Image-guided laser ablation in the treatment of recurrence of renal tumours: technique and preliminary results

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models

RATIONALE AND OBJECTIVES

Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease.

MATERIALS AND METHODS

We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations.

RESULTS

We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs.

CONCLUSION

Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications.

Are we happy with our work in globalization? Globalization experience, achievement motivation, and job seniority as predictors of work satisfaction in a group of office workers

BACKGROUND

The main aim of this study was to determine whether globalization experience is a predictor of work satisfaction. In addition, we inspected a regression model consisting of globalization experience, job seniority, and goal achievement to determine how much variance in work satisfaction is accounted for by globalization experience. Most the theoretical texts about globalization suggest its negative impact on everyday life. The negative effects are - work-life balance problem, weakening of mechanisms to protect against the fear of death, and uncertainty.

METHOD

250 office workers participated in the study (M_age = 38.37; 145 females and 105 males). They responded to paper-and-pencil anonymous questionnaires measuring globalization experience, achievement goals, and work satisfaction. Respondents were also asked about their job seniority. We used Spearman's rho correlations and multiple linear regression to check the basic linear relation between variables, and hierarchical multiple regression to determine which of them is the strongest predictor of work satisfaction.

RESULTS

The results indicated that globalization experience (R² change = 0.089; p < .05) is a statistically significant negative predictor of work satisfaction and job seniority (R² change = 0.056; p < .05) while achievement goals (R² change = 0.188; p < .001) are positive predictors of work satisfaction.

CONCLUSION

We concluded that further research on globalization experience is necessary because it is the precursory individualistic approach to globalization research and we obtained a statistically significant yet small relation with work satisfaction in correlation and regression analyses. The presented results are also the rationale for promoting mastery approach goals in the workplace to improve work satisfaction as they are statistically significant positive predictors of it.

Investigation of chalcopyrite removal from low-grade molybdenite using response surface methodology and its effect on molybdenum trioxide morphology by roasting

In this research, purification of molybdenite concentrate (MoS₂) using a nitric acid leaching process was employed for the improvement of molybdenum trioxide morphology during oxidative roasting in an air atmosphere. These experiments were performed using 19 trials designed with response surface methodology and three effective parameters being temperature, time, and acid molarity. It was found that the leaching process reduced the chalcopyrite content in the concentrate by more than 95%. The influence of chalcopyrite elimination and roasting temperature on the morphology and fiber growth of the MoO₃ was also investigated by SEM images. Copper plays an important role in controlling the morphology of MoO₃ and its decrease led to enhancing the length of quasi-rectangular microfibers from less than 30 μm for impure MoO₃ up to several centimeters for purified MoO₃.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

Identifying the areas of low self-reported confidence of internal medicine residents in geriatrics: a descriptive study of findings from a structured geriatrics skills assessment survey

BACKGROUND

Currently, no standardized methods exist to assess the geriatric skills and training needs of internal medicine trainees to enable them to become confident in caring for older patients. This study aimed to describe the self-reported confidence and training requirements in core geriatric skills amongst internal medicine residents in Toronto, Ontario using a standardized assessment tool.

METHODS

This study used a novel self-rating instrument, known as the Geriatric Skills Assessment Tool (GSAT), among incoming and current internal medicine residents at the University of Toronto, to describe self-reported confidence in performing, teaching and interest in further training with regard to 15 core geriatric skills previously identified by the American Board of Internal Medicine.

RESULTS

190 (75.1%) out of 253 eligible incoming (Year 0) and current internal medicine residents (Years 1-3) completed the GSAT. Year 1-3 internal medicine residents who had completed a geriatric rotation reported being significantly more confident in performing 13/15 (P < 0.001 to P = 0.04) and in teaching 9/15 GSAT skills (P < 0.001 to P = 0.04). Overall, the residents surveyed identified their highest confidence in administering the Mini-Mental Status Examination and lowest confidence in assessing fall risk using a gait and balance tool, and in evaluating and managing chronic pain.

CONCLUSION

A structured needs assessment like the GSAT can be valuable in identifying the geriatric training needs of internal medicine trainees based on their reported levels of self-confidence. Residents in internal medicine could further benefit from completing a mandatory geriatric rotation early in their training, since this may improve their overall confidence in providing care for the mostly older patients they will work with during their residency and beyond.

