Publications by authors named "Roman Rubtsov"
Article Synopsis
- - The study aimed to evaluate tracheal collapsibility using low-dose 4D CT, comparing the results with traditional methods like inspiratory-expiratory CT and bronchoscopy in patients suspected of tracheal collapse.
- - Researchers analyzed 4D CT scans from 52 patients, finding that 48% exhibited significant tracheal collapsibility (50% or greater) and noted differences between visual 4D CT assessments and other diagnostic methods regarding detection rates of collapsibility.
- - Results showed that 4D CT was superior in identifying patients with severe collapsibility compared to both paired CT and bronchoscopy, with significant differences in collapsibility measurements between the groups assessed.
The "bullseye" sign has been exclusively reported in patients suffering from coronavirus disease 2019 (COVID-19) pneumonia. It is theorized that this newly recognized computed tomography (CT) feature represents a sign of organizing pneumonia. Well established signs of organizing pneumonia also reported in COVID-19 patients include linear opacities, the "reversed halo" sign (or "atoll" sign), and a perilobular distribution of abnormalities.
View Article and Find Full Text PDFIntroduction: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity.
Methods: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.