Feature-based Quantification of Fibrotic Lesions in CT Imaging Using Homology Profiles

Overview

This study explores the use of homology-based features (HFs) derived from high-resolution CT images to quantify fibrotic lesions in idiopathic interstitial pneumonias (IIPs). By applying the homology-profile (HP) method to CT images from COVID-19 patients, the research demonstrates a novel approach to characterize fibrosis patterns that may assist in diagnosis and classification.

Background

Idiopathic interstitial pneumonia (IIP) encompasses several fatal lung diseases with varying prognoses and treatment strategies, notably idiopathic pulmonary fibrosis (IPF) which has a poor outcome. High-resolution computed tomography (HRCT) is essential for diagnosing IIPs but is limited by inter-observer variability and difficulty in accurately classifying disease categories. Homology-based image analysis, specifically the HP method calculating Betti numbers (b0 and b1), offers a way to quantify topological features such as isolated components and holes in binary images, potentially capturing fibrotic lesion heterogeneity in CT scans.

Data Highlights

Eighteen CT datasets from COVID-19 patients and twenty lung cancer radiotherapy CT datasets were analyzed. Forty-seven CT images with clear fibrotic lesions were selected from COVID-19 cases. Thirty-six CT images without lung cancer lesions or abnormalities were selected from lung cancer patients for comparison. The HP method was applied to 32 × 32 pixel tiles shifted every 8 pixels across 512 × 512 CT images. Threshold values for binarization ranged from -700 to -400 Hounsfield units (HU).

Key Findings

• Homology-based features (HFs), calculated via Betti numbers b0 and b1, effectively quantify fibrotic lesion connectivity and morphology in CT images.
• The HP method applied to COVID-19 CT images successfully identified fibrotic lesions analogous to those seen in IIPs.
• Tile-based analysis with shifting and thresholding allowed detailed mapping of fibrotic patterns across lung fields.
• HFs provide a topologically invariant morphological characterization, potentially reducing inter-observer variability in fibrosis assessment.
• The approach differentiates fibrotic lesions from non-fibrotic lung abnormalities and lung cancer-related changes.

Clinical Implications

The homology-profile method offers a promising quantitative tool to assist radiologists in diagnosing and classifying fibrotic lung diseases by objectively characterizing fibrotic lesion patterns on HRCT. This technique may improve diagnostic accuracy and prognostic prediction in IIPs, including IPF, and could be adapted to other fibrotic lung conditions such as post-COVID-19 fibrosis. Integration of HFs into clinical workflows may reduce variability and enhance treatment decision-making.

Conclusion

This conceptual study demonstrates that homology-based features derived from CT imaging can effectively quantify fibrotic lesions, providing a novel and objective metric to support diagnosis and classification of idiopathic interstitial pneumonias. Further validation may establish this method as a valuable adjunct in clinical practice.

References

1. Nakane et al. 2019 -- Homology-profile method for tumor cell detection
2. Ninomiya and Arimura 2020 -- Prognostic prediction of lung cancer using homology-based features
3. Cancer Image Archive 2021 -- CT datasets for COVID-19 and lung cancer