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
  • [1] Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI
    Shim, Ka Young
    Chung, Sung Won
    Jeong, Jae Hak
    Hwang, Inpyeong
    Park, Chul-Kee
    Kim, Tae Min
    Park, Sung-Hye
    Won, Jae Kyung
    Lee, Joo Ho
    Lee, Soon-Tae
    Yoo, Roh-Eul
    Kang, Koung Mi
    Yun, Tae Jin
    Kim, Ji-Hoon
    Sohn, Chul-Ho
    Choi, Kyu Sung
    Choi, Seung Hong
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [2] The Initial Area Under the Curve Derived from Dynamic Contrast-Enhanced MRI Improves Prognosis Prediction in Glioblastoma with Unmethylated MGMT Promoter
    Choi, Y. S.
    Ahn, S. S.
    Lee, H. -J.
    Chang, J. H.
    Kang, S. -G.
    Kim, E. H.
    Kim, S. H.
    Lee, S. -K.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2017, 38 (08) : 1528 - 1535
  • [3] Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN
    Yoshimura, Hisanori
    Kawahara, Daisuke
    Saito, Akito
    Ozawa, Shuichi
    Nagata, Yasushi
    PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2024, 47 (03) : 1227 - 1243
  • [4] Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype
    Sohn, Beomseok
    Park, Kisung
    Ahn, Sung Soo
    Park, Yae Won
    Choi, Seung Hong
    Kang, Seok-Gu
    Kim, Se Hoon
    Chang, Jong Hee
    Lee, Seung-Koo
    JOURNAL OF NEURO-ONCOLOGY, 2023, 164 (02) : 341 - 351
  • [5] Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI
    Yoshida, Kotaro
    Kawashima, Hiroko
    Kannon, Takayuki
    Tajima, Atsushi
    Ohno, Naoki
    Terada, Kanako
    Takamatsu, Atsushi
    Adachi, Hayato
    Ohno, Masako
    Miyati, Tosiaki
    Ishikawa, Satoko
    Ikeda, Hiroko
    Gabata, Toshifumi
    MAGNETIC RESONANCE IMAGING, 2022, 92 : 19 - 25
  • [6] Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study
    Mirshahvalad, Seyed Ali
    Dias, Adriano B.
    Ghai, Sangeet
    Ortega, Claudia
    Perlis, Nathan
    Berlin, Alejandro
    Avery, Lisa
    van der Kwast, Theodorus
    Metser, Ur
    Veit-Haibach, Patrick
    ACADEMIC RADIOLOGY, 2025, 32 (01) : 250 - 259
  • [7] Prognostic Prediction Based on Dynamic Contrast-Enhanced MRI and Dynamic Susceptibility Contrast-Enhanced MRI Parameters from Non-Enhancing, T2-High-Signal-Intensity Lesions in Patients with Glioblastoma
    Jo, Sang Won
    Choi, Seung Hong
    Lee, Eun Jung
    Yoo, Roh Eul
    Kang, Koung Mi
    Yun, Tae Jin
    Kim, Ji Hoon
    Sohn, Chul Ho
    KOREAN JOURNAL OF RADIOLOGY, 2021, 22 (08) : 1369 - 1378
  • [8] Evaluation of dynamic contrast-enhanced MRI derived microvascular permeability in recurrent glioblastoma treated with bevacizumab
    Kickingereder, Philipp
    Wiestler, Benedikt
    Graf, Markus
    Heiland, Sabine
    Schlemmer, Heinz Peter
    Wick, Wolfgang
    Wick, Antje
    Bendszus, Martin
    Radbruch, Alexander
    JOURNAL OF NEURO-ONCOLOGY, 2015, 121 (02) : 373 - 380
  • [9] Deep Learning Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma
    Dong, Xue
    Yang, Jiawen
    Zhang, Binhao
    Li, Yujing
    Wang, Guanliang
    Chen, Jinyao
    Wei, Yuguo
    Zhang, Huangqi
    Chen, Qingqing
    Jin, Shengze
    Wang, Lingxia
    He, Haiqing
    Gan, Meifu
    Ji, Wenbin
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2023, : 108 - 119
  • [10] Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity
    Lin, Qiao
    Ji, Yi-fan
    Chen, Yong
    Sun, Huan
    Yang, Dan-dan
    Chen, Ai-li
    Chen, Tian-wu
    Zhang, Xiao Ming
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 51 (02) : 397 - 406