Literature DB >> 31425809

Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas.

Xiao Wang1, Wenjing Gao2, Fuyan Li3, Wenqi Shi4, Hongxia Li5, Qingshi Zeng6.   

Abstract

PURPOSE: To retrospectively explore the utilization of MR diffusion kurtosis imaging (DKI) in predicting prognosis of the patients with high-grade gliomas.
MATERIALS AND METHODS: Thirty-three consecutive patients with cerebral gliomas underwent pretreatment DKI and diffusion-weighted imaging examination on a 3.0-T MR scanner. Diffusion parameters, including conventional tensor parameters, kurtosis metrics (mean kurtosis [MK], radial kurtosis [AK], and axial kurtosis [RK]), and minimum apparent diffusion coefficient (minADC), were obtained and normalized to the contralateral normal-appearing white matter. Correlations among each diffusion parameter and overall survival were analyzed by a Spearman method. The diagnostic efficiency of each parameter in predicting survival for patients with high-grade gliomas was assessed by a receiver operating characteristic curve. The favorable prognostic imaging biomarkers were further analyzed by using a Kaplan-Meier method with log-rank test.
RESULTS: In 33 patients, 17 patients reached overall survival >15 months (long survival group), whereas 16 showed overall survival <15 months (short survival group). Negative correlations between kurtosis metrics (MK, AK, and RK) and overall survival were obtained by using Spearman analysis (r = -0.63, -0.57, and -0.61, respectively, all P < 0.01), whereas minADC was positively correlated with overall survival (r = 0.56, P < 0.01). The kurtosis parameters of the long survival group were significantly lower than that of the short survival group (P < 0.001), while the minADC of the long survival group was significantly higher than that of the short survival group (P = 0.002). Among these diffusion parameters, the optimal cut-off value of MK (0.688) provided the best combination of sensitivity (93.75%) and specificity (76.47%) for differentiation of patients with long survival from those with short survival. High kurtosis metrics and low minADC were significant predictors of poor outcome. (P < 0.05).
CONCLUSION: Both kurtosis metrics and minADC have the potential to predict survival for the patients with high-grade gliomas. The preoperative kurtosis parameters, especially MK, can be taken as a preoperative prognostic biomarker to predict prognosis in patients with high-grade gliomas.
Copyright © 2019 Elsevier Inc. All rights reserved.

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Year:  2019        PMID: 31425809     DOI: 10.1016/j.mri.2019.08.001

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  3 in total

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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

2.  Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.

Authors:  Liang Jiang; Leilei Zhou; Zhongping Ai; Chaoyong Xiao; Wen Liu; Wen Geng; Huiyou Chen; Zhenyu Xiong; Xindao Yin; Yu-Chen Chen
Journal:  J Clin Med       Date:  2022-04-21       Impact factor: 4.964

3.  Diffusion Kurtosis Imaging as a Prognostic Marker in Osteosarcoma Patients with Preoperative Chemotherapy.

Authors:  Chenglei Liu; Yue Xing; Dongmin Wei; Qiong Jiao; Qingcheng Yang; Dapeng Lei; Xiaofeng Tao; Weiwu Yao
Journal:  Biomed Res Int       Date:  2020-09-26       Impact factor: 3.411

  3 in total

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