Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

被引:25
作者
Pak, Elena [1 ]
Choi, Kyu Sung [1 ]
Choi, Seung Hong [1 ,4 ,5 ]
Park, Chul-Kee [6 ,7 ]
Kim, Tae Min [8 ]
Park, Sung-Hye [2 ]
Lee, Joo Ho [9 ]
Lee, Soon-Tae [3 ]
Hwang, Inpyeong [1 ]
Yoo, Roh-Eul [1 ]
Kang, Koung Mi [1 ]
Yun, Tae Jin [1 ]
Kim, Ji-Hoon [1 ]
Sohn, Chul-Ho [1 ]
机构
[1] Seoul Natl Univ Hosp, Dept Radiol, 101 Daehak Ro, Seoul 03080, South Korea
[2] Seoul Natl Univ Hosp, Dept Pathol, Seoul, South Korea
[3] Seoul Natl Univ Hosp, Dept Neurol, Seoul, South Korea
[4] Seoul Natl Univ, Ctr Nanoparticle Res, Inst Basic Sci, Seoul, South Korea
[5] Seoul Natl Univ, Sch Chem & Biol Engn, Seoul, South Korea
[6] Seoul Natl Univ Hosp, Dept Neurosurg, Seoul, South Korea
[7] Seoul Natl Univ Hosp, Biomed Res Inst, Seoul, South Korea
[8] Seoul Natl Univ Hosp, Canc Res Inst, Dept Internal Med, Seoul, South Korea
[9] Seoul Natl Univ Hosp, Canc Res Inst, Dept Radiat Oncol, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Glioblastoma; Progression; Dynamic contrast enhanced MRI; K-trans; V-e; V-p; Radiomics; SIGNAL-INTENSITY LESIONS; STANDARD TREATMENT; SURVIVAL; DIMENSIONALITY; TEMOZOLOMIDE; DIAGNOSIS;
D O I
10.3348/kjr.2020.1433
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: To develop a radiomics risk score based on dynamic contrast-enhanced (OCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age +/- standard deviation, 60.5 +/- 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K-trans), fractional volume of vascular plasma space (V-p), and fractional volume of extravascular extracellular space (V-s) maps of OCE MRI, wherein the regions of interest were based on both T1weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (ION) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the "radiomics risk score" from the features of OCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.
引用
收藏
页码:1514 / 1524
页数:11
相关论文
共 50 条
  • [41] Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma
    Zhao, Ying
    Wang, Nan
    Wu, Jingjun
    Zhang, Qinhe
    Lin, Tao
    Yao, Yu
    Chen, Zhebin
    Wang, Man
    Sheng, Liuji
    Liu, Jinghong
    Song, Qingwei
    Wang, Feng
    An, Xiangbo
    Guo, Yan
    Li, Xin
    Wu, Tingfan
    Liu, Ai Lian
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [42] Early Prediction of Response to Neoadjuvant Chemotherapy Using Dynamic Contrast-Enhanced MRI and Ultrasound in Breast Cancer
    Kim, Yunju
    Kim, Sung Hun
    Song, Byung Joo
    Kang, Bong Joo
    Yim, Kwang-il
    Lee, Ahwon
    Nam, Yoonho
    KOREAN JOURNAL OF RADIOLOGY, 2018, 19 (04) : 682 - 691
  • [43] A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma
    Zhu, Yuemin
    Weng, Shuping
    Li, Yueming
    Yan, Chuan
    Ye, Rongping
    Wen, Liting
    Zhou, Lili
    Gao, Lanmei
    ABDOMINAL RADIOLOGY, 2021, 46 (07) : 3139 - 3148
  • [44] Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer
    Deling Song
    Fei Yang
    Yujiao Zhang
    Yazhe Guo
    Yingwu Qu
    Xiaochen Zhang
    Yuexiang Zhu
    Shujun Cui
    Cancer Imaging, 22
  • [45] Cluster Analysis of DSC MRI, Dynamic Contrast-Enhanced MRI, and DWI Parameters Associated with Prognosis in Patients with Glioblastoma after Removal of the Contrast-Enhancing Component: A Preliminary Study
    Chung, H.
    Seo, H.
    Choi, S. H.
    Park, C. -k.
    Kim, T. M.
    Park, S. -h.
    Won, J. K.
    Lee, J. H.
    Lee, S. -t.
    Lee, J. Y.
    Hwang, I.
    Kang, K. M.
    Yun, T. J.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2022, 43 (11) : 1559 - 1566
  • [46] Contrast-enhanced mammography in comparison with dynamic contrast-enhanced MRI: which modality is appropriate for whom?
    Rasha Kamal
    Sahar Mansour
    Amr Farouk
    Mennatallah Hanafy
    Ahmed Elhatw
    Mohammed Mohammed Goma
    Egyptian Journal of Radiology and Nuclear Medicine, 52
  • [47] Contrast-enhanced mammography in comparison with dynamic contrast-enhanced MRI: which modality is appropriate for whom?
    Kamal, Rasha
    Mansour, Sahar
    Farouk, Amr
    Hanafy, Mennatallah
    Elhatw, Ahmed
    Goma, Mohammed Mohammed
    EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, 2021, 52 (01)
  • [48] Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis
    Xiao, Jennifer
    Rahbar, Habib
    Hippe, Daniel S.
    Rendi, Mara H.
    Parker, Elizabeth U.
    Shekar, Neal
    Hirano, Michael
    Cheung, Kevin J.
    Partridge, Savannah C.
    NPJ BREAST CANCER, 2021, 7 (01)
  • [49] Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer
    Song, Deling
    Yang, Fei
    Zhang, Yujiao
    Guo, Yazhe
    Qu, Yingwu
    Zhang, Xiaochen
    Zhu, Yuexiang
    Cui, Shujun
    CANCER IMAGING, 2022, 22 (01)
  • [50] Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
    Zhang, Di
    Wei, Qi
    Wu, Ge-Ge
    Zhang, Xian-Ya
    Lu, Wen-Wu
    Lv, Wen-Zhi
    Liao, Jin-Tang
    Cui, Xin-Wu
    Ni, Xue-Jun
    Dietrich, Christoph F.
    FRONTIERS IN ONCOLOGY, 2021, 11