Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis

Authors
Ju Gang Nam, Yunhee Choi, Sang-Min Lee, Soon Ho Yoon, Jin Mo Goo, Hyungjin Kim
Journal
European Radiology
Related Product

Lung Texture

Date Published
2023.02
Summary

This study investigated the prognostic value of deep learning (DL)-driven CT fibrosis quantification (using Coreline Soft AVIEW Lung Texture) in idiopathic pulmonary fibrosis (IPF). A total of 161 patients were evaluated, and CT-Norm% and CT-Fib% demonstrated significant correlations with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (DLCO). Both CT-Norm% and CT-Fib% were found to be independent prognostic factors for overall survival in IPF when adjusted for various factors. The study concluded that CT-Norm% and CT-Fib% calculated using chest CT-based deep learning software could serve as reliable prognostic factors for IPF patients.

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