Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT-Reference-Cited by-同舟云学术

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

Published:2022-06-28 Issue:9 Volume:17 Page:1651-1661
ISSN:1861-6429
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Sasaki Yuki^ORCID,Kondo Yohan^ORCID,Aoki Tadashi,Koizumi Naoya,Ozaki Toshiro,Seki Hiroshi

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11548-022-02694-0.pdf

Reference37 articles.

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4. Asamura H, Goya T, Koshiishi Y, Sohara Y, Eguchi K, Mori M, Nakanishi Y, Tsuchiya R, Shimokata R, Shimokata K, Inoue H, Nukiwa T, Miyaoka E, Japanese Joint Committee for Lung Cancer Registry (2008) A Japanese lung cancer registry study: prognosis of 13,010 resected lung cancers. J Thorac Oncol 3:46–52. https://doi.org/10.1097/JTO.0b013e31815e8577

5. Okami J, Shintani Y, Okumura M, Ito H, Ohtsuka T, Toyooka S, Mori T, Watanabe SI, Date H, Yokoi K, Asamura H, Nagayasu T, Miyaoka E, Yoshiono I, Japanese Joint Committee for Lung Cancer Registry (2019) Demographics, safety and quality, and prognostic information in both the seventh and eighth editions of the TNM classification in 18,973 surgical cases of the Japanese joint committee of lung cancer registry database in 2010. J Thorac Oncol 14:212–222. https://doi.org/10.1016/j.jtho.2018.10.002

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