1. The International Association for the Study of Lung Cancer Early Lung Imaging Confederation
- Author
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Anthony P. Reeves, John K. Field, Xie Xueqian, Stephen Lam, Fred R. Hirsch, David F. Yankelevitz, Murry W. Wynes, Patrick Rogalla, Matthijs Oudkerk, Daniel C. Sullivan, Laurie Fenton Ambrose, Claudia I. Henschke, Robert F. Janz, Haije H J Wind, Ed Conley, A. Devaraj, Annette McWilliams, James L. Mulshine, Peter M. A. van Ooijen, Ricardo Scott Avila, Ning Wu, Ryutaro Kakinuma, Ugo Pastorino, Heidi Schmidt, and Tanya Flanagan
- Subjects
0301 basic medicine ,medicine.medical_specialty ,Lung Neoplasms ,Early lung cancer ,Computed tomography ,03 medical and health sciences ,0302 clinical medicine ,Lung imaging ,Image Processing, Computer-Assisted ,Humans ,Medicine ,Lung cancer ,PULMONARY NODULES ,Review Articles ,Early Detection of Cancer ,medicine.diagnostic_test ,business.industry ,Patient Selection ,MORTALITY ,Reproducibility of Results ,General Medicine ,medicine.disease ,030104 developmental biology ,Tomography x ray computed ,030220 oncology & carcinogenesis ,Practice Guidelines as Topic ,TRIAL ,Tomography ,Radiology ,Tomography, X-Ray Computed ,business ,Algorithms ,CT - Abstract
PURPOSE To improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care. METHODS ELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC’s hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk. RESULTS The goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools. CONCLUSION This initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide.
- Published
- 2019
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