Correlation of chest CT severity score with clinical parameters in COVID-19 pulmonary disease in a tertiary care hospital in Delhi during the pandemic period

Background

Since November 2019, the rapid outbreak of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. COVID-19 disease is caused by a new variant of coronavirus, named as ‘severe acute respiratory syndrome coronavirus 2.’ Chest CT has a potential role in the diagnosis, detection of complications and in predicting clinical recovery of patients or progression of coronavirus disease 2019. Degree and severity of lung involvement can be assessed by 25 point CT severity score. This quantification plays an important role to modify the treatment plan at times in critically ill patient of COVID-19. Hence, the purpose of present study was to describe and quantify the severity of COVID-19 infection on chest computed tomography (CT) by 25-point CT severity score and to determine the relationship of CT severity score with clinical and laboratory parameters.

Results

A total of 150 patients with COVID-19 disease were assessed. Mean age of the study group was 54.46 years (62.7% males and 37.3% females). The most common comorbidity present in the study group was diabetes mellitus, which was present in 17.3% cases. Severity of disease was significantly associated with age of the patient. CT severity score was positively correlated with lymphopenia and raised CRP, D-dimer and serum ferritin levels. A significant statistical correlation was found between CT severity grade and patient survival.

Conclusions

This is a large comprehensive study, collecting data from 150 cases of COVID-19 pneumonia patients, in a tertiary care hospital in India to describe the correlation of CT severity score with clinical land laboratory parameters. Chest CT severity score correlates well with laboratory parameters and can aid in predicting COVID-19 disease outcome.

Current status of biopsy markers for the breast in clinical settings

INTRODUCTION

A breast biopsy marker is a very small object that is introduced into the breast to serve as a tissue marker. The placement of a breast marker following a biopsy or to mark an abnormality in the breast has become standard practice in the clinical setting. Breast biopsy markers offer a wide range of benefits which includes the prevention of re-biopsy of a benign tumor, differentiating multiple lesions within the breast, evaluation of the extent of a tumor, and increased precision during surgery.

AREAS COVERED

This review article presents a range of breast biopsy markers used in clinical practice. First, an overview of the necessity of breast markers in healthy breast management. Second, it summarizes the diversity in composition, shape, unique properties and features, and bio-absorbable carriers of breast biopsy markers. Finally, it also discusses the possible use of clinically approved breast biopsy markers in various scenarios and their implications.

EXPERT OPINION

This review serves as a guide in the selection of an appropriate breast marker. We believe that some of the common drawbacks associated with current breast biopsy markers can be overcome by developing novel polymer-metal and composite-based breast biopsy markers.

MiR-4461 Inhibits Tumorigenesis of Renal Cell Carcinoma by Targeting PPP1R3C

Background: Renal cell carcinoma (RCC) is one of the most common and malignant tumors in the urinary system. The aim of this research was to investigate the mechanism and clinical significance of miR-4461 in the RCC progression. Materials and Methods: Twenty-eight (28) paired RCC tissue samples and adjacent nontumor tissue samples, as well as RCC cell lines were used to measure the expression of miR-4461 and protein phosphatase 1 regulatory subunit 3C (PPP1R3C) transcript by real-time quantitative PCR. The target relationship between miR-4461 and PPP1R3C was predicted by TargetScan and further verified by dual-luciferase reporter gene assay and RNA pull-down assay. Cell Counting Kit-8 (CCK-8) assay and BrdU ELISA assay were performed to measure RCC cell viability and proliferation. In addition, caspase-3 activity assay and cell adhesion assay were implemented to measure RCC cell apoptosis and adhesion. Results: MiR-4461 was lowly expressed both in RCC tissues and cells, while upregulated PPP1R3C was tested in RCC tissues and cells. In addition, miR-4461 was validated to directly target PPP1R3C, thereby negatively regulating PPP1R3C. Particularly, miR-4461 exerted a clear inhibitory effect on the malignant phenotypes of RCC cells by binding and inhibiting PPP1R3C. Conclusion: MiR-4461, which served as a tumor suppressor, inhibited RCC progression by targeting and downregulating PPP1R3C.

WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections

BACKGROUND AND OBJECTIVE

The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians' efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community.

METHODS

This paper proposes a weakly-supervised framework named "Weak Variational Autoencoder for Localisation and Enhancement" (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE.

RESULTS

The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection.

CONCLUSIONS

Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Background

Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention.

Methods

This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model.

Results

The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively.

Conclusions

Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Adoption of M-Learning in Business English Course and Its Relationship to Learning Style Preferences: An Empirical Investigation

Learning around the world has been changed with the rapid development in technology which promotes the students to be more flexible and interactive with each other which has been encouraged by the mobile learning environment. Therefore, the current study intends to analyze the impact of inquiry learning, reflective thinking on problem-solving skills, and critical thinking skills with the mediation of peer communication. To carry out the study, data was collected from 378 college students in China by using survey forms. The analysis of the data and validation of the proposed hypotheses were conducted using Smart-PLS and structural equation modeling (SEM) technique. The results revealed that inquiry learning and reflective thinking affect problem-solving skills. However, inquiry learning and reflective thinking did not affect critical thinking skills. Moreover, the study found that peer communication mediated the relationship between reflective thinking, problem-solving skills, and between reflective thinking and critical thinking skills. However, peer communication did not mediate the relationship among inquiry learning as independent variable and problem-solving skills and critical thinking skills as dependent. The study has theoretically contributed by examining the impact of online learning styles on higher-order thinking skill (HOTS) in the M-learning environment. Also, the study greatly advances the literature by investigating the mediating role of peer communication. Practically, the colleges can improve the students HOTS by devising policies and educational programs focusing on learning styles.

The Bone Strain Index: An Innovative Dual X-ray Absorptiometry Bone Strength Index and Its Helpfulness in Clinical Medicine

Bone strain Index (BSI) is an innovative index of bone strength that provides information about skeletal resistance to loads not considered by existing indexes (Bone Mineral Density, BMD. Trabecular Bone Score, TBS. Hip Structural Analysis, HSA. Hip Axis Length, HAL), and, thus, improves the predictability of fragility fractures in osteoporotic patients. This improved predictability of fracture facilitates the possibility of timely intervention with appropriate therapies to reduce the risk of fracture. The development of the index was the result of combining clinical, radiographical and construction-engineering skills. In fact, from a physical point of view, primary and secondary osteoporosis, leading to bone fracture, are determined by an impairment of the physical properties of bone strength: density, internal structure, deformation and fatigue. Dual X-ray absorptiometry (DXA) is the gold standard for assessing bone properties, and it allows measurement of the BMD, which is reduced mainly in primary osteoporosis, the structural texture TBS, which can be particularly degraded in secondary osteoporosis, and the bone geometry (HSA, HAL). The authors recently conceived and developed a new bone deformation index named Bone Strain Index (BSI) that assesses the resistance of bone to loads. If the skeletal structure is equated to engineering construction, these three indexes are all considered to determine the load resistance of the construct. In particular, BSI allows clinicians to detect critical information that BMD and TBS cannot explain, and this information is essential for an accurate definition of a patient’s fracture risk. The literature demonstrates that both lumbar and femoral BSI discriminate fractured osteoporotic people, that they predict the first fragility fracture, and further fragility fractures, monitor anabolic treatment efficacy and detect patients affected by secondary osteoporosis. BSI is a new diagnostic tool that offers a unique perspective to clinical medicine to identify patients affected by primary and, specially, secondary osteoporosis. This literature review illustrates BSI’s state of the art and its ratio in clinical medicine.

Laser technology in urologic oncology

Laser technology has been used in Urology since the 80s with a lot of different applications in endoscopic and open surgery. With the developments of the technology and the introduction of new active medium and source of laser energy, this technology have become the gold standard not only in stone surgery but even in benign prostate enlargement (BPE) surgical treatment. Regarding urologic oncology, laser energy has now reached an important role in focal therapy and in conservative treatment. The possibility of having better functional outcomes without any relevant impact on oncological results led to an increased use of laser in penile surgery, with a significant mention in urological guidelines for this option. In urothelial cancers as well, both in conservative management of upper tract tumors that in the treatment of non muscle invasive bladder cancer, a clear role of these relatively new source of energy have been demonstrated. Finally, both in prostate that in renal cancer the strategy of focal therapy may take advantage from this precise and fine technology. In this review we analyzed and described the applications of laser energy in urological cancers with a specific focus on penile, urothelial and prostate cancer.

Role of Knowledge Management on the Sustainable Environment: Assessing the Moderating Effect of Innovative Culture

Environmental sustainability has become the need of the hour and has been emphasized immensely because of the increased environmental awareness and resulting problems caused due to negligence. This study has intended to determine the role of knowledge management (KM) practices in achieving a sustainable environment with the mediating role of environmental awareness and green technological use. The study further examined the moderating role of green innovative culture between the relationship of KM practices and a sustainable environment. The data were acquired from 378 managerial level personnel of the construction industry in China through questionnaires. Smart-PLS 3.3.3 was used to determine the study's hypothesis through the structural equation modeling (SEM) technique. The study found that KM practice has a significant relationship with a sustainable environment, environmental awareness, and green technological use. Also, environmental awareness has a significant effect on a sustainable environment. Moreover, it was found in the study that environmental awareness significantly mediated the relationship between KM practices and sustainable environment, but green technological use did not find any mediating effect on the relationship between KM practices and sustainable environment. Furthermore, green innovative culture considerably moderated the relationship between KM practices and a sustainable environment. Theoretically, this study contributes to the existing literature by incorporating and investigating the role of KM practices in a sustainable environment. Practically, this article presented some implications for the management concerning promoting KM practices and environmental awareness within the organization and developing a green innovative culture.

A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy

The resting HR is an upward trend with the development of chronic obstructive pulmonary disease (COPD) severity. Chest computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying COPD. Therefore, CT images should provide more information to analyze the lung and heart relationship. The relationship between HR variability and PFT or/and COPD has been fully revealed, but the relationship between resting HR variability and COPD radiomics features remains unclear. 231 sets of chest high-resolution CT (HRCT) images from "COPD patients" (at risk of COPD and stage I to IV) are segmented by the trained lung region segmentation model (ResU-Net). Based on the chest HRCT images and lung segmentation images, 231 sets of the original lung parenchyma images are obtained. 1316 COPD radiomics features of each subject are calculated by the original lung parenchyma images and its derived lung parenchyma images. The 13 selected COPD radiomics features related to the resting HR are generated from the Lasso model. A COPD radiomics features combination strategy is proposed to satisfy the significant change of the lung radiomics feature among the different COPD stages. Results show no significance between COPD stage Ⅰ and COPD stage Ⅱ of the 13 selected COPD radiomics features, and the lung radiomics feature Y1-Y4 (P > 0.05). The lung radiomics feature F2 with the dominant selected COPD radiomics features based on the proposed COPD radiomics features combination significantly increases with the development of COPD stages (P < 0.05). It is concluded that the lung radiomics feature F2 with the dominant selected COPD radiomics features not only can characterize the resting HR but also can characterize the COPD stage evolution.

Comparative evaluation of semi-quantitative CT-severity scoring versus serum lactate dehydrogenase as prognostic biomarkers for disease severity and clinical outcome of COVID-19 patients

Background

Coronavirus disease 2019 pandemic causes significant strain on healthcare infrastructure and medical resources. So, it becomes crucial to identify reliable predictor biomarkers for COVID-19 disease severity and short term mortality. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients. Our study is retrospective and aims to evaluate role of semi-quantitative CT-severity scoring versus LDH as prognostic biomarkers for COVID-19 disease severity and short-term clinical outcome.

Results

Two hundred sixty-six patients between April 2020 and November 2020 with positive RT-PCR results underwent non-enhanced CT scan chest in our hospital and were retrospectively evaluated for CT severity scoring and serum LDH level measurement. Data were correlated with clinical disease severity. CT severity score and LDH were significantly higher in severe and critical cases compared to mild cases (P value < 0.001). High predictive significance of CT severity score for COVID-19 disease course noted, with cut-off value ≥ 13 highly predictive of severe disease (96.96% accuracy); cut-off value ≥ 16 highly predictive of critical disease (94.21% accuracy); and cut-off value ≥ 19 highly predictive of short-term mortality (92.56% accuracy). CT severity score has higher sensitivity, specificity, positive, and negative predictive values as well as overall accuracy compared to LDH level in predicting severe, critical cases, and short-term mortality.

Conclusion

Semi-quantitative CT severity scoring has high predictive significance for COVID-19 disease severity and short-term mortality with higher sensitivity, specificity, and overall accuracy compared to LDH. Our study strongly supports the use of CT severity scoring as a powerful prognostic biomarker for COVID-19 disease severity and short-term clinical outcome to allow triage of need for hospital admission, earlier medical interference, and to effectively prioritize medical resources for cases with high mortality risk for better decision making and clinical outcome.

