Co-occurrence of bronchiectasis, airway wall thickening, and emphysema in Chinese low-dose CT screening.

Objective: To assess the co-occurrence of incidental CT lung findings (emphysema, bronchiectasis, and airway wall thickening) as well as associated risk factors in low-dose CT (LDCT) lung cancer screening in a Chinese urban population.

Methods: Data from 978 participants aged 40-74 years from the Chinese NELCIN-B3 urban population study who underwent LDCT screening were selected. CT scans were reviewed for incidental lung findings: emphysema, bronchiectasis and airway wall thickness.

Ct-defined emphysema prevalence in a Chinese and Dutch general population.

Purpose: We determine and compare the prevalence, subtypes, severity, and risk factors for emphysema assessed by low-dose CT(LDCT) in Chinese and Dutch general populations.

Methods: This cross-sectional study included LDCT scans of 1143 participants between May and October 2017 from a Chinese Cohort study and 1200 participants with same age range and different smoking status between May and October 2019 from a Dutch population-based study. An experienced radiologist visually assessed the scans for emphysema presence (≥trace), subtype, and severity.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening.

Objectives: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density.

Methods: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D).

Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability.

Background: Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability.

Purpose: To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation.

Study Type: Retrospective.

Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks.

Objectives: To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI.

Materials And Methods: A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women).