Literature DB >> 31456318

Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review.

Anahita Fathi Kazerooni1,2, Spyridon Bakas1,2,3, Hamidreza Saligheh Rad4, Christos Davatzikos1,2.   

Abstract

Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  glioblastoma; machine learning; magnetic resonance imaging; molecular signatures; radiogenomics; radiomics

Mesh:

Year:  2019        PMID: 31456318      PMCID: PMC7457548          DOI: 10.1002/jmri.26907

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  129 in total

1.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.

Authors:  Philipp Kickingereder; David Bonekamp; Martha Nowosielski; Annekathrin Kratz; Martin Sill; Sina Burth; Antje Wick; Oliver Eidel; Heinz-Peter Schlemmer; Alexander Radbruch; Jürgen Debus; Christel Herold-Mende; Andreas Unterberg; David Jones; Stefan Pfister; Wolfgang Wick; Andreas von Deimling; Martin Bendszus; David Capper
Journal:  Radiology       Date:  2016-09-16       Impact factor: 11.105

2.  Magnetic resonance imaging characteristics predict epidermal growth factor receptor amplification status in glioblastoma.

Authors:  Manish Aghi; Paola Gaviani; John W Henson; Tracy T Batchelor; David N Louis; Fred G Barker
Journal:  Clin Cancer Res       Date:  2005-12-15       Impact factor: 12.531

3.  Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers.

Authors:  Rajan Jain; Laila Poisson; Jayant Narang; David Gutman; Lisa Scarpace; Scott N Hwang; Chad Holder; Max Wintermark; Rivka R Colen; Justin Kirby; John Freymann; Daniel J Brat; Carl Jaffe; Tom Mikkelsen
Journal:  Radiology       Date:  2012-12-13       Impact factor: 11.105

4.  Probabilistic radiographic atlas of glioblastoma phenotypes.

Authors:  B M Ellingson; A Lai; R J Harris; J M Selfridge; W H Yong; K Das; W B Pope; P L Nghiemphu; H V Vinters; L M Liau; P S Mischel; T F Cloughesy
Journal:  AJNR Am J Neuroradiol       Date:  2012-09-20       Impact factor: 3.825

5.  Inhibition of Nuclear PTEN Tyrosine Phosphorylation Enhances Glioma Radiation Sensitivity through Attenuated DNA Repair.

Authors:  Jianhui Ma; Jorge A Benitez; Jie Li; Shunichiro Miki; Claudio Ponte de Albuquerque; Thais Galatro; Laura Orellana; Ciro Zanca; Rachel Reed; Antonia Boyer; Tomoyuki Koga; Nissi M Varki; Tim R Fenton; Suely Kazue Nagahashi Marie; Erik Lindahl; Timothy C Gahman; Andrew K Shiau; Huilin Zhou; John DeGroot; Erik P Sulman; Webster K Cavenee; Richard D Kolodner; Clark C Chen; Frank B Furnari
Journal:  Cancer Cell       Date:  2019-02-28       Impact factor: 31.743

6.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

Authors:  Zhi-Cheng Li; Hongmin Bai; Qiuchang Sun; Qihua Li; Lei Liu; Yan Zou; Yinsheng Chen; Chaofeng Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-03-21       Impact factor: 5.315

7.  Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma.

Authors:  Yi Cui; Shangjie Ren; Khin Khin Tha; Jia Wu; Hiroki Shirato; Ruijiang Li
Journal:  Eur Radiol       Date:  2017-02-06       Impact factor: 5.315

8.  Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging.

Authors:  Ramon F Barajas; J Graeme Hodgson; Jamie S Chang; Scott R Vandenberg; Ru-Fang Yeh; Andrew T Parsa; Michael W McDermott; Mitchel S Berger; William P Dillon; Soonmee Cha
Journal:  Radiology       Date:  2010-02       Impact factor: 11.105

9.  Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma.

Authors:  Doo-Sik Kong; Jinkuk Kim; In-Hee Lee; Sung Tae Kim; Ho Jun Seol; Jung-Il Lee; Woong-Yang Park; Gyuha Ryu; Zichen Wang; Avi Ma'ayan; Do-Hyun Nam
Journal:  Oncotarget       Date:  2016-03-08

10.  Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma.