Recognition of phrenic paralysis as atypical presentation during CT chest examination of COVID-19 infection and its correlation with CT severity scoring: a local experience during pandemic era

Background

Coronavirus disease 2019 (COVID-19) was declared a global pandemic by the World Health Organization on March 11, 2020. COVID-19 infection is considered a multi-system disease with neurological, digestive, and cardiovascular symptoms and complications. It can trigger acute and diffuse endothelial dysfunction, resulting in a cytokine storm, most likely induced by the interleukin-6 (IL-6) amplifier. The peripheral and central neurological complications may explain some clinical manifestations such as vagus nerve palsy. The known main CT chest findings of COVID-19 pneumonia include ground glass patches, pulmonary consolidations, inter-lobar septal thickening, crazy paving appearance, and others. We presented our experience in the incidental discovery of phrenic nerve paralysis as atypical chest finding in patients with a known history of COVID-19-associated pneumonia, proved by RT-PCR and coming for evaluation of the lung changes. Patients with evidence of diaphragmatic paralysis underwent close follow-up with a re-evaluation of the phrenic nerve palsy at their routine follow-up for COVID-19 pneumonia. The association of the phrenic nerve palsy was correlated with the CT chest severity score.

Results

Among 1527 scanned patients with known COVID-19 pneumonia, we had recognized 23 patients (1.5%) with unilateral diaphragmatic paralysis, accidentally discovered during CT chest examination. Twenty-one patients had shown complete recovery of the associated diaphragmatic paralysis during their follow-up CT chest with regression or the near-total resolution of the pulmonary changes of COVID-19- pneumonia. No significant correlation between the incidence of unilateral diaphragmatic paralysis and CT severity score with p value = 0.28.

Conclusion

Phrenic paralysis is considered a serious but rare neurological complication of COVID-19 pneumonia. No significant correlation between the CT severity score and the incidental discovery of unilateral diaphragmatic paralysis. The majority of the cases show spontaneous recovery together with the improvement of the pulmonary changes of COVID-19 pneumonia. The association of phrenic paralysis with anosmia and dysgeusia could suggest a direct viral attack on the nerve cells.

The effect of decongestion on nasal airway patency and airflow

Nasal decongestant reduces blood flow to the nasal turbinates, reducing tissue volume and increasing nasal airway patency. This study maps the changes in nasal anatomy and measures how these changes affect nasal resistance, flow partitioning between superior and inferior cavity, flow patterns and wall shear stress. High-resolution MRI was applied to capture nasal anatomy in 10 healthy subjects before and after application of a topical decongestant. Computational fluid dynamics simulated nasal airflow at steady inspiratory flow rates of 15 L.min[Formula: see text] and 30 L.min[Formula: see text]. The results show decongestion mainly increases the cross-sectional area in the turbinate region and SAVR is reduced (median approximately 40[Formula: see text] reduction) in middle and lower parts of the cavity. Decongestion reduces nasal resistance by 50[Formula: see text] on average, while in the posterior cavity, nasal resistance decreases by a median factor of approximately 3 after decongestion. We also find decongestant regularises nasal airflow and alters the partitioning of flow, significantly decreasing flow through the superior portions of the nasal cavity. By comparing nasal anatomies and airflow in their normal state with that when pharmacologically decongested, this study provides data for a broad range of anatomy and airflow conditions, which may help characterize the extent of nasal variability.

SARS-CoV-2 PCR-positive and PCR-negative cases of pneumonia admitted to the hospital during the peak of COVID-19 pandemic: analysis of in-hospital and post-hospital mortality

BACKGROUND

During the spike of COVID-19 pandemic in Kazakhstan (June-2020), multiple SARS-CoV-2 PCR-test negative pneumonia cases with higher mortality were reported by media. We aimed to study the epidemiologic characteristics of hospitalized PCR-test positive and negative patients with analysis of in-hospital and post-hospital mortality. We also compare the respiratory disease characteristics between 2019 and 2020.

METHODS

The study population consist of 17,691 (March-July-2020) and 4600 (March-July-2019) hospitalized patients with respiratory diseases (including COVID-19). The incidence rate, case-fatality rate and survival analysis for overall mortality (in-hospital and post-hospital) were assessed.

RESULTS

The incidence and mortality rates for respiratory diseases were 4-fold and 11-fold higher in 2020 compared to 2019 (877.5 vs 228.2 and 11.2 vs 1.2 per 100,000 respectively). The PCR-positive cases (compared to PCR-negative) had 2-fold higher risk of overall mortality. We observed 24% higher risk of death in males compared to females and in older patients compared to younger ones. Patients residing in rural areas had 66% higher risk of death compared to city residents and being treated in a provisional hospital was associated with 1.9-fold increased mortality compared to those who were treated in infectious disease hospitals.

