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. 2024 Jan;19(1):94-105.
doi: 10.1016/j.jtho.2023.08.016. Epub 2023 Aug 16.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative

Stephen Lam  1 , Murry W Wynes  2 , Casey Connolly  2 , Kazuto Ashizawa  3 , Sukhinder Atkar-Khattra  4 , Chandra P Belani  5 , Domenic DiNatale  6 , Claudia I Henschke  7 , Bruno Hochhegger  8 , Claudio Jacomelli  9 , Małgorzata Jelitto  10 , Artit Jirapatnakul  7 , Karen L Kelly  2 , Karthik Krishnan  11 , Takeshi Kobayashi  12 , Jacqueline Logan  13 , Juliane Mattos  14 , John Mayo  15 , Annette McWilliams  16 , Tetsuya Mitsudomi  17 , Ugo Pastorino  18 , Joanna Polańska  19 , Witold Rzyman  20 , Ricardo Sales Dos Santos  21 , Giorgio V Scagliotti  22 , Heather Wakelee  23 , David F Yankelevitz  7 , John K Field  24 , James L Mulshine  25 , Ricardo Avila  11
Affiliations
Free article

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative

Stephen Lam et al. J Thorac Oncol. 2024 Jan.
Free article

Abstract

Introduction: 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.

Methods: 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.

Results: A total of 1394 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 more than or equal to 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.

Conclusions: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

Keywords: Artificial intelligence; Deep learning; Emphysema; Lung cancer screening; Nodule detection; Nodule volume measurement.

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