Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns

TL;DR

This study demonstrates that a texture-based quantitative CT model can improve diagnosis and prognosis prediction in fibrosing interstitial lung disease, outperforming traditional histogram methods and aiding in patient management.

Contribution

The paper introduces a novel lattice-based texture model (TM) that enhances classification and prognostic accuracy over existing histogram-based approaches in ILD.

Findings

TM model achieved AUC of 0.70 in classification.

TM features significantly partitioned survival outcomes.

Expert labels did not significantly differentiate survival groups.

Abstract

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.