Literature DB >> 30414095

Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status.

Jing Zhao1, Yu-Liang Wang2, Xin-Bei Li3, Man-Shi Hu1, Zhu-Hao Li1, Yu-Kun Song4, Jing-Yan Wang1, Yi-Su Tian1, Da-Wei Liu5, Xu Yan6, Li Jiang1, Zhi-Yun Yang1, Jian-Ping Chu7.   

Abstract

INTRODUCTION: Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results.
METHODS: Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn.
RESULTS: MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (rs = 0.72).
CONCLUSIONS: Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.

Entities:  

Keywords:  Diffusion; Glioma; Isocitrate dehydrogenase; Ki-67 label index; Magnetic resonance imaging

Mesh:

Substances:

Year:  2018        PMID: 30414095     DOI: 10.1007/s11060-018-03025-7

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  12 in total

1.  Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging.

Authors:  Sirui Li; Yuan Zheng; Wenbo Sun; Samo Lasič; Filip Szczepankiewicz; Qing Wei; Shihong Han; Shuheng Zhang; Xiaoli Zhong; Liang Wang; Huan Li; Yuxiang Cai; Dan Xu; Zhiqiang Li; Qiang He; Danielle van Westen; Karin Bryskhe; Daniel Topgaard; Haibo Xu
Journal:  Eur Radiol       Date:  2021-04-29       Impact factor: 5.315

2.  Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

Authors:  Jing Guo; Jialiang Ren; Junkang Shen; Rui Cheng; Yexin He
Journal:  Diagn Interv Radiol       Date:  2021-05       Impact factor: 2.630

Review 3.  Machine Learning-Based Radiomics in Neuro-Oncology.

Authors:  Felix Ehret; David Kaul; Hans Clusmann; Daniel Delev; Julius M Kernbach
Journal:  Acta Neurochir Suppl       Date:  2022

4.  Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters.

Authors:  Zhenxing Huang; Changyu Lu; Gen Li; Zhenye Li; Shengjun Sun; Yazhuo Zhang; Zonggang Hou; Jian Xie
Journal:  Front Oncol       Date:  2021-03-10       Impact factor: 6.244

5.  Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI.

Authors:  Sheng-Hui Xie; Rui Lang; Bo Li; He Zhao; Peng Wang; Jin-Long He; Xue-Ying Ma; Qiong Wu; Shao-Yu Wang; Hua-Peng Zhang; Yang Gao; Jian-Lin Wu
Journal:  Neuroradiology       Date:  2022-07-15       Impact factor: 2.995

6.  The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis.

Authors:  Gehad Abdalla; Luke Dixon; Eser Sanverdi; Pedro M Machado; Joey S W Kwong; Jasmina Panovska-Griffiths; Antonio Rojas-Garcia; Daisuke Yoneoka; Jelle Veraart; Sofie Van Cauter; Ahmed M Abdel-Khalek; Magdy Settein; Tarek Yousry; Sotirios Bisdas
Journal:  Neuroradiology       Date:  2020-05-04       Impact factor: 2.804

Review 7.  Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors.

Authors:  Francesco Sanvito; Antonella Castellano; Andrea Falini
Journal:  Cancers (Basel)       Date:  2021-01-23       Impact factor: 6.639

8.  Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading.

Authors:  E L Pogosbekian; I N Pronin; N E Zakharova; A I Batalov; A M Turkin; T A Konakova; I I Maximov
Journal:  Neuroradiology       Date:  2021-01-07       Impact factor: 2.804

9.  The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis.

Authors:  Jixin Luan; Mingzhen Wu; Xiaohui Wang; Lishan Qiao; Guifang Guo; Chuanchen Zhang
Journal:  Radiat Oncol       Date:  2020-08-24       Impact factor: 3.481

10.  Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging.

Authors:  Johann-Martin Hempel; Cornelia Brendle; Sasan Darius Adib; Felix Behling; Ghazaleh Tabatabai; Salvador Castaneda Vega; Jens Schittenhelm; Ulrike Ernemann; Uwe Klose
Journal:  J Clin Med       Date:  2021-05-26       Impact factor: 4.241

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