Radiographic and Clinical Analysis of Cranio-Maxillofacial Complications of Cavernous Sinus Thrombosis Among 256 COVID-19 Patients.

BACKGROUND: A heavy burden of cranio-maxillofacial complications may be encountered in corona virus disease-2019 patients due to the associated coagulopathy and inflammatory consequences of the disease. This study aims to describe clinical and radiographic features of these complications in 256 patients who developed 1 or more of the following complications: cavernous sinus thrombosis, osteomyelitis or necrosis of the jaws. METHODS: Clinical assessment of cranial nerve function and general clinical assessment were performed. Imaging techniques used were multi-slice computed tomography, magnetic resonance imaging, and MRI with contrast enhancement. RESULTS: Thromboembolism of brain and facial blood vessels were associated with inflammation and necrosis. Multi-slice computed tomography/MR angiography showed thrombotic occlusions of the internal carotid artery in the area of the cavernous sinus, and in the ophthalmic veins. Cavernous sinus thrombosis was attributed to coagulopathy and, inflammation of the paranasal sinuses, especially sphenoiditis. A noticeable increase in the size of the cavernous sinus was detected. Compression of the cranial nerves in the cavernous sinus (CS) region causes dysfunction and pathology in the corresponding regions.

A lexicon for hepatocellular carcinoma surveillance ultrasonography: benign versus malignant lesions

BACKGROUND/AIMS: To suggest a lexicon for liver ultrasonography and to identify radiologic features indicative of benign or malignant lesions on surveillance ultrasonography. METHODS: This retrospective study included 188 nodules (benign, 101; malignant, 87) from 175 at-risk patients identified during surveillance ultrasonography for hepatocellular carcinoma. We created a lexicon for liver ultrasonography by reviewing relevant literature regarding the ultrasonographic features of hepatic lesions. Using this lexicon, two abdominal radiologists determined the presence or absence of each ultrasonographic feature for the included hepatic lesions. Independent factors associated with malignancy and interobserver agreement were determined by logistic regression analysis and kappa statistics, respectively. RESULTS: Larger tumor size (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.183; P<0.001), multinodular confluent morphology (OR, 7.712; 95% CI, 1.053-56.465; P=0.044), thick hypoechoic rim (OR, 5.878; 95% CI, 2.681-12.888; P<0.001), and posterior acoustic enhancement (OR, 3.077; 95% CI, 1.237-7.655; P=0.016) were independently associated with malignant lesions. In a subgroup analysis of lesions <2 cm, none of the ultrasonographic features were significantly associated with malignancy or benignity. Interobserver agreement for morphology was fair (κ=0.36), while those for rim (κ=0.427), echogenicity (κ=0.549), and posterior acoustic enhancement (κ=0.543) were moderate. CONCLUSIONS: For hepatic lesions larger than 2 cm, some ultrasonography (US) features might be suggestive of malignancy. We propose a lexicon that may be useful for surveillance US.

Liver imaging reporting and data system (LI-RADS) version 2014: understanding and application of the diagnostic algorithm

Liver Imaging Reporting and Data System (LI-RADS) is a system for interpreting and reporting of computed tomography and magnetic resonance imaging of the liver in patients at risk for hepatocellular carcinoma (HCC). LI-RADS has been developed to address the limitations of prior imaging-based criteria including the lack of established consensus regarding the exact definitions of imaging features, binary categorization (either definite or not definite HCC), and failure to consider non-HCC malignancies. One of the most important goals of LI-RADS is to facilitate clear communication between all the personnel involved in the diagnosis and treatment of HCC, such as radiologists, hepatologists, surgeons, and pathologists. Therefore, clinicians should also be familiar with LI-RADS. This article reviews the LI-RADS diagnostic algorithm, and the definitions and management implications of LI-RADS categories.