Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High-<i>b</i>-Value Diffusion-weighted MR Imaging—Correlation with Tumor Grade

Kang, Yusuhn; Choi, Seung Hong; Kim, Young Jae; Kim, Kwang Gi; Sohn, Chul‐Ho; Kim, Ji Hoon; Yun, Tae Jin; Chang, Kee Hyun

doi:10.1148/radiol.11110686

Cited by 296 publications

(263 citation statements)

References 30 publications

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“…7,23 Studies that reported central necrotic regions for either tumor grade were excluded. Some studies separated tumor core values for enhancing and nonenhancing components, and these were recorded.…”

Section: Methodsmentioning

confidence: 99%

Meta-Analysis of Diffusion Metrics for the Prediction of Tumor Grade in Gliomas

Miloushev

Chow

Filippi

2014

AJNR Am J Neuroradiol

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Section: Methodsmentioning

confidence: 99%

Meta-Analysis of Diffusion Metrics for the Prediction of Tumor Grade in Gliomas

Miloushev

Chow

Filippi

2014

AJNR Am J Neuroradiol

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“…Glioma grading using conventional MRI sequences remains challenging and numerous studies aimed at determining the utility of MRS (19) or diffusion imaging with apparent diffusion coefficient (ADC) maps computation (1,12,15,22,29) in this setting.…”

Section: Schlüsselwörtermentioning

confidence: 99%

Combination of MRI and dynamic FET PET for initial glioma grading

Dunet¹,

Maeder²,

Lalonde³

et al. 2014

Nuklearmedizin

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SummaryAim: MRI and PET with 18 F-fluoro-ethyl-tyrosine (FET) have been increasingly used to evaluate patients with gliomas. Our purpose was to assess the additive value of MR spectroscopy (MRS), diffusion imaging and dynamic FET-PET for glioma grading. Patients, methods: 38 patients (42 ± 15 aged, F/M: 0.46) with untreated histologically proven brain gliomas were included. All underwent conventional MRI, MRS, diffusion sequences, and FET-PET within 3±4 weeks. Performances of tumour FET time-activity-curve, early-to-middle SUVmax ratio, choline / creatine ratio and ADC histogram distribution pattern for gliomas grading were assessed, as compared to histology. Combination of these parameters and respective odds were also evaluated. Results: Tumour time-activitycurve reached the best accuracy (67%) when taken alone to distinguish between low and high-grade gliomas, followed by ADC histogram analysis (65%). Combination of timeactivity-curve and ADC histogram analysis improved the sensitivity from 67% to 86% and the specificity from 63-67% to 100% (p < 0.008). On multivariate logistic regression analysis, negative slope of the tumour FET time-activity-curve however remains the best predictor of high-grade glioma (odds 7.6, SE 6.8, p = 0.022). Conclusion: Combination of dynamic FET-PET and diffusion MRI reached good performance for gliomas grading. The use of FET-PET/MR may be highly relevant in the initial assessment of primary brain tumours.

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“…[25][26][27] In addition, a histogram-based approach with ADC maps has been used in neuro-oncologic imaging to differentiate tumor progression from pseudoprogession and to evaluate tumor grades and even gene mutations. 25,[28][29][30] Histogram analysis is considered to represent tumor heterogeneity, which is well known to affect tumor response to chemoradiation therapy. Therefore, analysis of the spatial heterogeneity of the tumor cellularity by histogram analysis of ADC maps could help evaluate and predict tumor response to treatment.…”

Section: Introductionmentioning

confidence: 99%

Squamous Cell Carcinoma of the Head and Neck: Comparison of Diffusion-weighted MRI at b-values of 1,000 and 2,000 s/mm<sup>2</sup> to Predict Response to Induction Chemotherapy

et al. 2015

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Purpose: Recent publications have reported contradictory results of pretreatment diffusion-weighted magnetic resonance imaging (DWI) for the prediction of chemoradiotherapeutic response in primary squamous cell carcinomas of the head and neck (HNSCC). Therefore, we evaluated the diagnostic performance of DWI obtained with both standard (b = 0 and 1,000 s/mm 2 ) and high (b = 0 and 2,000 s/mm 2 ) b-values for predicting response to induction chemotherapy in HNSCCs.Methods: For 25 patients with primary HNSCC who underwent DWI with both standard and high b-values prior to treatment, we calculated corresponding apparent diffusion coefficient (ADC) maps. Regions of interest containing the tumor were drawn on every section of ADC maps and summated to make volume-based data of the entire tumor. Histogram parameters (mean ADC, kurtosis, and skewness) were correlated with treatment response using unpaired Student t test. Univariate and multivariate analysis of the ADC parameters, patient age, sex, whole tumor volume, and T stage were also performed to predict tumor response to induction chemotherapy.Results: Response to induction chemotherapy was good in 13 of the 25 patients and poor in 12. The mean ADC values of good responders at standard b-value (ADC 1000 ), 1.23 « 0.34 (© 10 ¹3 mm 2 /s), and high b-value (ADC 2000 ), 0.62 « 0.14 (© 10 ¹3 mm 2 /s), were lower than those of poor responders (ADC 1000 , 1.32 « 0.28 [© 10 ¹3 mm 2 /s]; ADC 2000 , 0.76 « 0.15 [© 10 ¹3 mm 2 /s]), but significant difference was achieved only at the ADC 2000 map (P = 0.02). In addition, mean tumor volume prior to treatment of good responders was smaller than that of poor responders. However, at multiple logistic regression analysis, only the mean ADC 2000 value remained as a significant predictor of response to induction chemotherapy.Conclusion: DWI with high b-values (b = 0 and 2,000 s/mm 2 ) as an assessment of ADC values may help predict tumor response to neoadjuvant chemotherapy for primary HNSCCs.

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Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High-b-Value Diffusion-weighted MR Imaging—Correlation with Tumor Grade

Abstract: Histogram analysis of ADC maps based on entire tumor volume can be a useful tool for grading gliomas. The fifth percentile of the cumulative ADC histogram obtained at a high b value was the most promising parameter for differentiating high- from low-grade gliomas.

Cited by 296 publications

References 30 publications

Meta-Analysis of Diffusion Metrics for the Prediction of Tumor Grade in Gliomas

Meta-Analysis of Diffusion Metrics for the Prediction of Tumor Grade in Gliomas

Combination of MRI and dynamic FET PET for initial glioma grading

Squamous Cell Carcinoma of the Head and Neck: Comparison of Diffusion-weighted MRI at b-values of 1,000 and 2,000 s/mm<sup>2</sup> to Predict Response to Induction Chemotherapy

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