Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model.
ABSTRACT: BACKGROUND AND PURPOSE:Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D). MATERIALS AND METHODS:Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, ?, and ?) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation. RESULTS:Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ? .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ? .706). The receiver operating characteristic analysis showed that ? (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or ? (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%. CONCLUSIONS:The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, ? exhibited a better performance than the other 2 parameters, including ADC (or D).
Project description:To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas.This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations.ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05).Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.
Project description:The preoperative grading of gliomas, which is critical for guiding therapeutic strategies, remains unsatisfactory. We aimed to retrospectively assess the efficacy of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in the grading of gliomas. Forty-two newly diagnosed glioma patients underwent conventional MR imaging, DWI, and contrast-enhanced MR imaging. Parameters of apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast ADC (f) were generated. They were tested for differences between low- and high-grade gliomas based on one-way ANOVA. Receiver-operating characteristic (ROC) analyses were conducted to determine the optimal thresholds as well as the sensitivity and specificity for grading. ADC, D, and f were higher in the low-grade gliomas, whereas D* tended to be lower (all P<0.05). The AUC, sensitivity, specificity and the cutoff value, respectively, for differentiating low- from high-grade gliomas for ADC, D and f, and differentiating high- from low-grade gliomas for D* were as follows: ADC, 0.926, 100%, 82.8%, and 0.7 × 10(-3) mm(2)/sec; D, 0.942, 92.3%, 86.2%, and 0.623 × 10(-3) mm(2)/sec; f, 0.902, 92.3%, 86.2%, and 35.3%; D*, 0.798, 79.3%, 84.6%, and 0.303 × 10(-3) mm(2)/sec. The IVIM DWI demonstrates efficacy in differentiating the low- from high-grade gliomas.
Project description:Distinguishing between low-grade oligodendrogliomas (ODs) and astrocytomas (AC) is of interest for defining prognosis and stratifying patients to specific treatment regimens. The purpose of this study was to determine if the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion imaging can help to differentiate between newly diagnosed grade II OD and AC subtypes and to evaluate the ADC and FA values for the mixed population of oligoastrocytomas (OA). Fifty-three patients with newly diagnosed grade II gliomas were studied using a 1.5T whole body scanner (23 ODs, 16 ACs, and 14 OAs). The imaging protocol included post-gadolinium T1-weighted images, T2-weighted images, and either three and/or six directional diffusion imaging sequence with b = 1000 s/mm(2). Diffusion-weighted images were analyzed using in-house software to calculate maps of ADC and for six directional acquisitions, FA. The intensity values were normalized by values from normal appearing white matter (NAWM) to generate maps of normalized apparent diffusion coefficient (nADC) and normalized fractional anisotropy (nFA). The hyperintense region in the T2 weighted image was defined as the T2All region. A Mann-Whitney rank-sum test was performed on the 25th, median, and 75th nADC and nFA among the three subtypes. Logistic regression was performed to determine how well the nADC and nFA predict subtype. Lesions diagnosed as being OD had significantly lower nADC and significantly higher nFA, compared to AC. The nADC and nFA values individually classified the data with an accuracy of 87%. Combining the two did not enhance the classification. The patients with OA had nADC and nFA values between those of OD and AC. This suggests that ADC and FA may be helpful in directing tissue sampling to the most appropriate regions for taking biopsies in order to make a definitive diagnosis.
Project description:OBJECTIVES:To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas. METHODS:Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared according to the World Health Organization grade. The diagnostic ability and added value of the APT signal to the ADC and rCBV for discriminating between low- and high-grade gliomas were evaluated using receiver operating characteristic (ROC) analyses and integrated discrimination improvement. RESULTS:The APT signal increased as the glioma grade increased. The discrimination abilities of the APT, ADC and rCBV values were not significantly different. Using both the APT signal and ADC significantly improved discrimination vs. the ADC alone (area under the ROC curve [AUC], 0.888?vs.?0.910; P?=?0.007), whereas using both the APT signal and rCBV did not improve discrimination vs. the rCBV alone (AUC, 0.927?vs.?0.923; P?=?0.222). CONCLUSIONS:APT imaging may be a useful imaging biomarker that adds value to the ADC for discriminating between low- and high-grade gliomas. KEY POINTS:• Higher APT values were correlated with higher glioma grades. • Adding the APT signal to the ADC improved glioma grading. • Adding the APT signal to rCBV did not improve glioma grading. • APT is a useful adjunct to the ADC for glioma grading.
Project description: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.
