A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients.

ABSTRACT:

Purpose

It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.

Methods

Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute s

SUBMITTER: Zhou T 

PROVIDER: S-EPMC11233899 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature