Literature DB >> 28079714

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images.

Srishti Abrol1, Aikaterini Kotrotsou, Ahmed Salem, Pascal O Zinn, Rivka R Colen.   

Abstract

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

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Year:  2017        PMID: 28079714     DOI: 10.1097/RMR.0000000000000117

Source DB:  PubMed          Journal:  Top Magn Reson Imaging        ISSN: 0899-3459


  9 in total

1.  Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

Authors:  J Pérez-Beteta; D Molina-García; M Villena; M J Rodríguez; C Velásquez; J Martino; B Meléndez-Asensio; Á Rodríguez de Lope; R Morcillo; J M Sepúlveda; A Hernández-Laín; A Ramos; J A Barcia; P C Lara; D Albillo; A Revert; E Arana; V M Pérez-García
Journal:  AJNR Am J Neuroradiol       Date:  2019-03-28       Impact factor: 3.825

2.  Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.

Authors:  Sara Dastmalchian; Ozden Kilinc; Louisa Onyewadume; Charit Tippareddy; Debra McGivney; Dan Ma; Mark Griswold; Jeffrey Sunshine; Vikas Gulani; Jill S Barnholtz-Sloan; Andrew E Sloan; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-09-26       Impact factor: 9.236

3.  Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics.

Authors:  Bin Zhang; Fusheng Ouyang; Dongsheng Gu; Yuhao Dong; Lu Zhang; Xiaokai Mo; Wenhui Huang; Shuixing Zhang
Journal:  Oncotarget       Date:  2017-08-02

4.  Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma.

Authors:  Nabil Elshafeey; Aikaterini Kotrotsou; Ahmed Hassan; Nancy Elshafei; Islam Hassan; Sara Ahmed; Srishti Abrol; Anand Agarwal; Kamel El Salek; Samuel Bergamaschi; Jay Acharya; Fanny E Moron; Meng Law; Gregory N Fuller; Jason T Huse; Pascal O Zinn; Rivka R Colen
Journal:  Nat Commun       Date:  2019-07-18       Impact factor: 14.919

5.  Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone.

Authors:  Florent Tixier; Hyemin Um; Dalton Bermudez; Aditi Iyer; Aditya Apte; Maya S Graham; Kathryn S Nevel; Joseph O Deasy; Robert J Young; Harini Veeraraghavan
Journal:  Oncotarget       Date:  2019-01-18

6.  Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients.

Authors:  Zi-Zhuo Li; Peng-Fei Liu; Ting-Ting An; Hai-Chao Yang; Wei Zhang; Jia-Xu Wang
Journal:  Transl Oncol       Date:  2021-03-21       Impact factor: 4.243

Review 7.  The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude.

Authors:  Melissa Silva; Catalina Vivancos; Hugues Duffau
Journal:  Brain Sci       Date:  2022-04-15

Review 8.  Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis.

Authors:  Ioannis Tsougos; Alexandros Vamvakas; Constantin Kappas; Ioannis Fezoulidis; Katerina Vassiou
Journal:  Comput Math Methods Med       Date:  2018-09-23       Impact factor: 2.238

9.  The Association between Mortality-to-Incidence Ratios and Health Expenditures in Brain and Nervous System Cancers.

Authors:  Tsung-Han Lee; Wen-Wei Sung; Lung Chan; Hsiang-Lin Lee; Sung-Lang Chen; Yu-Hui Huang; Aij-Lie Kwan
Journal:  Int J Environ Res Public Health       Date:  2019-07-31       Impact factor: 3.390

  9 in total

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