Assessing the Severity of Connective Tissue-Related Interstitial Lung Disease Using Computed Tomography Quantitative...

The aims of the study are to predict lung function impairment in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) through computed tomography (CT) quantitative analysis parameters based on CT deep learning model and density threshold method and to assess the severity of the disease in patients with CTD-ILD.

We retrospectively collected chest high-resolution CT images and pulmonary function test results from 105 patients with CTD-ILD between January 2021 and December 2023 (patients staged according to the gender-age-physiology [GAP] system), including 46 males and 59 females, with a median age of 64 years. Additionally, we selected 80 healthy controls (HCs) with matched sex and age, who showed no abnormalities in their chest high-resolution CT. Based on our previously developed RDNet analysis model, the proportion of the lung occupied by reticulation, honeycombing, and total interstitial abnormalities in CTD-ILD patients (ILD% = total interstitial abnormal volume/total lung volume) were calculated. Using the Pulmo-3D software with a threshold segmentation method of -260 to -600, the overall interstitial abnormal proportion (AA%) and mean lung density were obtained. The correlations between CT quantitative analysis parameters and pulmonary function indices were evaluated using Spearman or Pearson correlation coefficients. Stepwise multiple linear regression analysis was used to identify the best CT quantitative predictors for different pulmonary function parameters. Independent risk factors for GAP staging were determined using multifactorial logistic regression. The area under the ROC curve (AUC) differentiated between the CTD-ILD groups and HCs, as well as among GAP stages. The Kruskal-Wallis test was used to compare the differences in pulmonary function indices and CT quantitative analysis parameters among CTD-ILD groups.

Among 105 CTD-ILD patients (58 in GAP I, 36 in GAP II, and 11 in GAP III), results indicated that AA% distinguished between CTD-ILD patients and HCs with the highest AUC value of 0.974 (95% confidence interval: 0.955-0.993). With a threshold set at 9.7%, a sensitivity of 98.7% and a specificity of 89.5% were observed. Both honeycombing and ILD% showed statistically significant correlations with pulmonary function parameters, with honeycombing displaying the highest correlation coefficient with Composite Physiologic Index (CPI, r = 0.612). Multiple linear regression results indicated honeycombing was the best predictor for both the Dlco% and the CPI. Furthermore, multivariable logistic regression analysis identified honeycombing as an independent risk factor for GAP staging. Honeycombing differentiated between GAP I and GAP II + III with the highest AUC value of 0.729 (95% confidence interval: 0.634-0.811). With a threshold set at 8.0%, a sensitivity of 79.3% and a specificity of 57.4% were observed. Significant differences in honeycombing and ILD% were also noted among the disease groups ( P  < 0.05).

An AA% of 9.7% was the optimal threshold for differentiating CTD-ILD patients from HCs. Honeycombing can preliminarily predict lung function impairment and was an independent risk factor for GAP staging, offering significant clinical guidance for assessing the severity of the patient's disease.