Multiparametric MRI along with machine learning informs on molecular underpinnings, prognosis, and treatment response in pediatric low grade glioma

被引：0
|
作者：
Nabavizadeh, Ali
Kraya, Adam
Rathi, Komal
Kim, Meen Chul
Khalili, Nastaran
Familiar, Ariana
Kesherwani, Varun
Anderson, Hannah
Gandhi, Deep
Mahtabfar, Aria
Bagheri, Sina
Huang, Xiaoyan
Zhu, Yuankun
Sickler, Alexander
Phul, Saksham
Mueller, Sabine
Koptyra, Mateusz P.
Storm, Philip B.
Ware, Jeff
Song, Yuanquan
Fisher, Michael J.
Vossough, Arastoo
Davatzikos, Christos
Resnick, Adam C.
Kazerooni, Anahita Fathi
机构：
来源：
CANCER RESEARCH | 2024年 / 84卷 / 06期
关键词：
D O I：
10.1158/1538-7445.AM2024-5200
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
5200
引用
收藏
页数：2
相关论文
共 32 条
  • [21] RADIOMIC-BASED RISK STRATIFICATION OF PEDIATRIC LOW-GRADE GLIOMA REVEALS DIFFERENTIALLY EXPRESSED MOLECULAR PROCESSES CONTRIBUTING TO VARIABLE PATIENT PROGNOSIS
    Kazerooni, Anahita Fathi
    Kim, Meen Chul
    Kraya, Adam
    Mahtabfar, Aria
    Haldar, Debanjan
    Rathi, Komal
    Khalili, Nastaran
    Familiar, Ariana
    Ware, Jeffrey B.
    Bagheri, Sina
    Anderson, Hannah
    Storm, Phillip B.
    Resnick, Adam C.
    Vossough, Arastoo
    Davatzikos, Christos
    Nabavizadeh, Ali
    NEURO-ONCOLOGY, 2023, 25
  • [22] RADIOMICS AND MACHINE LEARNING ON [18F]FET PET AND T1CE MRI DISCRIMINATE BETWEEN LOW-GRADE AND HIGH-GRADE GLIOMA
    Bonte, S.
    Donche, S.
    Henrotte, M.
    van Holen, R.
    Goethals, I.
    NEURO-ONCOLOGY, 2018, 20 : 226 - 226
  • [23] Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location
    Namdar, Khashayar
    Wagner, Matthias W.
    Kudus, Kareem
    Hawkins, Cynthia
    Tabori, Uri
    Ertl-Wagner, Birgit B.
    Khalvati, Farzad
    CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES, 2025, 76 (02): : 313 - 323
  • [24] Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
    Ubaldi, Leonardo
    Saponaro, Sara
    Giuliano, Alessia
    Talamonti, Cinzia
    Retico, Alessandra
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2023, 107
  • [25] POOR RESPONSE TO MEKI RE-INITIATION IN PEDIATRIC LOW-GRADE GLIOMA AFTER TREATMENT DISCONTINUATION. A PLGG TASKFORCE STUDY
    Bajin, Inci Yaman
    Siddaway, Robert
    Levine, Adrian
    Mckeown, Tara
    Nobre, Liana
    Das, Anirban
    Bennett, Julie
    Ramaswamy, Vijay
    Huang, Annie
    Bouffet, Eric
    Lafay-Cousin, Lucie
    Quang, Dong Anh Khuong
    Hansford, Jordan
    Rajagopal, Revathi
    Gottardo, Nicholas G.
    Rowe, Rachel
    Dutton, Jordann
    Li, Amanda
    Mcthenia, Sheila
    Fangusaro, Jason
    Perreault, Sebastien
    Hawkins, Cynthia
    Tabori, Uri
    NEURO-ONCOLOGY, 2024, 26
  • [26] GANGLIOGLIOMA IN CHILDREN AND ADOLESCENTS - NATURAL COURSE AND RESPONSE TO TREATMENT IN 193 PATIENTS OF THE LOW GRADE GLIOMA (LGG) STUDIES OF THE GERMAN SOCIETY OF PEDIATRIC ONCOLOGY/HEMATOLOGY
    Kandels, Daniela
    Schmidt, Rene
    Geh, Marina
    Breitmoser-Greiner, Sabine
    Gnekow, Astrid K.
    NEURO-ONCOLOGY, 2014, 16 : 62 - 62
  • [27] Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers
    Haldar, Debanjan
    Kazerooni, Anahita Fathi
    Arif, Sherjeel
    Familiar, Ariana
    Madhogarhia, Rachel
    Khalili, Nastaran
    Bagheri, Sina
    Anderson, Hannah
    Shaikh, Ibraheem Salman
    Mahtabfar, Aria
    Kim, Meen Chul
    Tu, Wenxin
    Ware, Jefferey
    Vossough, Arastoo
    Davatzikos, Christos
    Storm, Phillip B.
    Resnick, Adam
    Nabavizadeh, Ali
    NEOPLASIA, 2023, 36
  • [28] Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging
    Yae Won Park
    Sung Soo Ahn
    Ju Hyung Moon
    Eui Hyun Kim
    Seok-Gu Kang
    Jong Hee Chang
    Se Hoon Kim
    Seung-Koo Lee
    Neuroradiology, 2021, 63 : 1811 - 1822
  • [29] Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging
    Park, Yae Won
    Ahn, Sung Soo
    Moon, Ju Hyung
    Kim, Eui Hyun
    Kang, Seok-Gu
    Chang, Jong Hee
    Kim, Se Hoon
    Lee, Seung-Koo
    NEURORADIOLOGY, 2021, 63 (11) : 1811 - 1822
  • [30] Machine learning-based screening of an epithelial-mesenchymal transition-related long non-coding RNA signature reveals lower-grade glioma prognosis and the tumor microenvironment and predicts antitumor therapy response
    Wang, Nan
    Gao, Xin
    Ji, Hang
    Ma, Shuai
    Wu, Jiasheng
    Dong, Jiawei
    Wang, Fang
    Zhao, Hongtao
    Liu, Zhihui
    Yan, Xiuwei
    Li, Bo
    Du, Jianyang
    Zhang, Jiheng
    Hu, Shaoshan
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2022, 9
1234