P = 0.006) and running nose (P = 0.002) had significantly higher mean SAR than their counterparts.

Conclusion

Contacts with above said risk factors who were found to be more prone to infection could be given special focus to prevent the transmission in them.

Dual Energy CT Physics-A Primer for the Emergency Radiologist

Dual energy CT (DECT) refers to the acquisition of CT images at two energy spectra and can provide information about tissue composition beyond that obtainable by conventional CT. The attenuation of a photon beam varies depends on the atomic number and density of the attenuating material and the energy of the incoming photon beam. This differential attenuation of the beam at varying energy levels forms the basis of DECT imaging and enables separation of materials with different atomic numbers but similar CT attenuation. DECT can be used to detect and quantify materials like iodine, calcium, or uric acid. Several post-processing techniques are available to generate virtual non-contrast images, iodine maps, virtual mono-chromatic images, Mixed or weighted images and material specific images. Although initially the concept of dual energy CT was introduced in 1970, it is only over the past two decades that it has been extensively used in clinical practice owing to advances in CT hardware and post-processing capabilities. There are numerous applications of DECT in Emergency radiology including stroke imaging to differentiate intracranial hemorrhage and contrast staining, diagnosis of pulmonary embolism, characterization of incidentally detected renal and adrenal lesions, to reduce beam and metal hardening artifacts, in identification of uric acid renal stones and in the diagnosis of gout. This review article aims to provide the emergency radiologist with an overview of the physics and basic principles of dual energy CT. In addition, we discuss the types of DECT acquisition and post processing techniques including newer advances such as photon-counting CT followed by a brief discussion on the applications of DECT in Emergency radiology.

Assessment of retinal manifestations of Parkinson's disease using spectral domain optical coherence tomography: A study in Indian eyes

Purpose:

To assess the retinal manifestations of Parkinson’s disease using optical coherence tomography.

Methods:

A prospective case-control study comparing 30 eyes from 15 patients with Parkinson’s disease and 22 eyes from 11 healthy age-matched controls. Total macular subfield thickness and the thickness of the ganglion cell layer, nerve fiber layer, and peripapillary retinal nerve fiber layer were measured with spectral-domain optical coherence tomography (SD-OCT).

Results:

The mean age of PD patients was 68.4 years ± 10.64 (range: 46–82) and in the control group was 66.36 ± 5.22 (range: 64–68). The average disease duration in patients with PD was 6.7 ± 2.8 years (range: 2–10 years). The mean best-corrected visual acuity in PD was 20/26 and 20/20 in controls, with P = 0.0059, which was significant. Significant difference was also found in the contrast sensitivity between both groups. Structural differences in the central macular thickness (P = 0.0001), subfield thicknesses in the superior (P = 0.003), inferior (P = 0.001), nasal (P = 0.004), and temporal subfields (P = 0.017) was seen. Severe thinning of the ganglion cell layer was seen in PD patients (P = 0.000) as well as of the nerve fiber layer (P = 0.004). Peripapillary retinal nerve fiber thickness measured showed significant thinning in superotemporal (P = 0.000), superonasal (P = 0.04), inferonasal (P = 0.000), inferotemporal (P = 0.000), nasal (P = 0.000), and temporal quadrants (P = 0.000).

Conclusion:

Visual dysfunction was observed in patients with PD along with structural alterations on OCT, which included macular volumes, ganglion cell layer, and peripapillary retinal nerve fiber layer.

A cross sectional study on psychological impact of covid19 on post graduate doctors and Compulsory Rotatory Residential interns in COVID isolation ward of a tertiary care centre, Madurai

Background

COVID-19 pandemic causes major impact on economic, physical, mental well-being of people all over the world. Doctors are working in stressful, unprepared, limited resource setting, and they are under the continuous threat of getting infection. Managing mental health of these warriors is great importance. Hence the present study to estimate the psychological impact of COVID-19* and factors associated with it among doctors in tertiary care hospital, Madurai.

