The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

Chen, Zhiye; Zhou, Peng; Lv, Bin; Liu, Mengqi; Wang, Yan; Wang, Yulin; Lou, Xin; Gui, Qiuping; He, Huiguang; Ma, Lin

doi:10.1007/s00330-017-4899-4

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…A meta-analysis showed that the quantitative measurement of the apparent diffusion coefficient (ADC) value can be used to grade gliomas with high accuracy ( 9 ). Our previous study demonstrated that the minimum ADC (ADCmin) can be used to predict the grading of neuroepithelial tumors ( 10 ). Prior studies ( 11 , 12 ) have shown that the characteristics of lesions, such as location, internal structure, and enhancement pattern, are different among the genetic subtypes of glioma.…”

Section: Introductionmentioning

confidence: 99%

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

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Section: Introductionmentioning

confidence: 99%

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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“…Recently, molecular features have also been added to the grading along with the previously used histopathological characteristics [ 18 ]. Over the years, different imaging modalities such as diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI have also been studied for their potential use in tumor grading [ 19 , 20 ]. More recently, great interest has been taken in studying high-grade and low-grade tumor characteristics using DTI.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Implications of White Matter Tract Involvement by Intra-axial Brain Tumors

Bakhshi¹,

Quddusi²,

Mahmood³

et al. 2021

Cureus

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Introduction Diffusion tensor imaging (DTI) is being increasingly used during brain tumor surgery. However, there is limited data available on its diagnostic and prognostic value. Our objective was to assess the pattern of involvement of white matter tracts (WMTs) by intra-axial brain tumors on DTI. Secondary objectives were to evaluate implications of involvement of WMT on surgical resection, and the post-operative functional outcome. Methods This was a retrospective study of consecutive patients, who underwent DTI-guided surgery for brain tumors. The involvement of WMTs by tumors on DTI was assessed by a radiologist (who was blind to the pathology) using the Witwer classification. The pathology was reported by histopathologists using the World Health Organization brain tumor classification. Karnofsky Performance Status Scale (KPS) was used for assessing patients’ neurological status at admission, and at follow-up. Results Forty-five (58.4%) out of 77 tumors reviewed caused infiltration of WMTs, whereas only 22 (28.6%) tumors caused displacement of WMTs (p= 0.040). Among 32 cases of astrocytoma, the involvement of WMTs was influenced by the grade of tumor (p= 0.012), as high-grade tumors caused infiltration (19; 59.4%), unlike low-grade tumors that commonly caused displacement (2; 50%). Oligodendrogliomas caused infiltration/disruption of WMTs in most cases, irrespective of the grade (19 out of 25 cases; 76%). At the last follow-up, 27 (35.1%) patients showed improvement in KPS and 14 (18.2%) reported deterioration, while there was no change observed in 36 (46.8%) patients. The infiltration of WMTs was associated with a poor functional outcome. Conclusions High-grade astrocytomas mostly cause infiltration of WMTs, unlike oligodendrogliomas, which often infiltrate WMTs, irrespective of the tumor grade. The infiltration of WMTs is associated with a poor functional outcome at follow-ups.

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Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging

Kang

Liu

et al. 2020

BMC Med Imaging

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Background To investigate the ability of amide proton transfer (APT) weighted magnetic resonance imaging (MRI), arterial spin labeling (ASL), diffusion weighted imaging (DWI) and the combination for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). Methods Twenty-seven patients including nine LGGs and eighteen HGGs underwent conventional, APT, ASL and DWI MRI with a 3.0-T MR scanner. Histogram analyses was performed and quantitative parameters including mean apparent diffusion coefficient (ADC mean), 20th-percentile ADC (ADC 20th), mean APT (APT mean), 90th-percentile APT (APT 90th), relative mean cerebral blood flow (rCBF mean) and relative 90th-percentile CBF (rCBF 90th) were compared between HGGs and LGGs. The diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis of each parameter and their combination. Correlations were analyzed among the MRI parameters and Ki-67. Results The APT values were significantly higher in the HGGs compared to the LGGs (p < 0.005), whereas ADC values were significantly lower in HGGs than LGGs (P < 0.0001). The ADC 20th and APT mean had higher discrimination abilities compared with other single parameters, with the area under the ROC curve (AUC) of 0.877 and 0.840. Adding ADC parameter, the discrimination ability of APT and rCBF significantly improved. The ADC was negatively correlated with the APT and rCBF value, respectively, while APT value was positively correlated with rCBF value. Significant correlations between ADC values and Ki-67 were also observed. Conclusions APT and DWI are valuable in differentiating HGGs from LGGs. The combination of APT, DWI and ASL imaging could improve the ability for discriminating HGGs from LGGs.

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The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

Abstract: • HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.

Cited by 4 publications

References 17 publications

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Diagnostic Implications of White Matter Tract Involvement by Intra-axial Brain Tumors

Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging

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