A Radiomics-Based Light Gradient Boosting Machine to Predict Radiation-Induced Toxicities in Nasopharynx Cancer Patients Receiving Chemoradiotherapy

被引:0
|
作者
Jiang, Z. [1 ]
Liang, Y. [2 ]
Wang, X. [3 ]
Min, Z. [4 ]
Feng, M. [4 ]
Kuang, Y. [5 ]
机构
[1] Univ Nevada, Las Vegas, NV 89154 USA
[2] Chinese Acad Med Sci, Sichuan Ctr, Canc Hosp, Chengdu, Peoples R China
[3] Radiat Oncol Key Lab Sichuan Prov, Chengdu, Peoples R China
[4] Sichuan Canc Hosp & Inst, Sichuan Canc Ctr, Chengdu, Peoples R China
[5] Univ Nevada, Las Vegas, NV 89154 USA
关键词
D O I
暂无
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PO-GePV-T-
引用
收藏
页码:E852 / E852
页数:1
相关论文
共 50 条
  • [21] MRI Radiomics-Based Machine Learning to Predict Lymphovascular Invasion of HER2-Positive Breast Cancer
    Han, Fang
    Li, Wenfei
    Hu, Yurui
    Wang, Huiping
    Liu, Tianyu
    Wu, Jianlin
    JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2024,
  • [22] Development and Validation of an MRI Radiomics-Based Signature to Predict Histological Grade in Patients with Invasive Breast Cancer
    Wang, Shihui
    Wei, Yi
    Li, Zhouli
    Xu, Jingya
    Zhou, Yunfeng
    BREAST CANCER-TARGETS AND THERAPY, 2022, 14 : 335 - 342
  • [23] Evaluation of the effect of selenium on radiation-induced toxicities in head neck cancer patients
    Buentzel, J.
    Micke, O.
    Glatzel, M.
    Bruns, F.
    Kisters, K.
    Muecke, R.
    JOURNAL OF CLINICAL ONCOLOGY, 2009, 27 (15)
  • [24] Radiomics of skeletal muscle helps to predict gastrointestinal toxicity in locally advanced rectal cancer patients receiving neoadjuvant chemoradiotherapy
    Yang, Wang
    Zhang, Zhiyuan
    Zhou, Menglong
    Wang, Jiazhou
    Li, Guichao
    Wang, Yan
    Shen, Lijun
    Zhang, Hui
    Wan, Juefeng
    Xia, Fan
    Zhang, Zhen
    CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY, 2024, 44
  • [25] A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study
    Wei, Zongjie
    Bai, Xuesong
    Xv, Yingjie
    Chen, Shao-Hao
    Yin, Siwen
    Li, Yang
    Lv, Fajin
    Xiao, Mingzhao
    Xie, Yongpeng
    INSIGHTS INTO IMAGING, 2024, 15 (01):
  • [26] A Machine Learning Model with Radiomics Based on PET Images to Predict Pathological Response by Neoadjuvant Chemoradiotherapy for Esophageal Cancer
    Murakami, Y.
    Kawahara, D.
    Imano, N.
    Takahashi, I.
    Takeuchi, Y.
    Nishibuchi, I.
    Kimura, T.
    Nagata, Y.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2020, 108 (03): : E622 - E622
  • [27] Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine
    Zeng, Xiangrui
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [28] Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer
    Talebi, Amin
    Bitarafan-Rajabi, Ahmad
    Alizadeh-asl, Azin
    Seilani, Parisa
    Khajetash, Benyamin
    Hajianfar, Ghasem
    Tavakoli, Meysam
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2024,
  • [29] Radiomics-based machine learning models to predict progression and biomarker status in non-small cell lung cancer (NSCLC) patients treated with immunotherapy
    Yu, Jisang
    Velichko, Yury
    Kim, Hyeonseon
    Soliman, Moataz
    Gennnaro, Nicolo
    Kim, Leeseul
    Oh, Youjin
    Djunadi, Trie Arni
    Lee, Jeeyeon
    Chung, Liam Il-Young
    Yoon, Sungmi
    Shah, Zunairah
    Lee, Soowon
    Nam, Cecilia
    Hong, Timothy
    Agrawal, Rishi
    Aouad, Pascale
    Chae, Young Kwang
    CANCER RESEARCH, 2023, 83 (07)
  • [30] The unique CARWL score stratifies locally advanced nasopharyngeal cancer patients receiving concurrent chemoradiotherapy into risk groups for radiation-induced trismus
    Senyurek, Sukran
    Somay, Efsun
    Durankus, Nilufer Kilic
    Bascil, Sibel
    Ozturk, Duriye
    Selek, Ugur
    Topkan, Erkan
    DISCOVER ONCOLOGY, 2024, 15 (01)