Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.

ABSTRACT: OBJECTIVE:The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. MATERIALS AND METHODS:A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from -1000 HU to -700 HU. Spearman's correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was

SUBMITTER: Nishio M 

PROVIDER: S-EPMC5444793 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature