Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT-Reference-Cited by-同舟云学术

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Published:2022-08 Issue: Volume:82 Page:104127
ISSN:2352-3964
Container-title:eBioMedicine
language:en
Short-container-title:eBioMedicine

Author:

Huang Brian,Sollee John^ORCID,Luo Yong-Heng,Reddy Ashwin^ORCID,Zhong Zhusi,Wu Jing,Mammarappallil Joseph,Healey Terrance,Cheng Gang,Azzoli Christopher,Korogodsky Dana,Zhang Paul,Feng Xue,Li Jie,Yang Li,Jiao Zhicheng,Bai Harrison Xiao

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Reference42 articles.

1. Lung cancer 2020: epidemiology, etiology, and prevention;Bade,2020

2. Recent Advances in the Management of Lung Cancer;Jones;Clin Med (Lond),2018

3. Reduced lung-cancer mortality with volume CT screening in a randomized trial;de Koning;N Engl J Med,2020

4. Cancer statistics, 2018;Siegel;CA Cancer J Clin,2018

5. The eighth edition lung cancer stage classification;Detterbeck,2017

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