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Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics.

作者信息

Fang Mengjie, Tian Jie, Dong Di

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, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing 100190, China.

出版信息

EClinicalMedicine. 2022 Apr 7;46:101380. doi: 10.1016/j.eclinm.2022.101380. eCollection 2022 Apr.

DOI:10.1016/j.eclinm.2022.101380
PMID:35434584
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9006631/
Abstract
摘要

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Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics.通过深度学习影像组学对胃癌新辅助化疗反应进行无创预测。
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2
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.基于CT的深度学习影像组学列线图预测局部晚期胃癌患者新辅助化疗反应：一项多中心队列研究
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BioData Min. 2023 Jul 18;16(1):21. doi: 10.1186/s13040-023-00335-z.
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System Analysis Based on Lipid-Metabolism-Related Genes Identifies AGT as a Novel Therapy Target for Gastric Cancer with Neoadjuvant Chemotherapy.基于脂质代谢相关基因的系统分析确定血管紧张素原（AGT）为新辅助化疗胃癌的新型治疗靶点。
Pharmaceutics. 2023 Mar 2;15(3):810. doi: 10.3390/pharmaceutics15030810.

本文引用的文献

1
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.基于CT的深度学习影像组学列线图预测局部晚期胃癌患者新辅助化疗反应：一项多中心队列研究
EClinicalMedicine. 2022 Mar 21;46:101348. doi: 10.1016/j.eclinm.2022.101348. eCollection 2022 Apr.
2
Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy.晚期胃癌：CT 放射组学预测及新辅助化疗降期的早期检测。
Eur Radiol. 2021 Nov;31(11):8765-8774. doi: 10.1007/s00330-021-07962-2. Epub 2021 Apr 28.
3
Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study.基于深度学习的放大窄带成像识别早期胃癌：多中心研究。
Gastrointest Endosc. 2021 Jun;93(6):1333-1341.e3. doi: 10.1016/j.gie.2020.11.014. Epub 2020 Nov 26.
4
Gastric cancer.胃癌。
Lancet. 2020 Aug 29;396(10251):635-648. doi: 10.1016/S0140-6736(20)31288-5.
5
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.深度学习放射组学列线图可预测局部进展期胃癌的淋巴结转移数目：一项国际多中心研究。
Ann Oncol. 2020 Jul;31(7):912-920. doi: 10.1016/j.annonc.2020.04.003. Epub 2020 Apr 15.
6
Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer.基于 CT 的影像组学列线图的开发与验证：用于预测晚期胃癌患者早期复发的术前预测。
Radiother Oncol. 2020 Apr;145:13-20. doi: 10.1016/j.radonc.2019.11.023. Epub 2019 Dec 21.
7
Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer.开发和验证一种个体化列线图以识别晚期胃癌患者隐匿性腹膜转移。
Ann Oncol. 2019 Mar 1;30(3):431-438. doi: 10.1093/annonc/mdz001.
8
Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study.基于计算机断层扫描的放射组学预测局部晚期胃癌新辅助化疗疗效的初步研究
Chin J Cancer Res. 2018 Aug;30(4):406-414. doi: 10.21147/j.issn.1000-9604.2018.04.03.
9
Histopathologic diversity of gastric cancers: Relationship between enhancement pattern on dynamic contrast-enhanced CT and histological type.胃癌的组织病理学多样性：动态对比增强 CT 增强模式与组织学类型的关系。
Eur J Radiol. 2017 Dec;97:90-95. doi: 10.1016/j.ejrad.2017.10.018. Epub 2017 Oct 26.