Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989064PMC
http://dx.doi.org/10.1148/radiol.219029DOI Listing

Publication Analysis

Top Keywords

chest radiograph
4
radiograph scoring
4
scoring combined
4
combined risk
4
risk scores
4
scores predicting
4
predicting outcomes
4
outcomes covid-19
4
chest
1
scoring
1

Similar Publications

Background: Distinguishing between benign and malignant pulmonary nodules based on CT imaging features such as the spiculation sign and/or lobulation sign remains challenging and these nodules are often misinterpreted as malignant tumors. this retrospective study aimed to develop a prediction model to estimate the likelihood of benign and malignant lung nodules exhibiting spiculation and/or lobulation signs.

Methods: A total of 500 patients with pulmonary nodules from June 2022 to August 2024 were retrospectively analyzed.

View Article and Find Full Text PDF

Cytomegalovirus (CMV) infection is one of the most common congenital infections. We present a case of an infant who presented with respiratory distress since birth with a normal antenatal history. The infant had bilateral pleural effusion.

View Article and Find Full Text PDF

Background: Iatrogenic pneumothorax is a common complication of diagnostic and therapeutic pulmonary procedures. New guidelines on primary spontaneous pneumothorax suggest ambulatory approaches may be suitable. However, guidance on iatrogenic pneumothorax occurring in patients with impaired lung function, increased age, comorbidity and frailty is lacking, and the safety profile of ambulatory management is not known.

View Article and Find Full Text PDF

Purpose: Deciding whether to provide preventive treatment to contacts of individuals with multidrug-resistant (MDR) tuberculosis is complex.

Methods: We present the diagnostic pathways, clinical course and outcome of tuberculosis treatment in eight siblings from a single family. Tuberculosis disease was diagnosed by Mycobacterium tuberculosis culture and molecular detection of M.

View Article and Find Full Text PDF

Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!