METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.
METHODS: Six hemi-mandible samples were scanned using the i-CAT CBCT system. The scanned data was transferred to the OsiriX software for measurement protocol and subsequently into Mimics software to fabricate customized cutting jigs and 3D biomodels based on rapid prototyping technology. The hemi-mandibles were segmented into 5 dentoalveolar blocks using the customized jigs. Digital calliper was used to measure six distances surrounding the mandibular canal on each section. The same distances were measured on the corresponding cross-sectional OsiriX images and the 3D biomodels of each dentoalveolar block.
RESULTS: Statistically no significant difference was found when measurements from OsiriX images and 3D biomodels were compared to the "gold standard" -direct digital calliper measurement of the cadaveric dentoalveolar blocks. Moreover, the mean value difference of the various measurements between the different study components was also minimal.
CONCLUSION: Various distances surrounding the mandibular canal from 3D biomodels produced from the CBCT scanned data was similar to that of direct digital calliper measurements of the cadaveric specimens.
METHODS: Retrospective review of 119 consecutive paediatric patients referred for 18F-FDG-PET/CT at the Department of Nuclear Medicine of the National Cancer Institute, Putrajaya. All had DRE and underwent evaluation at the Paediatric Institute, Hospital Kuala Lumpur. Visually detected areas of 18F-FDG-PET/CT hypometabolism were correlated with clinical, MRI and VEM findings.
RESULTS: Hypometabolism was detected in 102/119 (86%) 18FFDG- PET/CT scans. The pattern of hypometabolism in 73 patients with normal MRI was focal unilobar in 16/73 (22%), multilobar unilateral in 8/73 (11%), bilateral in 27/73 (37%) and global in 5/73 (7%) of patients; whilst 17/73 (23%) showed normal metabolism. In 46 patients with lesions on MRI, 18F-FDG-PET/CT showed concordant localisation and lateralization of the EF in 30/46 (65%) patients, and bilateral or widespread hypometabolism in the rest. Addition of 18FFDG PET/CT impacted decision making in 66/119 (55%) of patients; 24/73 with non-lesional and 30/46 patients with lesional epilepsies were recommended for surgery or further surgical work up, whilst surgery was not recommended in 11/46 patients with lesional epilepsy due to bilateral or widespread hypometabolism. 25 patients subsequently underwent epilepsy surgery, with 16/25 becoming seizure free following surgery.
CONCLUSION: 18F-FDG-PET/CT has an added benefit for the localization and lateralization of EF, particularly in patients with normal or inconclusive MRI.
METHOD: From January 2013 to December 2015, patients aged 6 months and below with duct-dependent pulmonary circulation underwent CT angiography to delineate the ductus arteriosus origin, tortuosity, site of insertion, and pulmonary artery anatomy. The ductus arteriosus were classified into type I, IIa, IIb, and III based on its site of origin, either from descending aorta, distal arch, proximal arch, or subclavian artery, respectively.
RESULTS: A total of 114 patients and 116 ductus arteriosus (two had bilateral ductus arteriosus) were analysed. Type I, IIa, IIb, and III ductus arteriosus were seen in 13 (11.2 %), 71 (61.2%), 21 (18.1%), and 11 (9.5%), respectively. Tortuous ductus arteriosus was found in 38 (32.7%), which was commonly seen in single ventricular lesions. Ipsilateral and bilateral branch pulmonary artery stenosis was seen in 68 (59.6%) and 6 (5.3%) patients, respectively. The majority of patients with pulmonary atresia intact ventricular septum had type I (54.4%) and non-tortuous ductus arteriosus, while those with single and biventricular lesions had type II ductus arteriosus (84.9% and 89.7%, respectively). Type III ductus arteriosus was more common in biventricular lesions (77.8%).
CONCLUSIONS: Ductus arteriosus in duct-dependent pulmonary circulation has a diverse morphology with a distinct origin and tortuosity pattern in different types of ventricular morphology. CT may serve as an important tool in case selection and pre-procedural planning for ductal stenting.