Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis

Objectives To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). Methods A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were inclu...

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Bibliographic Details
Published in:European radiology 2018-03, Vol.28 (3), p.1293-1300
Main Authors: Lee, Sang Min, Seo, Joon Beom, Oh, Sang Young, Kim, Tae Hoon, Song, Jin Woo, Kim, Namkug
Format: Article
Language:English
Subjects:
Adult
Aged
Automation
Carbon monoxide
Chest
Computed tomography
Desaturation
Diagnostic Radiology
Female
Fibrosis
Follow-Up Studies
Humans
Idiopathic Pulmonary Fibrosis - diagnosis
Idiopathic Pulmonary Fibrosis - mortality
Idiopathic Pulmonary Fibrosis - physiopathology
Imaging
Internal Medicine
Interventional Radiology
Lung - diagnostic imaging
Lung - physiopathology
Lung diseases
Male
Mathematical models
Medical prognosis
Medicine
Medicine & Public Health
Middle Aged
Multivariate analysis
Neuroradiology
Opacity
Patients
Positron Emission Tomography Computed Tomography - methods
Prediction models
Predictive Value of Tests
Prognosis
Pulmonary fibrosis
Radiology
Regional analysis
Republic of Korea - epidemiology
Retrospective Studies
Survival
Survival Rate - trends
Texture
Ultrasound
Vital Capacity - physiology
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Summary:Objectives To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). Methods A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity. The Cox proportional hazard model was used to determine the independent predictors of survival. Results A total of 106 patients (73.6%) died during the follow-up period. Univariate analysis revealed that age, baseline forced vital capacity, total lung capacity, diffusing capacity of the lung for carbon monoxide, six-minute walk distance, desaturation , honeycombing, reticular opacity, fibrosis score, and interval changes in honeycombing and fibrosis score were significantly associated with survival. Multivariate analysis revealed that age, desaturation, fibrosis score and interval change in fibrosis score were significant independent predictors of survival ( p = 0.003,
ISSN:0938-7994
1432-1084
DOI:10.1007/s00330-017-5028-0