Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.

Purpose: To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans.

Methods: Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building ( = 73), and another for model validation ( = 55).

Ultrasound imaging in an experimental model of fatty liver disease and cirrhosis in rats.

Background: Domestic dogs and cats are very well known to develop chronic hepatic diseases, including hepatic lipidosis and cirrhosis. Ultrasonographic examination is extensively used to detect them. However, there are still few reports on the use of the ultrasound B-mode scan in correlation with histological findings to evaluate diffuse hepatic changes in rodents, which represent the most important animal group used in experimental models of liver diseases.