The Early Lung Imaging Confederation (ELIC) is an international alliance of collaborating individuals and institutions who share a vision to develop a globally distributed, privacy-secured, lung cancer imaging database and computational analysis environment designed to enable the analysis and study of extremely large collections of quality-controlled internationally assembled CT lung cancer images and associated biomedical data for research and healthcare delivery.
The ELIC infrastructure is being developed using a Hub and Spoke model that leverages cloud computing resources and will allow clinical research groups (Spokes) to securely make their locally stored de-identified lung cancer imaging collections available for computational analysis by other research groups (Clients), all coordinated by a central ELIC managed server (Hub). Clinical sites will be able to make lung cancer imaging data available for specific types of computational analysis without transmitting the imaging data over national boundaries to other groups and losing control over how the data is used and further distributed. This allows lung cancer screening research groups to more easily make available datasets to large global lung cancer imaging research studies with far more control over data use.
A fundamental goal of ELIC is to support the development of deep learning methods or artificial intelligence approaches to detect early lung cancer and other diseases using thoracic CT images, characterize small pulmonary nodules and surrounding structures, and to measure responsiveness to therapeutic interventions. Implementing LDCT screening could be greatly facilitated by the use of validated deep learning algorithms and the development of other types of reliable software tools but these will require large amounts of data for algorithm development and performance evaluation.