European Radiology

, Volume 28, Issue 3, pp 1293–1300 | Cite as

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

  • Sang Min Lee
  • Joon Beom SeoEmail author
  • Sang Young Oh
  • Tae Hoon Kim
  • Jin Woo Song
  • Sang Min Lee
  • Namkug Kim



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).


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.


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, <0.001, 0.001 and <0.001). The C-index for the developed model was 0.768.


Texture-based, automated CT quantification of fibrosis can be used as an independent predictor of survival in IPF patients.

Key Points

Automated quantified fibrosis on CT was a significant predictor of survival.

Automated quantified interval change in fibrosis on CT was an independent predictor.

The predictive model showed comparable discriminative power with a C-index of 0.768.

Automated CT quantification can be considered to evaluate prognosis in routine practice.


Idiopathic pulmonary fibrosis CT Quantification Texture analysis Survival 



six-minute walk distance




diffusing capacity of the lung for carbon monoxide




forced vital capacity


gender, age, and physiology


ground-glass opacity




idiopathic pulmonary fibrosis




pulmonary function test


reticular opacity


region of interest


oxyhaemoglobin saturation


total lung capacity


Compliance with ethical standards


The scientific guarantor of this publication is Dr. Joon Beom Seo.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.


This study was supported by a grant (2016-7014) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

The 144 patients of our study were identical to the study population in the previous report [21]. While the previous study assessed predictive factors for decline in FVC on initial CT using texture-based automated quantification in IPF, this work focused on whether baseline extent and 1-year change in regional disease patterns on CT can be used to predict survival of patients with IPF. Thus, this work is substantially different from the previous report.


• retrospective

• observational

• performed at one institution


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Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Sang Min Lee
    • 1
  • Joon Beom Seo
    • 1
    Email author
  • Sang Young Oh
    • 1
  • Tae Hoon Kim
    • 2
  • Jin Woo Song
    • 2
  • Sang Min Lee
    • 1
  • Namkug Kim
    • 1
  1. 1.Department of Radiology and Research Institute of RadiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea
  2. 2.Department of Pulmonary and Critical Care MedicineUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea

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