The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative-Reference-Cited by-同舟云学术

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

Published:2024-01 Issue:1 Volume:19 Page:94-105
ISSN:1556-0864
Container-title:Journal of Thoracic Oncology
language:en
Short-container-title:Journal of Thoracic Oncology

Author:

Lam Stephen,Wynes Murry W.,Connolly Casey,Ashizawa Kazuto,Atkar-Khattra Sukhinder,Belani Chandra P.,DiNatale Domenic,Henschke Claudia I.,Hochhegger Bruno,Jacomelli Claudio,Jelitto Małgorzata,Jirapatnakul Artit,Kelly Karen L.,Krishnan Karthik,Kobayashi Takeshi,Logan Jacqueline,Mattos Juliane,Mayo John,McWilliams Annette,Mitsudomi Tetsuya,Pastorino Ugo,Polańska Joanna,Rzyman Witold,Sales dos Santos Ricardo,Scagliotti Giorgio V.,Wakelee Heather,Yankelevitz David F.,Field John K.,Mulshine James L.,Avila Ricardo

Funder

International Association for the Study of Lung Cancer

AstraZeneca

Publisher

Elsevier BV

Subject

Pulmonary and Respiratory Medicine,Oncology

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1. A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology;Journal of Thoracic Oncology;2024-09

2. Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer;Journal of Thoracic Oncology;2024-01

3. Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary;International Journal of Cancer;2023-12-29