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. A 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.

METHODS: A total of 224 patients were recruited. An optimised CT protocol was applied using 100 kV and 1 mL/kg of contrast media dosing. The image quality and radiation dose exposure of this CT protocol were compared to those of a standard 120 kV, 80 mL fixed volume protocol. The radiation dose information and CT Hounsfield units were recorded. The signal-to-noise ratio, contrast-to-noise ratio (CNR) and figure of merit (FOM) were used as comparison metrics. The images were assessed for contrast opacification and visual quality by two radiologists. The renal function, contrast media volume and cost were also evaluated.

RESULTS: The median effective dose was lowered by 16% in the optimised protocol, while the arterial phase images achieved significantly higher CNR and FOM. The radiologists' evaluation showed more than 97% absolute agreement with no significant differences in image quality. No significant differences were found in the pre- and post-CT estimated glomerular filtration rate. However, contrast media usage was significantly reduced by 1,680 mL, with an overall cost savings of USD 421 in the optimised protocol.

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.