The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.


Lam, Stephen, Wynes, Murry W, Connolly, Casey, Ashizawa, Kazuto, Atkar-Khattra, Sukhinder, Belani, Chandra P, DiNatale, Domenic, Henschke, Claudia I, Hochhegger, Bruno, Jacomelli, Claudio et al (show 20 more authors) (2023) The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 19 (1). S1556-0864(23)00736-0-S1556-0864(23)00736-0.

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Abstract

<h4>Background</h4>With global adoption of 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 dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) 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 utilized for clinically relevant AI research.<h4>Methods</h4>In this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.<h4>Results</h4>A total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.<h4>Conclusion</h4>These initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets.

Item Type: Article
Uncontrolled Keywords: Lung, Humans, Lung Neoplasms, Emphysema, Artificial Intelligence, Early Detection of Cancer, Deep Learning
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 05 Sep 2023 07:15
Last Modified: 03 Feb 2024 06:02
DOI: 10.1016/j.jtho.2023.08.016
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3172531
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