Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients

Abstract

Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.

Methods: We evaluated the potential of an automated ILD quantification system (icolung^®) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time.

Results: We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively).

Conclusion: AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.

Keywords: Artificial intelligence; Computed tomography; Interstitial lung disease; Pulmonary function tests; Systemic sclerosis.

Fig. 1

A) Icolung software output combining the overall automatized lung segmentation (TLV quantification) and the association with lobar abnormalities. 3D analysis of parenchymal lung abnormalities: Coronal view illustrating automated lung segmentation and the visualization of abnormalities. The abnormalities are quantified using Icolung following lobe segmentation and represented with a conventional coronal view. The severity score is based on a five lung lobes scoring on a scale of 0 to 5, with 0 indicating no involvement (< 1%); 1, less than 5% involvement; 2, 5-25% involvement; 3, 26-49% involvement; 4, 50–75% involvement; and 5, more than 75% involvement. The total severity score is the sum of the individual lobar scores and range from 0 (no involvement) to 25 (maximum involvement). (B) Screenshots of the Icolung analysis report from baseline (left) and follow up scan (right) of SSc female patient, SCL-70 + treated with MMF exhibiting a PPF phenotype. FVC and DLCO (in % predicted) were 103% and 60% at baseline versus 60% and 28% at follow-up respectively. The 3D segmentation masks of the abnormalities are visualized in 2D axial and coronal views (red = consolidation, yellow = ground glass opacities)

Fig. 2

Correlation between IA quantification of lesions and functional parameters. Correlation between percentage of lesions quantified out of the TLV from the AI algorithm with FVC (A) and DLCO (B)

Fig. 3

Comparison between functional and imaging biomarkers in SSc-ILD and SSc-PFILD. Difference in FVC (A), TLV% (B), DLCO (C) and Severity score (D) for patients with SSc-ILD (blue bar) and SSc-PFILD (green bar). Data was analyzed by unpaired T test for FVC and DLCO and by Mann Whitney test for TLV and Severity score. *p value < 0.05

Fig. 4

ILD quantification compared to PFT modifications over time. A.B.C. Variation over time of FVC (A), DLCO (B) and TLV % (C). Data was analyzed by the paired T test for FVC and DLCO and by the Wilcoxon matched-pairs signed rank test for TLV %. T1 and T2 represent the two different timepoints for CT-scan analysis *P value < 0.05 ;**P value < 0.01. D.E. Correlation between the variation over the time of FVC (D) and DLCO (n = 48)(E) correlated with ILD progression over time out of the total lung volume (TLV %)