Re-IMAGinING the Pathway for Clinical Decision Making in Rare Lung Diseases: Moving Towards a United Vision

Abstract

This industry-supported symposium was held during the European Respiratory Society (ERS) International Congress and included presentations from several internationally renowned experts in rare lung diseases. The panel discussed the need to improve clinical decision making to expedite disease recognition, prognostic prediction, and early treatment in interstitial lung disease (ILD) and alpha 1 antitrypsin (AAT) deficiency-related chronic obstructive pulmonary disease (COPD). Daiana Stolz, Clinic for Pneumology, University Hospital Freiburg, Switzerland, and Marlies S. Wijsenbeek, Erasmus University Medical Centre, Rotterdam, the Netherlands, explained that although high-resolution CT (HRCT) scans may appear similar, ILD from different causes results in significantly different patient outcomes. Therefore, image analysis and the identification of sensitive and specific biomarkers are critical to improving diagnosis and monitoring treatment response and disease progression in ILD. Charlie Strange, Medical University of South Carolina, Charleston, USA, and Gerry McElvaney, Irish Centre for Genetic Lung Disease, Dublin, Ireland, described the variability in CT-based lung density measurements used to assess the progression of emphysema in patients with AAT deficiency. Clinical trial data indicate that accurate CT lung density measurements are superior to lung function measurements and other endpoints to detect disease progression. However, Strange presented data that showed the considerable impact of acute exacerbations of COPD on CT imaging measurements. One organisation working to improve the accuracy and value of imaging data in lung diseases is the Open Source Imaging Consortium (OSIC), Saugatuck, Michigan, USA. Elizabeth Estes, who works for OSIC, explained that OSIC aims to build a large, global database of anonymised patient data and CT images in ILD, with plans for future expansion into other rare lung diseases. The ultimate goals of this effort are to encourage collaboration, and to develop machine learning algorithms to improve clinical decision making in rare lung diseases.