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Development of a deep learning-based nomogram for predicting lymph node metastasis in cervical cancer: A multicenter study.

作者信息

Liu Yujia, Duan Hui, Dong Di, Chen Jiaming, Zhong Lianzhen, Zhang Liwen, Cao Runnan, Fan Huijian, Cui Zhumei, Liu Ping, Kang Shan, Zhan Xuemei, Wang Shaoguang, Zhao Xun, Chen Chunlin, Tian Jie

机构信息

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

出版信息

Clin Transl Med. 2022 Jul;12(7):e938. doi: 10.1002/ctm2.938.

DOI:10.1002/ctm2.938
PMID:35839331
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9286523/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/3c77957cd5ab/CTM2-12-e938-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/db31fa1e4183/CTM2-12-e938-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/0387dd7fee15/CTM2-12-e938-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/3c77957cd5ab/CTM2-12-e938-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/db31fa1e4183/CTM2-12-e938-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/0387dd7fee15/CTM2-12-e938-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d622/9286523/3c77957cd5ab/CTM2-12-e938-g001.jpg

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本文引用的文献

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Transl Oncol. 2021 Aug;14(8):101113. doi: 10.1016/j.tranon.2021.101113. Epub 2021 May 8.
2
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.《全球癌症统计数据 2020:全球 185 个国家和地区 36 种癌症的发病率和死亡率估计》。
CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
3
Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.
利用临床参数和基于MRI数据的深度学习预测可手术宫颈癌的淋巴结转移:一项多中心研究
Insights Imaging. 2024 Feb 27;15(1):56. doi: 10.1186/s13244-024-01618-7.
4
Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study.基于人群的肺腺癌预后列线图的建立和验证。
J Healthc Eng. 2022 Dec 10;2022:5698582. doi: 10.1155/2022/5698582. eCollection 2022.
基于 CT 影像组学的无创模型预测早期宫颈癌淋巴结转移的研究
Br J Radiol. 2020 Apr;93(1108):20190558. doi: 10.1259/bjr.20190558. Epub 2020 Jan 30.
4
Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Editorial Board.评估人工智能放射学研究:给作者、审稿人和读者的简要指南——来自编辑委员会
Radiology. 2020 Mar;294(3):487-489. doi: 10.1148/radiol.2019192515. Epub 2019 Dec 31.
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Squeeze-and-Excitation Networks.挤压激励网络。
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