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

Ju Gang Nam, Yunhee Choi, Sang-Min Lee, Soon Ho Yoon, Jin Mo Goo, Hyungjin Kim
European Radiology
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Lung Texture

Date Published

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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