Literature DB >> 31122918

Texture Analysis in Cerebral Gliomas: A Review of the Literature.

N Soni1, S Priya2, G Bathla1.   

Abstract

Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging.
© 2019 by American Journal of Neuroradiology.

Entities:  

Mesh:

Year:  2019        PMID: 31122918     DOI: 10.3174/ajnr.A6075

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  27 in total

1.  The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy.

Authors:  Orkun Sarioglu; Fatma Ceren Sarioglu; Ahmet Ergin Capar; Demet Funda Bas Sokmez; Pelin Topkaya; Umit Belet
Journal:  Eur Radiol       Date:  2021-02-09       Impact factor: 5.315

2.  RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.

Authors:  Jacob T Antunes; Marwa Ismail; Imran Hossain; Zhoumengdi Wang; Prateek Prasanna; Anant Madabhushi; Pallavi Tiwari; Satish E Viswanath
Journal:  IEEE J Biomed Health Inform       Date:  2022-06-03       Impact factor: 7.021

3.  Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

Authors:  Sarv Priya; Yanan Liu; Caitlin Ward; Nam H Le; Neetu Soni; Ravishankar Pillenahalli Maheshwarappa; Varun Monga; Honghai Zhang; Milan Sonka; Girish Bathla
Journal:  Sci Rep       Date:  2021-05-18       Impact factor: 4.379

Review 4.  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

5.  Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models.

Authors:  Sarv Priya; Amit Agarwal; Caitlin Ward; Thomas Locke; Varun Monga; Girish Bathla
Journal:  Neuroradiol J       Date:  2021-02-03

6.  Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.

Authors:  Sarv Priya; Caitlin Ward; Thomas Locke; Neetu Soni; Ravishankar Pillenahalli Maheshwarappa; Varun Monga; Amit Agarwal; Girish Bathla
Journal:  Neuroradiol J       Date:  2021-03-03

7.  Multiparametric MRI texture analysis in prediction of glioma biomarker status: added value of MR diffusion.

Authors:  Shingo Kihira; Nadejda M Tsankova; Adam Bauer; Yu Sakai; Keon Mahmoudi; Nicole Zubizarreta; Jane Houldsworth; Fahad Khan; Noriko Salamon; Adilia Hormigo; Kambiz Nael
Journal:  Neurooncol Adv       Date:  2021-04-08

8.  Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Authors:  Sarv Priya; Yanan Liu; Caitlin Ward; Nam H Le; Neetu Soni; Ravishankar Pillenahalli Maheshwarappa; Varun Monga; Honghai Zhang; Milan Sonka; Girish Bathla
Journal:  Cancers (Basel)       Date:  2021-05-24       Impact factor: 6.639

9.  Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.

Authors:  Sarv Priya; Tanya Aggarwal; Caitlin Ward; Girish Bathla; Mathews Jacob; Alicia Gerke; Eric A Hoffman; Prashant Nagpal
Journal:  Sci Rep       Date:  2021-06-16       Impact factor: 4.996

10.  Prediction of Malignant Acute Middle Cerebral Artery Infarction via Computed Tomography Radiomics.

Authors:  Xuehua Wen; Yumei Li; Xiaodong He; Yuyun Xu; Zhenyu Shu; Xingfei Hu; Junfa Chen; Hongyang Jiang; Xiangyang Gong
Journal:  Front Neurosci       Date:  2020-07-07       Impact factor: 4.677

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