Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification

被引:0
|
作者
Qi, W. [1 ]
Li, S. [1 ]
Xiao, J. [2 ]
Zhang, W. [3 ]
Mo, Z. [2 ]
He, S. M. [4 ]
Li, H. [5 ]
Chen, J. [6 ]
Zhao, S. [6 ]
机构
[1] Shanghai Jiaotong Univ Sch Med, Ruijin Hosp, Dept Radiat Oncol, Shanghai, Peoples R China
[2] Shenzhen United Imaging Res Inst Innovat Med Equi, Shenzhen, Peoples R China
[3] Shanghai United Imaging Healthcare Technol Co Ltd, Shanghai, Peoples R China
[4] United Imaging Res Inst Intelligent Imaging, Beijing, Peoples R China
[5] Shanghai Jiaotong Univ Sch Med, Ruijin Hosp, Dept Thorac Surg, Shanghai, Peoples R China
[6] Shanghai Jiao Tong Univ Sch Med, Ruijin Hosp, Shanghai, Peoples R China
来源
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS | 2023年 / 117卷 / 02期
关键词
D O I
暂无
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
2784
引用
收藏
页码:E358 / E359
页数:2
相关论文
共 50 条
  • [21] Prediction of response to neoadjuvant therapy in esophageal carcinoma by PET-CT
    Thurau, Kirsten
    Bruewer, Matthias
    Haier, Joerg
    Franzius, Christine
    Juergens, Kai U.
    Senninger, Norbert
    GASTROENTEROLOGY, 2008, 134 (04) : A903 - A903
  • [22] A Comparison of Machine Learning Algorithms for Outcome Prediction in Neoadjuvant Chemotherapy for Esophageal Squamous Cell Carcinoma
    Kang, X.
    Huang, C.
    ANNALS OF SURGICAL ONCOLOGY, 2019, 26 : S204 - S204
  • [23] Diffusion-Weighted MRI and 18f-FDG PET/CT in Assessing Response to Neoadjuvant Chemoradiotherapy in Potentially Resectable Locally Advanced Esophageal Squamous Cell Carcinoma
    Xu, X.
    Hu, B.
    Rong, L.
    Xie, H.
    Zhang, F.
    Zhang, C.
    Ye, Q.
    Ma, X.
    Bai, Y.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2019, 105 (01): : E204 - E204
  • [24] The feasibility and safety of radical esophagectomy in patients receiving neoadjuvant chemoradiotherapy with pembrolizumab for esophageal squamous cell carcinoma
    Park, Seong Yong
    Hong, Min Hee
    Kim, Hye Ryun
    Lee, Chang Geol
    Cho, Jae Ho
    Cho, Byoung Chul
    Kim, Dae Joon
    JOURNAL OF THORACIC DISEASE, 2020, 12 (11) : 6426 - 6434
  • [25] SUVmax of FDG-PET correlates with the effects of neoadjuvant chemoradiotherapy for oral squamous cell carcinoma
    Miyawaki, Akihiko
    Ikeda, Ryuji
    Hijioka, Hiroshi
    Ishida, Takayuki
    Ushiyama, Mina
    Nozoe, Etsuro
    Nakamura, Norifumi
    ONCOLOGY REPORTS, 2010, 23 (05) : 1205 - 1212
  • [26] Combination of liquid biopsy and PET/CT enhances prediction of pathological response to neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma
    Yang, Weixiong
    Fang, Zengli
    Wang, Xiaoyan
    Luo, Hui
    Zhang, Shuishen
    Zeng, Bo
    Liu, Zhenguo
    Wang, Chenxuan
    Ou, Qiuxiang
    Yang, Lingling
    Tang, Haimeng
    Yeung, Sai-Ching J.
    Cheng, Chao
    CANCER RESEARCH, 2024, 84 (06)
  • [27] Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation
    Jin Wang
    Xiang Zhu
    Jian Zeng
    Cheng Liu
    Wei Shen
    Xiaojiang Sun
    Qingren Lin
    Jun Fang
    Qixun Chen
    Yongling Ji
    European Radiology, 2023, 33 : 8554 - 8563
  • [28] Development of a nomogram for the prediction of pathological complete response after neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma
    Chao, Yin-Kai
    Chang, Hsien-Kun
    Tseng, Chen-Kan
    Liu, Yun-Hen
    Wen, Yu-Wen
    DISEASES OF THE ESOPHAGUS, 2017, 30 (02):
  • [29] Composite Pretreatment CT and 18F-FDG PET Radiomic-Based Prediction of Pathological Response of Rectal Cancer Patients Treated with Neoadjuvant Chemoradiotherapy
    Yuan, Z. M.
    Zhang, G. G.
    Latifi, K.
    Moros, E. G.
    Felder, S.
    Sanchez, J.
    Dessureault, S.
    Imanirad, I.
    Kim, R.
    Harrison, L. B.
    Hoffe, S.
    Frakes, J. M.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2019, 105 (01): : E177 - E177
  • [30] Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma
    Kaijiong Zhang
    Bo Ye
    Lichun Wu
    Sujiao Ni
    Yang Li
    Qifeng Wang
    Peng Zhang
    Dongsheng Wang
    Scientific Reports, 13