Evidence map›Paper›PMID 29770735›Full record

ArticleThe British journal of radiology2018

The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma.

Kerim Aslan, Hediye Pinar Gunbey, Leman Tomak, Lutfi Incesu

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Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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12citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kerim Aslan1 ¹Department of Radiology, Ondokuz Mayis University Faculty of Medicine , Samsun , Turkey.
Hediye Pinar Gunbey1 ¹Department of Radiology, Ondokuz Mayis University Faculty of Medicine , Samsun , Turkey.
Leman Tomak2 Department of Biostatistics, Ondokuz Mayis University Faculty of Medicine , Samsun , Turkey.
Lutfi Incesu1 ¹Department of Radiology, Ondokuz Mayis University Faculty of Medicine , Samsun , Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe purpose of this study was to examine whether the combined use of MR diffusion tensor imaging (DTI) parameters [DTI-apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)] could provide a more accurate diagnosis in differentiating between low-grade and atypical/anaplastic (high-grade) meningioma.

methodsPathologically proven 45 meningioma patients [32 low-grade, 13 high-grade (11 atypical and 2 anaplastic)] who had received DTI before surgery were assessed retrospectively by 2 independent observers. For each lesion, MR DTI parameters (ADCmin, ADCmax, ADCmean, FA, AD, and RD) and ratios (rADCmin, rADCmax, rADCmean, rFA, rAD, and rRD) were calculated. When differentiating between low- and high-grade meningioma, the optimum cutoff values of all MR DTI parameters were determined by using receiver operating characteristic (ROC) analysis. Area under the curve (AUC) was measured with combined ROC analysis for different combinations of MR DTI parameters in order to identify the model combination with the best diagnostic accuracy in differentiation between low and high-grade meningioma.

resultsAlthough the ADCmin, ADCmax, ADCmean, AD, RD, rADCmin, rADCmax, rADCmean, rAD, and rRD values of high-grade meningioma were significantly low (p = 0.007, p = 0.045, p = 0.035, p = 0.045, p = 0.003, p = 0.02, p = 0.03, p = 0.03, p = 0.045, and p = 0.01, respectively), when compared with low-grade meningioma, their FA and rFA values were significantly high (p = 0.007 and p = 0.01, respectively). For all MR DTI parameters, the highest individual distinctive power was RD with AUC of 0.778. The best diagnostic accuracy in differentiating between low- and high-grade meningioma was obtained by combining the ADCmin, RD, and FA parameters with 0.962 AUC.

conclusionThis study shows that combined MR DTI parameters consisting of ADCmin, RD, and FA can differentiate high-grade from low-grade meningioma with a diagnostic accuracy of 96.2%. Advances in knowledge: To the best of our knowledge, this is the first study reporting that a combined use of all MR DTI parameters provides higher diagnostic accuracy for the differentiation of low- from high-grade meningioma. Our study shows that any of the model combinations was superior to use of any individual MR DTI parameters for differentiation between low and high-grade meningioma. A combination of ADCmin, RD, and FA was found to be the best model for differentiating low-grade from high-grade meningioma and sensitivity, specificity, and AUC values were found to be 92.3%, 100%, and 0.96, respectively. Thus, a combination of MR DTI parameters can provide more accurate diagnostic information when differentiation between low and high-grade meningioma.

Indexed as

AdultAgedDiagnosis, DifferentialDiffusion Tensor ImagingFemaleHumansMaleMeningeal NeoplasmsMeningiomaMiddle AgedNeoplasm GradingRetrospective StudiesYoung Adult

Identifiers

PMID29770735
PMCPMC6209476

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.