A computer-aided diagnosis system for quantitative scoring of extent of lung fibrosis in scleroderma patients.

OBJECTIVES: To evaluate an improved quantitative lung fibrosis score based on a computer-aided diagnosis (CAD) system that classifies CT pixels with the visual semi-quantitative pulmonary fibrosis score in patients with scleroderma-related interstitial lung disease (SSc-ILD).
METHODS: High-resolution, thin-section CT images were obtained and analysed on 129 subjects with SSc-ILD (36 men, 93 women; mean age 48.8±12.1 years) who underwent baseline CT in the prone position at full inspiration. The CAD system segmented each lung of each patient into 3 zones. A quantitative lung fibrosis (QLF) score was established via 5 steps: 1) images were denoised; 2) images were grid sampled; 3) the characteristics of grid intensities were converted into texture features; 4) texture features classified pixels as fibrotic or non-fibrotic, with fibrosis defined by a reticular pattern with architectural distortion; and 5) fibrotic pixels were reported as percentages. Quantitative scores were obtained from 709 zones with complete data and then compared with ordinal scores from two independent expert radiologists. ROC curve analyses were used to measure performance.
RESULTS: When the two radiologists agreed that fibrosis affected more than 1% or 25% of a zone or zones, the areas under the ROC curves for QLF score were 0.86 and 0.96, respectively.
CONCLUSIONS: Our technique exhibited good accuracy for detecting fibrosis at a threshold of both 1% (i.e. presence or absence of pulmonary fibrosis) and a clinically meaningful threshold of 25% extent of fibrosis in patients with SSc-ILD.

RCT Entities:   Population Interventions Outcomes