Methods

A Cross-sectional study was conducted during October–November 2020 using a pre-designed semi structured questionnaire and DASS-21 scale which were sent through Google form to doctors who were in their quarantine period after the COVID duty. Totally 292 responses were received. Descriptive statistics done to find frequencies and percentages. Correlation for continuous variables; Univariate and multivariate regression for categorical variables were used to predict the factors influencing the psychological impact.

Results

In our study, 42.1% doctors were depressed, 43.8% were stressed and 50.7% had anxiety. Depression*, anxiety*, stress* scores were positively correlated with number of COVID duties(r² 0.163,0.138,0.133), number of elderly persons(r² 0.188,0.169,0.188) in their family and negatively correlated with sleep duration(r ²–0.219,-0.281,-0.239), attitude of study participants(r²-0.319,-0.274,-0.291). Multiple logistic regression showed that disturbed sleep(odd'sratio = 3.931,2.734,3.420) and poor quality of sleep which affect the next day function(odd'sratio = 3.470,2.968,3.122) were significant predictors for all three psychological impacts.

Conclusion

High prevalence of psychological impact estimated, ensures the requirement of early screening with timely psychological intervention and establishment of guideline policies to support mental health of healthcare workers* for maintaining the functionality of healthcare system.

Formulation and Evaluation of Gabapentin Sustained Release Matrix Tablet Using Hibiscus rosa sinensis Leaves Mucilage as Release Retardant

The major goal of this study was to develop and evaluate Sustained release matrix tablets of Gabapentin with Hibiscus rosa - sinensis leaves mucilage prepared by using wet granulation technique with microcrystalline cellulose as a diluents and magnesium stearate as a lubricant. Pre-compression and post-compression evaluation of physicochemical parameters were carried out and to be within acceptable limits. Drug and polymer compatibility were validated by FTIR measurements. Further, tablets were evaluated for in vitro release study. To get the sustained release of Gabapentin, the concentration of Hibiscus rosa- sinensis mucilage was tuned with a gas-generating agent. The % drug release of all formulation from F1 to F5 showed 91.24%, 80.24%, 70.53%, 62.12% and 49.83% respectively. All the dosage form release kinetics was computed using zero order, first order, Higuchi, and Korsmeyer–Peppas methods. From the above results, it is concluded that the n value of formulation F5 showed 0.78 suggesting anomalous (non-fickian) behavior of the drug. Mucilage from the leaves of Hibiscus rosa-sinensis has a great retarding effect in drug release from sustained release tablets.

Demographic profile of COVID-19 positive mothers & their outcome in government Rajaji hospital, Madurai, Tamilnadu - A cross sectional Study

Background

COVID-19 is a new pandemic disease. This disease course and its effect on pregnancy is little known due to limited available data. The objective of this study was to describe the demographic profile of COVID-19 positive mothers admitted in Government Rajaji hospital, Madurai in terms of time, place and person and to assess the general and pregnancy outcome of study population.

Methods

This cross-sectional study was done among 381 COVID-19 positive mothers* admitted during March 22 – August 31, 2020 in dedicated COVID-19 hospital, Madurai. Data was collected using Case Investigation Form (CIF) as a part of Rapid Response Team*(RRT) by Community Medicine* Department and analysed using SPSS version 21. Descriptive statistics done; Chi-square test & Fischer exact test was done to find out association between patient profile and outcomes.

Results

Out of 381, 154 (40.4%) belonged to 21–25 years, 192 (50.4%) to rural area, 318 (83.5%) to 3rd trimester,189 (49.6%) Primi gravida. 125 (32.8%) were symptomatic and 153 (80.8%) had at least one comorbidity. Death as general outcome was 3 (0.8%), all of them were referred cases and had comorbidity like GDM/PIH. 10 (2.62%) had abortion or perinatal death, 14 (3.77%) had preterm delivery, 99 (25.98%) babies were born small for gestational age. Increased maternal age had more death but was not statistically significant; All symptomatic mothers (p = 0.000),1st & 2nd trimester (p = 0.000) mothers had statistically significant poor pregnancy outcome*.

