# Homology-feature-assisted quantification of fibrotic lesions in computed tomography images: a proof of concept for CT image feature-based prediction for gene-expression-distribution

## TL;DR

This paper introduces a new method using homology-based image analysis to quantify fibrotic lesions in CT scans, showing promising results for diagnosing interstitial idiopathic pneumonias.

## Contribution

The novelty lies in using homology-based features to quantify fibrotic lesions in CT images for diagnosing interstitial lung diseases.

## Key findings

- The b0 homology-based feature map was more effective than b1 for quantifying fibrotic lesions.
- The proposed method achieved perfect classification performance (1.0) for fibrotic and non-fibrotic images.
- The method also showed perfect performance in distinguishing fibrotic from lung cancer images.

## Abstract

Computed tomography (CT) image is promising for diagnosing of interstitial idiopathic pneumonias (IIPs); however, quantification of IIPs lesions in CT images is required. This study aimed to quantitatively evaluate fibrotic lesions in CT images using homology-based image analysis.

We collected publicly available CT images comprising 47 fibrotic images and 36 non-fibrotic images. The homology-profile (HP) image analysis method provides b0 and b1 profiles, indicating the number of isolated components and holes in a binary image. We locally applied the HP method to the CT image and generated homology-based feature (HF) maps as resultant images. The collected images were randomly divided into the tuning dataset and the testing dataset. The cut-off value for classifying the HF map for fibrotic or non-fibrotic images was defined using receiver operating characteristic (ROC) analysis with the tuning dataset. This cut-off value was evaluated using the testing dataset with accuracy, sensitivity, specificity, and precision.

We successfully visualized the quantification of fibrotic lesions in the HF map. The b0 HF map was more suitable for quantifying fibrotic lesions than b1. The mean cut-off value of the b0 HF map was 199, with all performances achieved at 1.0. Furthermore, the classification of the b0 HF map for fibrotic or lung cancer images achieved all maximum performances at 1.0.

This study demonstrated the feasibility of using the HF in quantitatively evaluating fibrotic lesions in CT images. Our proposed HP-based method can also be promising in quantifying the fibrotic lesions of patients with IIPs, which can be applicable to assist the diagnosis of IIPs.

The online version contains supplementary material available at 10.1007/s11548-025-03428-8.