Pos0325 radiomic Biomarker of Pulmonary Vascular Related Structures Predicts Mortality in Systemic Sclerosis

Abstract

Background:Quantitative computed tomography (QCT) extracts features from high-resolution CT scans and quantifies lung parenchymal and vascular abnormalities which may not be discernable by qualitative review. The threshold values of individual parenchymal abnormalities and vascular features measured by QCT methods which associate with mortality in systemic sclerosis (SSc) are currently unknown.Objectives:To determine whether QCT measures, specifically pulmonary parenchymal abnormalities and pulmonary vascular related structures (PVRS), can predict mortality in SSc and to determine the optimal quantitative thresholds for those parameters.Methods:A total of 133 subjects (76% women) meeting 2013 ACR/EULAR classification criteria for SSc with a baseline CT within 3 years of diagnosis were retrospectively identified for inclusion. CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) was used to quantitatively measure volume of ground glass opacities (GGO), reticular densities, and honeycombing (HC). Total interstitial lung disease (ILD) was the summation of these features. PVRS was also quantified using CALIPER. Values for each feature were expressed as a percentage of total lung volume. Cox models evaluated the hazard ratio (HR) for mortality for each parameter adjusting for age at SSc diagnosis, sex, diffuse SSc subtype, and history of smoking. The optimal thresholds for mortality prediction for each parameter were determined using consensus between 4 methods: Contal and O’Quigley Method, Cox Model Hazard Ratio, Cox Model Wald P-value, and False Discovery Rate. The c-statistic was used to assess each models’ ability to predict mortality.Results:Mean ±SD for age at SSc diagnosis was 61 ± 13 years and length of follow-up was 4.7 ± 3.0 years. There were 32 deaths (24%). A Cox model including age (HR 1.05, 95% CI: 1.01-1.09), female sex (HR 0.49, 95% CI: 0.22-1.08), diffuse SSc subtype (HR 1.50, 95% CI: 0.69-3.30), and history of smoking (HR 2.09, 95% CI: 0.97-4.53) (Model 1) significantly predicted mortality (C-statistic 0.72, 95% CI: 0.63-0.81). Adjusting for Model 1, reticular densities% (HR 1.19, 95% CI: 1.05-1.35), total ILD% (HR 1.02, 95% CI: 1.00-1.03), and PVRS% (HR 1.19, 95% CI: 1.05-1.35) were associated with mortality on univariable analyses; GGO% (HR 1.01, 95% CI: 0.98-1.04) was not significantly associated with mortality. The optimal thresholds for mortality prediction were then determined and were as follows: GGO=20%, reticular densities=8%, total ILD=20%, and PVRS=5%. While the risk of mortality was significantly increased in subjects with GGO ≥20% (HR 2.70, 95% CI: 1.21-6.05), reticular densities ≥8% (HR 4.64, 95% CI: 1.68-12.81), and total ILD ≥20% (2.59, 95% CI: 1.12-5.99), these baseline thresholds did not improve upon mortality prediction when added individually to Model 1 (C-statistic 0.73 for each). PVRS ≥5%, which had an over six-fold increase in mortality (HR 6.42, 95% CI: 2.60-15.88), did improve mortality prediction when added to Model 1 (C-statistic 0.78, 95% CI: 0.70-0.86).Conclusion:PVRS strongly associates with early mortality in patients with SSc and represents a novel radiomic biomarker that provides prognostic information on mortality beyond pulmonary parenchymal abnormalities. CALIPER derived PVRS quantifies CT data through a function that defines connected tubular branching structures. This extracts pulmonary arteries and veins from the adjacent parenchyma but could potentially also include regions of adjoining of fibrosis.1 Larger studies examining the association between PVRS and progression of cardiopulmonary disease are warranted.References:[1]Jacob J, Bartholmai BJ, Rajagopalan S, et al. Predicting Outcomes in Idiopathic Pulmonary Fibrosis Using Automated Computed Tomographic Analysis. Am J Respir Crit Care Med 2018;198:767-76.Acknowledgements:This project was supported by the Mayo Clinic Margaret Harvey Schering Clinician Career Development Award.Disclosure of Interests:Alicia Hinze: None declared, Yasser Radwan: None declared, Mamoun Elnagar: None declared, Reto Kurmann: None declared, Shreyasee Amin: None declared, Robert Vassallo Grant/research support from: Pfizer, Bristol Myers Squibb, Sun Pharma, Cynthia S. Crowson: None declared, Brian Bartholmai Consultant of: AstraZenica, Boehringer Ingelheim, Promedior LLC (all <$5,000 annually)