Objective:

To explore the potential of homology-based features (HFs) in quantifying fibrotic lesions in HRCT images of idiopathic interstitial pneumonia (IIP), highlighting their significance in improving diagnostic accuracy.

Key Findings:

• Homology-based features effectively captured fibrotic lesions in CT images, which may lead to improved diagnostic strategies.
• The HP method demonstrated potential in distinguishing fibrotic from non-fibrotic lesions, suggesting its utility in clinical settings.
• Quantification of fibrotic lesions may assist in the diagnosis and classification of IIPs, potentially impacting patient management.

Interpretation:

The study suggests that HFs can enhance the diagnostic accuracy of HRCT images for IIPs by providing a quantitative assessment of fibrotic lesions, thereby improving clinical decision-making.

Limitations:

• The study is based on a limited dataset of COVID-19 and lung cancer CT images, which may not represent the broader population.
• Potential inter-observer variability in image interpretation remains a challenge, which could affect the reliability of the findings.
• Further validation with larger datasets is necessary to confirm findings and address potential biases in data selection.

Conclusion:

The proof of concept indicates that homology-based quantification may improve the assessment of fibrotic lesions in HRCT images, aiding in the diagnosis of IIPs and potentially enhancing patient outcomes.