Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning-Reference-Cited by-同舟云学术

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

Published:2019-05-10 Issue:7 Volume:8 Page:3532-3543
ISSN:2045-7634
Container-title:Cancer Medicine
language:en
Short-container-title:Cancer Med

Author:

Zhao Wei¹²,Yang Jiancheng³⁴⁵,Ni Bingbing³⁴,Bi Dexi⁶,Sun Yingli¹,Xu Mengdi⁵,Zhu Xiaoxia¹,Li Cheng¹,Jin Liang¹,Gao Pan¹,Wang Peijun⁷,Hua Yanqing¹,Li Ming¹²⁸^ORCID

Affiliation:

1. Department of Radiology Huadong Hospital Affiliated to Fudan University Shanghai China

2. Diagnosis and Treatment Center of Small Lung Nodules Huadong Hospital Shanghai China

3. Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

4. SJTU‐UCLA Joint Center for Machine Perception and Inference Shanghai Jiao Tong University Shanghai China

5. Diannei Technology Shanghai China

6. Department of Pathology Shanghai Tenth People's Hospital, Tongji University School of Medicine Shanghai China

7. Department of Radiology, School of Medicine Tongji Hospital, Tongji University Shanghai China

8. Institute of Functional and Molecular Medical Imaging Fudan University Shanghai China

Funder

National Natural Science Foundation of China

Shanghai Hospital Development Center

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cam4.2233

Reference50 articles.

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5. Prognostic value of K-RAS mutations in patients with non-small cell lung cancer: A systematic review with meta-analysis

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