Project description:The clinical value of MR diffusion tensor imaging (DTI) in grade diagnosis of gliomas was investigated. A total of 31 patients with glioma were administered 3.0T MR convention and DTI examination, with quantitative measurement of anisotropy coefficient fractional anisotropy (FA) and apparent dispersion coefficient (ADC) value, and the comparison of quantitative parameters of glioma between low- and high-grade, which was detected by Mann-Whitney U test. The receiver operation characteristic (ROC) curve was drawn to take the value of ADC and FA in tumor ROI as a critical point, to calculate the area under the curve and to confirm the diagnosis threshold value and evaluate its diagnostic efficiency. The FA value of 14 low-grade glioma cases was 139.4±81.3, with an ADC value of (1.36±0.21) ×10-3 mm2/sec. The FA value of 17 high-grade glioma cases was 103.1±41.5, with ADC value of (1.09±0.28)-3 mm2/sec; the difference between the two groups was statistically significant (P<0.05). The ADC value was taken as the critical point to judge tumor grade and draw the ROC curve; the area under the curve was 0.79. As the diagnosis threshold value, the ADC value of 1.11×10-3 mm2/sec was used to distinguish between low- and high-grade tumor with a sensitivity of 58.8% and specificity of 92.9%. The FA value was taken as a critical point to judge tumor grade and draw the ROC curve; the area under the curve was 0.62. As the diagnosis threshold value, the FA value of 178.9 was applied to distinguish between low- and high-grade tumor sensitivity of 94.1% and specificity of 35.7%. Therefore, the FA value and ADC value in DTI has an important estimated value for the pathological grade of glioma.
Project description:Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as "benign" neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed. Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status. Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.
Project description:Background:An assessment of the degree of white matter tract injury is important in neurosurgical planning for patients with gliomas. The main objective of this study was to assess the injury grade of the corticospinal tract (CST) in rats with glioma using diffusion tensor imaging (DTI). Methods:A total 17 rats underwent 7.0T MRI on day 10 after tumor implantation. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were acquired in the tumor, peritumoral and contralateral areas, and the ADC ratio (ipsilateral ADC/contralateral ADC) and rFA (relative FA = ipsilateral FA/contralateral FA) in the peritumoral areas were measured. The CST injury was divided into three grades and delineated by diffusion tensor tractography reconstruction imaging. The fiber density index (FDi) of the ipsilateral and contralateral CST and rFDi (relative FDi = ipsilateral FDi/contralateral FDi) in the peritumoral areas were measured. After the mice were sacrificed, the invasion of glioma cells and fraction of proliferating cells were observed by hematoxylin-eosin and Ki67 staining in the tumor and peritumoral areas. The correlations among the pathology results, CST injury grade and DTI parameter values were calculated using a Spearman correlation analysis. One-way analysis of variance was performed to compare the different CST injury grade by the rFA, rFDi and ADC ratio values. Results:The tumor cells and proliferation index were positively correlated with the CST injury grade (r = 0.8857, 0.9233, P < 0.001). A negative correlation was demonstrated between the tumor cells and the rFA and rFDi values in the peritumoral areas (r = -0.8571, -0.5588), and the proliferation index was negatively correlated with the rFA and rFDi values (r = -0.8571, -0.5588), while the ADC ratio was not correlated with the tumor cells or proliferation index. The rFA values between the CST injury grades (1 and 3, 2 and 3) and the rFDi values in grades 1 and 3 significantly differed (P < 0.05). Conclusions:Diffusion tensor imaging may be used to quantify the injury degrees of CST involving brain glioma in rats. Our data suggest that these quantitative parameters may be used to enhance the efficiency of delineating the relationship between fiber tracts and malignant tumor.
Project description:BACKGROUND: We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas. METHODS: In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients. RESULTS: Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%. CONCLUSIONS: Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.
Project description:Navigated transcranial magnetic stimulation (nTMS) combined with diffusion tensor imaging (DTI) is used preoperatively in patients with eloquent-located brain lesions and allows analyzing non-invasively the spatial relationship between the tumor and functional areas (e.g. the motor cortex and the corticospinal tract [CST]). In this study, we examined the diffusion parameters FA (fractional anisotropy) and ADC (apparent diffusion coefficient) within the CST in different locations and analyzed their interrater reliability and usefulness for predicting the patients' motor outcome with a precise approach of specific region of interest (ROI) seeding based on the color-coded FA-map.Prospectively collected data of 30 patients undergoing bihemispheric nTMS mapping followed by nTMS-based DTI fiber tracking prior to surgery of motor eloquent high-grade gliomas were analyzed by 2 experienced and 1 unexperienced examiner. The following data were scrutinized for both hemispheres after tractography based on nTMS-motor positive cortical seeds and a 2nd region of interest in one layer of the caudal pons defined by the color-coded FA-map: the pre- and postoperative motor status (day of discharge und 3 months), the closest distance between the tracts and the tumor (TTD), the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC). The latter as an average within the CST as well as specific values in different locations (peritumoral, mesencephal, pontine).Lower average FA-values within the affected CST as well as higher average ADC-values are significantly associated with deteriorated postoperative motor function (p = 0.006 and p = 0.026 respectively). Segmental analysis within the CST revealed that the diffusion parameters are especially disturbed on a peritumoral level and that the degree of their impairment correlates with motor deficits (FA p = 0.065, ADC p = 0.007). No significant segmental variation was seen in the healthy hemisphere. The interrater reliability showed perfect agreement for almost all analyzed parameters.Adding diffusion weighted imaging derived information on the structural integrity of the nTMS-based tractography results improves the predictive power for postoperative motor outcome. Utilizing a second subcortical ROI which is specifically seeded based on the color-coded FA map increases the tracking quality of the CST independently of the examiner's experience. Further prospective studies are needed to validate the nTMS-based prediction of the patient's outcome.