More than a decade has passed since researchers in the Early Lung Cancer Action Project and the National Lung Screening Trial demonstrated the ability to save lives of high-risk individuals from lung cancer through regular screening by low dose computed tomography scan. The emergence of the most recent findings in the Dutch-Belgian lung-cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek [NELSON]) further strengthens and expands on this evidence. These studies demonstrate the benefit of integrating lung cancer screening into clinical practice, yet lung cancer continues to lead cancer mortality rates in the United States.
View Article and Find Full Text PDFLung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world.
View Article and Find Full Text PDFIntroduction: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research.
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