Quantitative chest CT assessment of pulmonary alveolar proteinosis with deep learning: a real-world longitudinal study.

ABSTRACT:

Background

High-resolution computed tomography (HRCT) plays an important role in accessing the severity of pulmonary alveolar proteinosis (PAP). Visual evaluation of changes between two HRCT scans is subjective. This study was conducted to quantitatively evaluate lung burden changes in patients with PAP using HRCT-based automated deep-learning method following 12 months of statin therapy.

Methods

In this prospective real-world observational study, patients with PAP who underwent chest HRCT were evaluated from November 28, 2018, to April 12, 2021. Oral statin administration was initiated as therapy for these PAP patients with 12 months of follow-up. HRCT-derived lung ground-glass opacification percentage of the whole lung and 5 lobes and the percentage of different densities

SUBMITTER: Shi S 

PROVIDER: S-EPMC9703118 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature