Association of artificial intelligence-based high-resolution computed tomography parameters with all-cause mortality in patients with connective tissue disease-associated interstitial lung disease: a longitudinal cohort study

Abstract

Background: The severity of interstitial lung disease (ILD) is frequently linked to poorer outcomes and reduced quality of life in connective tissue disease-associated ILD (CTD-ILD) patients. The purpose of this study is to investigate the utility of artificial intelligence (AI)-based quantitative high-resolution computed tomography (HRCT) analysis in assessing the prognosis of patients with CTD-ILD.

Methods: This retrospective study included 116 CTD-ILD patients who underwent HRCT scans. Patients were stratified into mild, moderate, and severe groups based on pulmonary function test (PFT) results. Differences in the 17 AI parameters across the three groups were evaluated using one-way analysis of variance (ANOVA) followed by least significant difference (LSD) post hoc pairwise comparisons. The Spearman rank correlation test was employed to examine the association between the 17 AI parameters and pulmonary function grades. Overall survival rates were compared using Kaplan-Meier analysis and ANOVA. Univariate analysis and Cox proportional-hazards regression were used to assess the association between AI-derived parameters and prognosis.

Results: The 17 AI parameters exhibited significant variations across the three groups. Among them, three pulmonary volume parameters were negatively correlated with lung function, while fourteen pulmonary parenchymal involvement parameters were positively correlated with pulmonary function. Significant differences in overall survival rates were observed among the mild, moderate, and severe groups. Univariate analysis revealed that there were 12 indicators showing significant differences between the survival group and the death group. The Cox proportional-hazards regression revealed that the two most important factors were left lung volume and the percentage of disease components ≤-751 Hounsfield units (HU), which were protective factors and risk factors for overall survival rate, respectively.

Conclusions: The quantitative HRCT analysis based on AI can be used to evaluate patients with CTD-ILD and is correlated with overall survival rate.

Keywords: Interstitial lung disease (ILD); artificial intelligence (AI); connective tissue disease (CTD); high-resolution computed tomography (HRCT).

Figure 1

The flowchart of this research. AI, artificial intelligence; CTD-ILD, connective tissue disease-associated interstitial lung disease; HRCT, high-resolution computed tomography; ILD, interstitial lung disease; PFT, pulmonary function test.

Figure 2

Representative AI images. A 75-year-old male patient with CTD-ILD. (A,B) Two HRCT slices, with the red regions indicating lung lesions automatically identified by AI. (C) A histogram of CT values from the lung lesions. AI, artificial intelligence; CT, computed tomography; CTD-ILD, connective tissue disease-associated interstitial lung disease; HRCT, high-resolution computed tomography; HU, Hounsfield units.

Figure 3

Overall survival function curves of the mild, moderate, and severe groups. The blue line represents the mild group, the red line represents the moderate group, and green line represents the severe group.