The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression

被引:2
|
作者
Fu, Fang-Xiong [1 ,2 ]
Cai, Qin-Lei [1 ]
Li, Guo [1 ]
Wu, Xiao-Jing [1 ]
Hong, Lan [3 ]
Chen, Wang-Sheng [1 ]
机构
[1] Hainan Med Univ, Hainan Affiliated Hosp, Hainan Gen Hosp, Dept Radiol, Haikou 570311, Hainan, Peoples R China
[2] Shenzhen Longhua Dist Cent Hosp, Dept Radiol, Shenzhen 518110, Peoples R China
[3] Hainan Med Univ, Dept Gynecol, Hainan Gen Hosp, Hainan Affiliated Hosp, 19 Xiuhua St, Haikou 570311, Peoples R China
基金
中国国家自然科学基金;
关键词
Glioma; Imageomics; Recurrence; Pseudoprogression; Magnetic resonance imaging; DIFFERENTIATION; DIFFUSION;
D O I
10.1016/j.mri.2024.05.003
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
Objective: The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner. Methods: A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy. Results: Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93-1). Conclusion: The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.
引用
收藏
页码:168 / 178
页数:13
相关论文
共 50 条
  • [21] Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma
    Li, Qi
    Li, Xuezhou
    Liu, Wenbin
    Yu, Jieyu
    Chen, Yukun
    Zhu, Mengmeng
    Li, Na
    Liu, Fang
    Wang, Tiegong
    Fang, Xu
    Li, Jing
    Lu, Jianping
    Shao, Chengwei
    Bian, Yun
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [22] Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion
    Liu, Yang
    INTERNATIONAL JOURNAL OF GENERAL MEDICINE, 2023, 16 : 3043 - 3051
  • [23] Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer
    Song, Xiao-Li
    Luo, Hong-Jian
    Ren, Jia-Liang
    Yin, Ping
    Liu, Ying
    Niu, Jinliang
    Hong, Nan
    RADIOLOGIA MEDICA, 2023, 128 (02): : 242 - 251
  • [24] Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer
    Wu, Feng
    Zhang, Rui
    Li, Feng
    Qin, Xiaomin
    Xing, Hui
    Lv, Huabing
    Li, Lin
    Ai, Tao
    FRONTIERS IN MEDICINE, 2024, 11
  • [25] Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer
    Xiao-Li Song
    Hong-Jian Luo
    Jia-Liang Ren
    Ping Yin
    Ying Liu
    Jinliang Niu
    Nan Hong
    La radiologia medica, 2023, 128 : 242 - 251
  • [26] Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: a retrospective study
    Du, Juan
    Yang, Linsha
    Zheng, Tao
    Liu, Defeng
    RADIOLOGIE, 2024, 64 (SUPPL 1): : 166 - 176
  • [27] Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma
    Chen, Hongyu
    Lin, Fuhua
    Zhang, Jinming
    Lv, Xiaofei
    Zhou, Jian
    Li, Zhi-Cheng
    Chen, Yinsheng
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [28] Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning
    Lam, Luu Ho Thanh
    Do, Duyen Thi
    Diep, Doan Thi Ngoc
    Nguyet, Dang Le Nhu
    Truong, Quang Dinh
    Tri, Tran Thanh
    Thanh, Huynh Ngoc
    Le, Nguyen Quoc Khanh
    NMR IN BIOMEDICINE, 2022, 35 (11)
  • [29] Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression
    Yu, Yang
    Ma, Yue
    Sun, Mengyao
    Jiang, Wenyan
    Yuan, Tingting
    Tong, Dan
    MEDICINE, 2020, 99 (23)
  • [30] Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model
    Kai Wang
    Zhen Qiao
    Xiaobin Zhao
    Xiaotong Li
    Xin Wang
    Tingfan Wu
    Zhongwei Chen
    Di Fan
    Qian Chen
    Lin Ai
    European Journal of Nuclear Medicine and Molecular Imaging, 2020, 47 : 1400 - 1411