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

CONCLUSION

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.