Chest Radiograph (CXR) Manifestations of the Novel Coronavirus Disease 2019 (Covid-19) - A Mini-Review

Background Coronavirus disease 2019 (COVID-19) is highly contagious and has claimed more than one million lives, besides causing hardship and disruptions. The Fleischner Society has recommended chest X-ray (CXR) in detecting cases with high risk for disease progression, for triaging suspected patients with moderate-to-severe illness, and to eliminate false negatives in areas with high pre-test probability or limited resources. Although CXR is less sensitive than real-time reverse transcription polymerase chain reaction (RT-PCR) in detecting mild COVID-19, it is nevertheless useful because of equipment portability, low cost and practicality in serial assessments of disease progression among hospitalized patients. Objective This study aims to review the typical and relatively atypical CXR manifestations of COVID-19 pneumonia in a tertiary care hospital. Methods The CXRs of 136 COVID-19 patients confirmed through real-time RT-PCR from March to May 2020 were reviewed. Literature search was performed using PubMed. Results A total of 54 patients had abnormal CXR whilst the others were normal. Typical CXR findings included pulmonary consolidation or ground-glass opacities in a multifocal, bilateral peripheral or lower zone distribution, whereas atypical CXR features comprised cavitation and pleural effusion. Conclusion Typical findings of COVID-19 infection in chest computed tomography studies can also be seen in CXR. The presence of atypical features is associated with worse disease outcome. Recognition of these features on CXR will improve accuracy and speed of diagnosing COVID-19 patients

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

Abstract Objectives: To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). Materials and methods: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. Results: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). Conclusion: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients

Objectives : To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes. Methods : In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling. Results : The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively. Conclusion : 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features

Cerebral nocardiosis in an immunocompetent patient : a diagnostic dilemma

Nocardia are aerobic, partially acid-fast, branching filamentous Gram-positive bacilli, found in soil and decaying vegetables which are acquired by direct inoculation or inhalation. Nocardiosis generally affects the immunocompromised patients and has become a significant opportunistic infection as the number of immunocompromised individuals has grown worldwide. Nocardial cerebral abscesses are rare and account for about 1–2% of all cerebral abscesses. The insidious manifestations and paucity of clinical and laboratory signs of bacterial inflammation often prompt the diagnosis of neoplasia. Early biopsy of the lesion to achieve specific identification, anti-microbial sensitivity profiles and institution of appropriate treatment are important for positive outcome of nocardial infections. This is a case of a nocardial brain abscess in an immunocompetent patient which has posed a diagnostic dilemma as the causative agent was only managed to be isolated after multiple biopsies.5 page(s

CFD modeling of turbulent convection heat transfer of nanofluids containing green functionalized graphene nanoplatelets flowing in a horizontal tube: Comparison with experimental data

In this research, a series of numerical simulations were conducted utilizing computational fluid dynamics (CFD) software in order to predict the heat transfer performance of queues nanofluids containing clove-treated graphene nanoplatelets (CGNPs) flowing in a horizontal stainless steel heated pipe. The GNPs were covalently functionalized with clove buds using free radical grafting reaction using an eco-friendly process. The advantage of this synthesis method was that it did not use hazardous acids, which are typically used in traditional treatment methods of carbon nanostructures. The thermo-physical properties of the aqueous nanofluids obtained experimentally were used as inputs for the CFD simulations for solving the governing equations of heat transfer and fluid motion. The shear stress transport (SST) k-ω turbulence model was also used in these simulations. The corresponding convective heat transfer coefficient and friction factor of aqueous nanofluids for nanoparticle weight concentrations of 0.025, 0.075, and 0.1% were evaluated. The simulation results for both heat transfer coefficient and friction factor were shown to be in agreement with the experimental data with an average relative deviation of about ±10%. The presented results confirmed the applicability of the numerical model for simulating the heat transfer performance of CGNPs aqueous nanofluids in turbulent flow regimes

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required

Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features

Background and objective: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images. Methods: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis. Results: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis. Conclusions: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis