Literature DB >> 29519794

Whole-Tumor Histogram and Texture Analyses of DTI for Evaluation of IDH1-Mutation and 1p/19q-Codeletion Status in World Health Organization Grade II Gliomas.

Y W Park1,2, K Han2, S S Ahn3, Y S Choi2, J H Chang4, S H Kim5, S-G Kang4, E H Kim4, S-K Lee2.   

Abstract

BACKGROUND AND
PURPOSE: Prediction of the isocitrate dehydrogenase 1 (IDH1)-mutation and 1p/19q-codeletion status of World Health Organization grade ll gliomas preoperatively may assist in predicting prognosis and planning treatment strategies. Our aim was to characterize the histogram and texture analyses of apparent diffusion coefficient and fractional anisotropy maps to determine IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas.
MATERIALS AND METHODS: Ninety-three patients with World Health Organization grade II gliomas with known IDH1-mutation and 1p/19q-codeletion status (18 IDH1 wild-type, 45 IDH1 mutant and no 1p/19q codeletion, 30 IDH1-mutant and 1p/19q codeleted tumors) underwent DTI. ROIs were drawn on every section of the T2-weighted images and transferred to the ADC and the fractional anisotropy maps to derive volume-based data of the entire tumor. Histogram and texture analyses were correlated with the IDH1-mutation and 1p/19q-codeletion status. The predictive powers of imaging features for IDH1 wild-type tumors and 1p/19q-codeletion status in IDH1-mutant subgroups were evaluated using the least absolute shrinkage and selection operator.
RESULTS: Various histogram and texture parameters differed significantly according to IDH1-mutation and 1p/19q-codeletion status. The skewness and energy of ADC, 10th and 25th percentiles, and correlation of fractional anisotropy were independent predictors of an IDH1 wild-type in the least absolute shrinkage and selection operator. The area under the receiver operating curve for the prediction model was 0.853. The skewness and cluster shade of ADC, energy, and correlation of fractional anisotropy were independent predictors of a 1p/19q codeletion in IDH1-mutant tumors in the least absolute shrinkage and selection operator. The area under the receiver operating curve was 0.807.
CONCLUSIONS: Whole-tumor histogram and texture features of the ADC and fractional anisotropy maps are useful for predicting the IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas.
© 2018 by American Journal of Neuroradiology.

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Year:  2018        PMID: 29519794     DOI: 10.3174/ajnr.A5569

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


  22 in total

1.  Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging.

Authors:  Eric Aliotta; Prem P Batchala; David Schiff; Beatriz M Lopes; Jason T Druzgal; Sugoto Mukherjee; Sohil H Patel
Journal:  J Neurooncol       Date:  2019-09-17       Impact factor: 4.130

2.  Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI.

Authors:  E Aliotta; H Nourzadeh; P P Batchala; D Schiff; M B Lopes; J T Druzgal; S Mukherjee; S H Patel
Journal:  AJNR Am J Neuroradiol       Date:  2019-08-14       Impact factor: 3.825

Review 3.  Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.

Authors:  Reza Forghani
Journal:  Radiol Imaging Cancer       Date:  2020-07-31

4.  Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status.

Authors:  Yae Won Park; Sooyon Kim; Chae Jung Park; Sung Soo Ahn; Kyunghwa Han; Seok-Gu Kang; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Eur Radiol       Date:  2022-06-28       Impact factor: 5.315

5.  Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas.

Authors:  P P Batchala; T J E Muttikkal; J H Donahue; J T Patrie; D Schiff; C E Fadul; E K Mrachek; M-B Lopes; R Jain; S H Patel
Journal:  AJNR Am J Neuroradiol       Date:  2019-01-31       Impact factor: 3.825

6.  Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

Authors:  Yae Won Park; Jongmin Oh; Seng Chan You; Kyunghwa Han; Sung Soo Ahn; Yoon Seong Choi; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Eur Radiol       Date:  2018-11-15       Impact factor: 5.315

7.  Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.

Authors:  Chae Jung Park; Yoon Seong Choi; Yae Won Park; Sung Soo Ahn; Seok-Gu Kang; Jong-Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Neuroradiology       Date:  2019-12-09       Impact factor: 2.804

8.  Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.

Authors:  Minjae Kim; So Yeong Jung; Ji Eun Park; Yeongheun Jo; Seo Young Park; Soo Jung Nam; Jeong Hoon Kim; Ho Sung Kim
Journal:  Eur Radiol       Date:  2019-12-11       Impact factor: 5.315

9.  Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma.

Authors:  Mustafa Yildirim; Murat Baykara
Journal:  Acta Neurol Belg       Date:  2021-02-08       Impact factor: 2.396

Review 10.  The application of radiomics in predicting gene mutations in cancer.

Authors:  Yana Qi; Tingting Zhao; Mingyong Han
Journal:  Eur Radiol       Date:  2022-01-20       Impact factor: 5.315

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