  Purpose: To utilize high-resolution quantitative CT (QCT) imaging features
for prediction of diagnosis and prognosis in fibrosing interstitial lung
diseases (ILD). Approach: 40 ILD patients (20 usual interstitial pneumonia
(UIP), 20 non-UIP pattern ILD) were classified by expert consensus of 2
radiologists and followed for 7 years. Clinical variables were recorded.
Following segmentation of the lung field, a total of 26 texture features were
extracted using a lattice-based approach (TM model). The TM model was compared
with previously histogram-based model (HM) for their abilities to classify UIP
vs non-UIP. For prognostic assessment, survival analysis was performed
comparing the expert diagnostic labels versus TM metrics. Results: In the
classification analysis, the TM model outperformed the HM method with AUC of
0.70. While survival curves of UIP vs non-UIP expert labels in Cox regression
analysis were not statistically different, TM QCT features allowed
statistically significant partition of the cohort. Conclusions: TM model
outperformed HM model in distinguishing UIP from non-UIP patterns. Most
importantly, TM allows for partitioning of the cohort into distinct survival
groups, whereas expert UIP vs non-UIP labeling does not. QCT TM models may
improve diagnosis of ILD and offer more accurate prognostication, better
guiding patient management.
