Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides-Reference-Cited by-同舟云学术

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides

Published:2019-02-06 Issue:1 Volume:9 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Gertych Arkadiusz^ORCID,Swiderska-Chadaj Zaneta^ORCID,Ma Zhaoxuan,Ing Nathan,Markiewicz Tomasz,Cierniak Szczepan,Salemi Hootan,Guzman Samuel,Walts Ann E.,Knudsen Beatrice S.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-018-37638-9.pdf

Reference51 articles.

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2. Russell, P. A. et al. Does lung adenocarcinoma subtype predict patient survival?: A clinicopathologic study based on the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol 6, 1496–1504, https://doi.org/10.1097/JTO.0b013e318221f701 (2011).

3. Yoshizawa, A. et al. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol 24, 653–664, https://doi.org/10.1038/modpathol.2010.232 (2011).

4. Yoshizawa, A. et al. Validation of the IASLC/ATS/ERS lung adenocarcinoma classification for prognosis and association with EGFR and KRAS gene mutations: analysis of 440 Japanese patients. J Thorac Oncol 8, 52–61, https://doi.org/10.1097/JTO.0b013e3182769aa8 (2013).

5. Araki, K. et al. Excellent prognosis of lepidic-predominant lung adenocarcinoma: low incidence of lymphatic vessel invasion as a key factor. Anticancer Res 34, 3153–3156 (2014).

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