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

被引：3

作者：

Lam, Stephen ^{[1
,2
]}

Wynes, Murry W. ^{[3
]}

Connolly, Casey ^{[3
]}

Ashizawa, Kazuto ^{[4
]}

Atkar-Khattra, Sukhinder ^{[5
]}

Belani, Chandra P. ^{[6
]}

DiNatale, Domenic ^{[7
]}

Henschke, Claudia I. ^{[8
]}

Hochhegger, Bruno ^{[9
]}

Jacomelli, Claudio ^{[10
]}

Jelitto, Malgorzata ^{[11
]}

Jirapatnakul, Artit ^{[8
]}

Kelly, Karen L. ^{[3
]}

Krishnan, Karthik ^{[12
]}

Kobayashi, Takeshi ^{[13
]}

Logan, Jacqueline ^{[14
]}

Mattos, Juliane ^{[15
]}

Mayo, John ^{[16
,17
]}

McWilliams, Annette ^{[18
]}

Mitsudomi, Tetsuya ^{[19
]}

Pastorino, Ugo ^{[20
]}

Polanska, Joanna ^{[21
]}

Rzyman, Witold ^{[22
]}

dos Santos, Ricardo Sales ^{[23
]}

Scagliotti, Giorgio V. ^{[24
]}

Wakelee, Heather ^{[25
]}

Yankelevitz, David F.

Field, John K. ^{[26
]}

Mulshine, James L. ^{[27
]}

Avila, Ricardo ^{[12
]}

机构：

[1] Univ British Columbia, British Columbia Canc Res Inst, Dept Integrat Oncol, Vancouver, BC, Canada

[2] Univ British Columbia, Dept Med, Vancouver, BC, Canada

[3] Int Assoc Study Lung Canc, Denver, CO USA

[4] Nagasaki Univ, Grad Sch Biomed Sci, Dept Clin Oncol, Nagasaki, Japan

[5] British Columbia Canc Res Inst, Dept Integrat Oncol, Vancouver, BC, Canada

[6] Penn State Coll Med, Dept Med, Hershey, PA USA

[7] Intellitech Innovat, Fonda, NY USA

[8] Icahn Sch Med Mt Sinai, Dept Radiol, New York, NY USA

[9] Univ Florida, Dept Radiol, Gainesville, FL USA

[10] Natl Canc Inst, Data Management, Milan, Italy

[11] Med Univ Gdansk, Radiol Dept, Gdansk, Poland

[12] Accumetra, Clifton Pk, NY USA

[13] Ishikawa Prefectural Cent Hosp, Dept Diagnost & Intervent Radiol, Kanazawa, Ishikawa, Japan

[14] Fiona Stanley Hosp, Perth, WA, Australia

[15] Fed Univ Hlth Sci Porto Alegre, Porto Alegre, RS, Brazil

[16] Vancouver Gen Hosp, Dept Radiol, Vancouver, BC, Canada

[17] Univ British Columbia, Vancouver, BC, Canada

[18] Univ Western Australia, Fiona Stanley Hosp, Perth, WA, Australia

[19] Kindai Univ, Fac Med, Dept Surg, Div Thorac Surg, Osaka, Japan

[20] Natl Canc Inst, Dept Surg, Sect Thorac Surg, Milan, Italy

[21] Silesian Tech Univ, Dept Data Sci & Engn, Gliwice, Poland

[22] Med Univ Gdansk, Dept Thorac Surg, Gdansk, Poland

[23] Hosp Israelita Albert Einstein, Hosp Cardio Pulm Bahia, Sao Paulo, Brazil

[24] Univ Torino, Dept Oncol, Turin, Italy

[25] Stanford Univ, Stanford Canc Inst, Stanford, CA USA

[26] Univ Liverpool, Dept Mol & Clin Canc Med, Roy Castle Lung Canc Res Programme, Liverpool, Merseyside, England

[27] Rush Univ, Grad Coll, Med Ctr, Internal Med, Chicago, IL USA

来源：

JOURNAL OF THORACIC ONCOLOGY | 2024年 / 19卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Lung cancer screening; Artificial intelligence; Deep learning; Emphysema; Nodule detection; Nodule vol ume measurement; ACTION PROJECT; CT; EMPHYSEMA; MORTALITY;

D O I：

10.1016/j.jtho.2023.08.016

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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. (c) 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

引用

页码：94 / 105

页数：12

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