Authors:  Patrick Grossmann; David A Gutman; William D Dunn; Chad A Holder; Hugo J W L Aerts
Journal:  BMC Cancer       Date:  2016-08-08       Impact factor: 4.430

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  24 in total

1.  iGLASS: imaging integration into the Glioma Longitudinal Analysis Consortium.

Authors:  Spyridon Bakas; David Ryan Ormond; Kristin D Alfaro-Munoz; Marion Smits; Lee Alex Donald Cooper; Roel Verhaak; Laila M Poisson
Journal:  Neuro Oncol       Date:  2020-10-14       Impact factor: 12.300

2.  Analyzing magnetic resonance imaging data from glioma patients using deep learning.

Authors:  Bjoern Menze; Fabian Isensee; Roland Wiest; Bene Wiestler; Klaus Maier-Hein; Mauricio Reyes; Spyridon Bakas
Journal:  Comput Med Imaging Graph       Date:  2020-12-02       Impact factor: 4.790

3.  Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities.

Authors:  Spyridon Bakas; Gaurav Shukla; Hamed Akbari; Guray Erus; Aristeidis Sotiras; Saima Rathore; Chiharu Sako; Sung Min Ha; Martin Rozycki; Russell T Shinohara; Michel Bilello; Christos Davatzikos
Journal:  J Med Imaging (Bellingham)       Date:  2020-06-09

4.  Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas.

Authors:  Omaditya Khanna; Anahita Fathi Kazerooni; Christopher J Farrell; Michael P Baldassari; Tyler D Alexander; Michael Karsy; Benjamin A Greenberger; Jose A Garcia; Chiharu Sako; James J Evans; Kevin D Judy; David W Andrews; Adam E Flanders; Ashwini D Sharan; Adam P Dicker; Wenyin Shi; Christos Davatzikos
Journal:  Neurosurgery       Date:  2021-10-13       Impact factor: 5.315

Review 5.  Radiomics and radiogenomics in pediatric neuro-oncology: A review.

Authors:  Rachel Madhogarhia; Debanjan Haldar; Sina Bagheri; Ariana Familiar; Hannah Anderson; Sherjeel Arif; Arastoo Vossough; Phillip Storm; Adam Resnick; Christos Davatzikos; Anahita Fathi Kazerooni; Ali Nabavizadeh
Journal:  Neurooncol Adv       Date:  2022-05-27

6.  Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma.

Authors:  Lei Wei; Chun Zou; Liechun Chen; Yan Lin; Lucong Liang; Beiquan Hu; Yingwei Mao; Donghua Zou
Journal:  Front Mol Biosci       Date:  2022-04-29

Review 7.  Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas.

Authors:  Jayapalli Rajiv Bapuraj; Nicholas Wang; Ashok Srinivasan; Arvind Rao
Journal:  Cancer J       Date:  2021 Sep-Oct 01       Impact factor: 3.360

8.  Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging.

Authors:  Hamed Akbari; Anahita Fathi Kazerooni; Jeffrey B Ware; Elizabeth Mamourian; Hannah Anderson; Samantha Guiry; Chiharu Sako; Catalina Raymond; Jingwen Yao; Steven Brem; Donald M O'Rourke; Arati S Desai; Stephen J Bagley; Benjamin M Ellingson; Christos Davatzikos; Ali Nabavizadeh
Journal:  Sci Rep       Date:  2021-07-22       Impact factor: 4.379

Review 9.  Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures.

Authors:  Dongming Liu; Jiu Chen; Xinhua Hu; Kun Yang; Yong Liu; Guanjie Hu; Honglin Ge; Wenbin Zhang; Hongyi Liu
Journal:  Front Oncol       Date:  2021-07-06       Impact factor: 6.244

10.  TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.

Authors:  Mario Zanfardino; Katia Pane; Peppino Mirabelli; Marco Salvatore; Monica Franzese
Journal:  Int J Mol Sci       Date:  2019-11-29       Impact factor: 5.923

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