Conclusion

COVID positive mothers with increased age, symptomatic, 1st & 2nd trimester were significantly associated with poor outcome, requires special attention. Early referral must be emphasized to mitigate maternal death.

A Rare Case of Emphysematous Osteomyelitis of Femur in an Intravenous Drug User

Emphysematous osteomyelitis (EO) is a rare condition characterized by the appearance of gas locules within the bone on imaging, usually as a result of anaerobic bacterial infection. We present the case of a 46-year-old known intravenous (IV) drug user who was admitted to the emergency department with intractable pain in the right groin. He was febrile with elevated white cell count and C-reactive protein. He underwent an X-ray and CT of the pelvis which demonstrated intraosseous gas in the proximal right femur. A diagnosis of EO was made radiologically, allowing for prompt antibiotic treatment and a plan for surgical debridement. There are only a handful of published cases of EO in the literature, only one of which has described IV drug use as the underlying factor.

SVEP1 IS IMPORTANT FOR MORPHOGENESIS OF LYMPHATIC SYSTEM: POSSIBLE IMPLICATIONS IN LYMPHEDEMA

SVEP1, also known as Polydom, is a large extracellular mosaic protein with functions in protein interactions and adhesion. Since Svep1 knockout animals show severe edema and lymphatic system malformations, the aim of this study is to evaluate the presence of SVEP1 variants in patients with lymphedema. We analyzed DNA from 246 lymphedema patients for variants in known lymphedema genes, 235 of whom tested negative and underwent a second testing for new candidate genes, including SVEP1, as reported here. We found three samples with rare heterozygous missense single-nucleotide variants in the SVEP1 gene. In one family, healthy members were found to carry the same variants and reported some subclinical edema. Based on our findings and a review of the literature, we propose SVEP1 as a candidate gene that should be sequenced in patients with lymphatic malformations, with or without lymphedema, in order to investigate and add evidence on its possible involvement in the development of lymphedema.

Multimodality imaging in a case of multiple pulmonary hyalinizing granulomas - A decade follow-up

A 44-year-old male was referred to our clinic (2015) to evaluate multiple lung nodules with increasing fatigue, dyspnea, and weight loss. He was being assessed to an outside hospital for the same since 2010. The X-ray and computed-tomography (CT)-chest showed numerous pulmonary nodules and bilateral hilar adenopathy. Imaging workup at our institute (2015) redemonstrated extensive calcified pulmonary nodules. 18fluoro-2-deoxy-d-glucose positron emission tomographyCT showed widespread pulmonary nodules with low-grade uptake. Video-assisted thoracic surgery lung biopsy revealed pulmonary hyalinizing granuloma (PHG). Recently because of increasing symptoms, he is being evaluated for a lung transplant. This case represents a rare diagnosis of PHG with a decade follow-up.

Vascular Involvement in Neurosarcoidosis: Early Experiences From Intracranial Vessel Wall Imaging

BACKGROUND AND OBJECTIVES

Cerebrovascular manifestations in neurosarcoidosis (NS) were previously considered rare but are being increasingly recognized. We report our preliminary experience in patients with NS who underwent high-resolution vessel wall imaging (VWI).

METHODS

A total of 13 consecutive patients with NS underwent VWI. Images were analyzed by 2 neuroradiologists in consensus. The assessment included segment-wise evaluation of larger- and medium-sized vessels (internal carotid artery, M1-M3 middle cerebral artery; A1-A3 anterior cerebral artery; V4 segments of vertebral arteries; basilar artery; and P1-P3 posterior cerebral artery), lenticulostriate perforator vessels, and medullary and deep cerebral veins. Cortical veins were not assessed due to flow-related artifacts. Brain biopsy findings were available in 6 cases and were also reviewed.

