Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image-Reference-Cited by-同舟云学术

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image

Published:2024-08-15 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Wang Guang-Yue,Zhu Jing-Fei,Wang Qi-Chao,Qin Jia-Xin,Wang Xin-Lei,Liu Xing,Liu Xin-Yu,Chen Jun-Zhi,Zhu Jie-Fei,Zhuo Shi-Chao,Wu Di,Li Na,Chao Liu,Meng Fan-Lai,Lu Hao,Shi Zhen-Duo,Jia Zhi-Gang,Han Cong-Hui

Funder

Xuzhou City health Commission general project

2023 Xuzhou City to promote science and technology innovation special fund key projects

the Jiangsu Province Key Research and Development Program

the Key Projects of Xuzhou Science and Technology Plan

the Xuzhou Medical Key Talents Project

the National Natural Science Foundation of China

the Xuzhou Medical Outstanding Talents

the Xuzhou Clinical Medicine Expert Team Project

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-66870-9.pdf

Reference23 articles.

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4. Sylvester, R. J. et al. Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials. Eur. Urol. 49(3), 466–465 (2006) (discussion 475-467).

5. Fernandez-Gomez, J. et al. Predicting nonmuscle invasive bladder cancer recurrence and progression in patients treated with bacillus Calmette-Guerin: The CUETO scoring model. J. Urol. 182(5), 2195–2203 (2009).