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

Author:

Sasaki YukiORCID,Kondo YohanORCID,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

Reference37 articles.

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2. Sawabata N, Asamura H, Goya T, Mori M, Nakanishi K, Eguchi K, Koshiishi Y, Okumura M, Miyaoka E, Fujii Y, Japanese Joint Committee for Lung Cancer Registry (2010) Japanese lung cancer registy study: first prospective enrollment of a large number of surgical and nonsurgical cases in 2002. J Thorac Oncol 5:1369–1375. https://doi.org/10.1097/JTO.0b013e3181e452b9

3. The Japan Lung Cancer Society (2020) Guidelines for diagnosis and treatment of the lung cancer/malignant pleural mesothelioma/thymic tumors. Kanehara & Co.,Ltd. https://www.haigan.gr.jp/modules/guideline/index.php?content_id=3. Accessed 10 April 2020

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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