Objectives: This study was designed (a) to evaluate an improved quantitative lung fibrosis score based on a computer-aided diagnosis (CaM) system in patients with systemic sclerosis (SSc),-related interstitial lung disease (SSc-ILD), (b) to investigate the relationship between physiologic parameters (forced vital capacity [FVC] and single-breath diffusing capacity for carbon monoxide [DLCO]), patient-centred measures of dyspnea and functional disability and CaM and visual reader-based (CoVR) methods, and (c) to identify potential surrogate measures from quantitative and visual HRCT measurement.
Methods: 126 patients with SSc underwent chest radiography, HRCT and PFTs. The following patient-centred measures were obtained: modified Borg Dyspnea Index (Borg score), VAS for breathing, and Health Assessment Questionnaire-Disability Index (HAQ-DI). HRCT abnormalities were scored according to the conventional visual reader-based score (CoVR) and by a CaM. The relationships among the HRCT scores, physiologic parameters (FVC and DLCO, % predicted) results and patient-centred measures, were calculated using linear regression analysis and Pearson's correlation. Multivariate regression models were performed to identify the predictor variables on severity of pulmonary fibrosis.
Results: Subjects with limited cutaneous SSc had lower HAQ-DI scores than subjects with diffuse cutaneous SSc (p <0.001). CaM and CoVR scores were similar in the 2 groups. In univariate analysis, a strong correlation between CaM and CoVR was observed (p <0.0001). In multivariate analysis the CaM and CoVR scores were predicted by DLco, FVC, Borg score and HAQ-DI. Age, sex, disease duration, anti-topoisomerase antibodies and mRSS were not significantly associated with severity of pulmonary fibrosis on CaM- and CoVR methods.
Conclusions: Although a close correlation between CaM score results and CoVR total score was found, CaM analysis showed a more significant correlation with DLco (more so than the FVC), patient-centred measures of perceived dyspnea and functional disability. Computer-aided tomographic analysis is computationally efficient, and in combination with physiologic and patient-centred measures, it could allow a means for accurately assessing and monitoring the disease progression or response to therapy.