Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas

被引：0
|
作者：
Zhen Zhao
Chuansheng Nie
Lei Zhao
Dongdong Xiao
Jianglin Zheng
Hao Zhang
Pengfei Yan
Xiaobing Jiang
Hongyang Zhao
机构：
[1] Union Hospital,Department of Neurosurgery
[2] Tongji Medical College,Department of Geriatric Medicine
[3] Huazhong University of Science and Technology,undefined
[4] International Education College of Henan University,undefined
[5] Union Hospital,undefined
[6] Tongji Medical College,undefined
[7] Huazhong University of Science and Technology,undefined
来源：
European Radiology | 2024年 / 34卷
关键词：
Meningioma; WHO grading; Radiomics; Machine learning; Nomogram;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：2468 / 2479
页数：11
相关论文
共 50 条
  • [41] Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI
    Shi, Liming
    Zhang, Yang
    Nie, Ke
    Sun, Xiaonan
    Niu, Tianye
    Yue, Ning
    Kwong, Tiffany
    Chang, Peter
    Chow, Daniel
    Chen, Jeon-Hor
    Su, Min-Ying
    MAGNETIC RESONANCE IMAGING, 2019, 61 : 33 - 40
  • [42] Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features
    Li, Xiaofen
    Zhang, Jingkun
    Leng, Yinping
    Liu, Jiaqi
    Li, Linlin
    Wan, Tianyi
    Dong, Wentao
    Fan, Bing
    Gong, Lianggeng
    BMC MEDICAL IMAGING, 2024, 24 (01):
  • [43] Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI
    Li, Chunyu
    Deng, Ming
    Zhong, Xiaoli
    Ren, Jinxia
    Chen, Xiaohui
    Chen, Jun
    Xiao, Feng
    Xu, Haibo
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [44] Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer
    Zhang, Yongsheng
    Chen, Wen
    Yue, Xianjie
    Shen, Jianliang
    Gao, Chen
    Pang, Peipei
    Cui, Feng
    Xu, Maosheng
    FRONTIERS IN ONCOLOGY, 2020, 10
  • [45] Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics
    Zhang, Bin
    Ouyang, Fusheng
    Gu, Dongsheng
    Dong, Yuhao
    Zhang, Lu
    Mo, Xiaokai
    Huang, Wenhui
    Zhang, Shuixing
    ONCOTARGET, 2017, 8 (42) : 72457 - 72465
  • [46] Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics
    Jian, Lian
    Sheng, Cai
    Liu, Huaping
    Li, Handong
    Hu, Pingsheng
    Ai, Zhaodong
    Yu, Xiaoping
    Liu, Huai
    BMC CANCER, 2025, 25 (01)
  • [47] MRI-Based Machine Learning for Prediction of Clinical Outcomes in Primary Central Nervous System Lymphoma
    Ko, Ching-Chung
    Liu, Yan-Lin
    Hung, Kuo-Chuan
    Yang, Cheng-Chun
    Lim, Sher-Wei
    Yeh, Lee-Ren
    Chen, Jeon-Hor
    Su, Min-Ying
    LIFE-BASEL, 2024, 14 (10):
  • [48] Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics
    Zhang, Yang
    Shu, Zhenyu
    Ye, Qin
    Chen, Junfa
    Zhong, Jianguo
    Jiang, Hongyang
    Wu, Cuiyun
    Yu, Taihen
    Pang, Peipei
    Ma, Tianshi
    Lin, Chunmiao
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [49] Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning
    Zhang C.
    Heng X.
    Neng W.
    Chen H.
    Sun A.
    Li J.
    Wang M.
    Chinese Neurosurgical Journal, 8 (1)
  • [50] Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning
    Lorenzo Ugga
    Renato Cuocolo
    Domenico Solari
    Elia Guadagno
    Alessandra D’Amico
    Teresa Somma
    Paolo Cappabianca
    Maria Laura del Basso de Caro
    Luigi Maria Cavallo
    Arturo Brunetti
    Neuroradiology, 2019, 61 : 1365 - 1373
12345