CONCLUSION

This is the first study from the Central Asia and Eurasia regions, evaluating the mortality of SARS-CoV-2 PCR-positive and PCR-negative respiratory system diseases during the peak of COVID-19 pandemic. We describe a higher mortality rate for PCR-test positive cases compared to PCR-test negative cases, for males compared to females, for elder patients compared to younger ones and for patients living in rural areas compared to city residents.

Post-COVID-19 pneumonia lung fibrosis: a worrisome sequelae in surviving patients

Background

Progressive fibrotic lung disease is one of the possible consequences of COVID-19 pulmonary pneumonia, and it is one of the most worrying long-term complications. Pulmonary fibrosis is associated with non-reversible lung dysfunction. The long-term lung changes of previous COVID-19 infection still not completely understood and should be included in further studies. The aim of this study is the early detection and prediction of patients whom may develop such serious complication, thus giving a chance for early introduction of anti-fibrotic drugs.

Results

From April 2020 to December 2020, 80 patients in Cairo, Egypt, who have clinical manifestations and confirmed COVID-19 by PCR, were evaluated by follow-up MDCT. CT image analysis was processed including comparative study using follow-up data (different radiological signs and residual fibrotic changes). Although there was no specific cause for post-COVID-19 lung fibrosis, there were some predicting factors such as old age, cigarette smoking, high CT severity score, and long-term mechanical ventilation.

Conclusion

Early detection of potential cases of post-COVID-19 pulmonary fibrosis may give a chance to prevent or at least modify such disabling complication.

Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol

INTRODUCTION

There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time.

METHODS AND ANALYSIS

We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients.

ETHICS AND DISSEMINATION

Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790.

The mean severity score and its correlation with common computed tomography chest manifestations in Egyptian patients with COVID-2019 pneumonia

Background

Computed tomography (CT) is one of the main diagnostic tools for early detection and management of coronavirus disease 2019 (COVID-19) pneumonia. This study aims to highlight the commonly encountered CT findings in patients with COVID-19 pneumonia in Egypt and the mean severity score and its correlation with the imaging findings. This study involved 200 patients with pathologically confirmed COVID-19 infection; non-contrast CT chest was performed for all cases; in addition, CT findings and severity score (CT-SS) were then assessed using descriptive analysis, and the correlation between the CT findings and disease severity was assessed.

Results

The ground-glass densities and peripheral adhesions were the most typical CT findings. Prominent interlobular septations; bronchial thickening/dilatation; CT signs of crazy-paving, halo, and reversed halo; and reactive mediastinal lymphadenopathy were significantly correlated with disease severity. The mean CT-SS of Egyptian patients with COVID-19 pneumonia was 11.2 (mild to moderate severity).

Conclusion

Multislice CT played a vital role in the early identification of Egyptian patients with COVID-19 pneumonia. The assessment of the CT severity score of COVID-19 is essential for the extent of pneumonia involvement to help clinicians achieve the purpose of early diagnosis and accurate treatment.

Long-Term Follow-Up Outcomes after Percutaneous US/CT-Guided Radiofrequency Ablation for cT1a-b Renal Masses: Experience from Single High-Volume Referral Center

Image-guided thermal ablations are increasingly applied in the treatment of renal cancers, under the guidance of ultrasound (US) or computed tomography (CT). Sometimes, multiple ablations are needed. The aim of the present study was to evaluate the long-term results in patients with renal mass treated with radiofrequency ablation (RFA) with both US and CT, with a focus on the multiple ablations rate. 149 patients (median age 67 years) underwent RFA from January 2008 to June 2015. Median tumor diameter was 25 mm (IQR 17–32 mm). Median follow-up was 54 months (IQR 44–68). 27 (18.1%) patients received multiple successful ablations, due to incomplete ablation (10 patients), local tumor progression (8 patients), distant tumor progression (4 patients) or multiple tumor foci (5 patients), with a primary and secondary technical efficacy of 100%. Complications occurred in 13 (8.7%) patients (6 grade A, 5 grade C, 2 grade D). 24 patients died during follow-up, all for causes unrelated to renal cancer. In conclusion, thermal ablations with the guidance of US and CT are safe and effective in the treatment of renal tumors in the long-term period, with a low rate of patients requiring multiple treatments over the course of their disease.