RESULTS

Mean patient age was 54.9 years (33-71 years) with an M:F of 8:5. Mean duration between initial diagnosis and VWI study was 18 months. Overall, 9/13 (69%) patients had vascular abnormalities. Circumferential large vessel enhancement was seen in 3/13 (23%) patients, whereas perforator vessel involvement was seen in 6/13 (46%) patients. Medullary and deep vein involvement was also seen in 6/13 patients. In addition, 7/13 (54%) patients had microhemorrhages in susceptibility-weighted imaging, and 4/13 (31%) had chronic infarcts. On biopsy, 5/6 cases showed perivascular granulomas with vessel wall involvement in all 5 cases.

DISCUSSION

Our preliminary findings suggest that involvement of intracranial vascular structures may be a common finding in patients with NS and should be routinely looked for. These findings appear concordant with previously reported autopsy literature and need to be validated on a larger scale.

Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

In present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

A cross-sectional study on COVID19 mortality among people below 30 years of age in Tamilnadu-2020

Introduction

The COVID19 pandemic has turned out to be one of the public health* burdens in 2020. The fear of deaths due to COVID19 has surmounted even in developed countries and hasn't spared young age. This study aims in assessing the mortality due to COVID19 among patients below 30years of age in TamilNadu.

Methods

The data was collected from a publicly available secondary data source(www.stopcorona.tn.gov.in)which is an official COVID19 state dashboard. Details of the young COVID19 deaths* under 30yrs of age, their gender, symptoms, Co-morbidities, date of symptoms, date of admission, and death were collected till October 2020. A total of 158 deaths were included in the analysis. Fischer exact test and Mann Whitney U test* were used and p-value <0.05 was considered significant.

Results

Among the 158 COVID19 deaths under 30 years of age, the median age affected was 25 years(IQR-7) and 70.3% (n-111) had at least one co-morbidity*. The median time interval between symptom onset and hospital admission was 3 days (IQR-3) and between admission and death was 4 days(IQR-7).There was a significant association of myocarditis, refractory seizures, Central nervous system involvement as the cause of death in the age group 0–15years, compared with 16–30years(p < 0.05). The majority of deaths occurred with a late presentation, also patients with higher age were admitted after 2 days of symptoms and the results were statistically significant(p < 0.05).

Conclusion

Understanding the age-dependent risk gradient and their trend of this new virus at young age* is essential for public health planning and prevent future deaths, future research gateways.

Role of FDG PET/CT for detection of primary tumor in patients with extracervical metastases from carcinoma of unknown primary

AIM

To explore the diagnostic performance of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to detect the primary tumor site in patients with extracervical metastases from carcinoma of unknown primary (CUP). We evaluated patient outcomes as overall survival (OS).

MATERIALS AND METHODS

In a single-center, retrospective study (2005-2019), patients with extracervical metastases from CUP underwent FDG PET/CT to detect primary tumor sites. The final diagnosis was based on histopathology/or clinical follow-up of at least 12 months.

RESULTS

A total of 83 patients [Male 41 (49%), mean age 59 ± 14 years, range: 32-83 years] fulfilled the inclusion/exclusion criteria and were enrolled for analysis. The primary tumor was detected in 36 out of 83 (43%) patients based on histopathology/or clinical follow-up. PET/CT suggested the primary tumor site in 39 (47%) patients with diagnostic accuracy of 87%, sensitivity 89%, specificity 85%, PPV 82%, NPV 91% and detection rate 39%. Patients with oligometastases (<3) (2.16 years, 1.04-2.54) and primary unidentified (1 year, 0.34-2.14) had longer median survival time compared to the patients with multiple metastases (0.67 years, 0.17-1.58, p = 0.009) and primary identified (0.67 years,0.16-1.33, p = 0.002). The SUVmax of the primary or metastatic lesions with maximum uptake was not significantly related to survival.

CONCLUSIONS

PET/CT could reveal the primary tumor site in 39% of the patients. It demonstrated the metastatic disease burden and distribution in patients with 'primary obscured', which directs management. Patients with multiple metastases and primary identified had a poorer prognosis. In patients with primary unidentified after PET/CT, a further search was futile.