Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas.
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ABSTRACT:
SUBMITTER: Kwee TC
PROVIDER: S-EPMC4123199 | biostudies-other | 2010 Feb
REPOSITORIES: biostudies-other
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Project description:BackgroundOur aim was to assess the diagnostic performance of intravoxel incoherent motion (IVIM) MR imaging for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).MethodsForty-five patients with diffuse glioma (age 50.9 ± 20.4 y; 26 males, 19 females) were assessed with IVIM imaging using 13 b-values (0-1000 s/mm(2)) at 3T. The perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were calculated by fitting the bi-exponential model. The apparent diffusion coefficient (ADC) was obtained with 2 b-values (0 and 1000 s/mm(2)). Relative cerebral blood volume was measured by the dynamic susceptibility contrast method. Two observers independently measured D, ADC, D*, and f, and these measurements were compared between the LGG group (n = 16) and the HGG group (n = 29).ResultsBoth D (1.26 ± 0.37 mm(2)/s in LGG, 0.94 ± 0.19 mm(2)/s in HGG; P < .001) and ADC (1.28 ± 0.35 mm(2)/s in LGG, 1.03 ± 0.19 mm(2)/s in HGG; P < .01) were lower in the HGG group. D was lower than ADC in the LGG (P < .05) and HGG groups (P < .0001). D* was not different between the groups. The f-values were significantly larger in HGG (17.5 ± 6.3%) than in LGG (5.8 ± 3.8%; P < .0001) and correlated with relative cerebral blood volume (r = 0.85; P < .0001). Receiver operating characteristic analyses showed areas under curve of 0.95 with f, 0.78 with D, 0.73 with ADC, and 0.60 with D*.ConclusionIVIM imaging is useful in differentiating HGGs from LGGs.
| S-EPMC4677415 | biostudies-literature
Project description:PurposeGlioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.MethodsPre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.ResultsGrade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.ConclusionsADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.
| S-EPMC8049265 | biostudies-literature
Project description: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).
| S-EPMC5018419 | biostudies-literature
Project description:PURPOSE:Perfusion and diffusion magnetic resonance imaging (MRI) provide important biomarkers for brain tumor analysis. Our aim was to investigate if regions of increased perfusion or tumor with restricted diffusion on the immediate post-operative MRI examination would be predictive of time to tumor progression in patients with high-grade gliomas. MATERIALS AND METHODS:Twenty-three patients with high-grade gliomas were retrospectively analyzed. We measured the perfusion at the resection area and evaluated the presence or absence of the restricted diffusion in residual tumor masses. The associations of the perfusion, diffusion and contrast enhancement (delayed static enhancement (DSE)) characteristics with time to tumor progression were statistically calculated. We also evaluated if the location of the tumor progression was concordant to the areas of the elevated perfusion, tumor type restricted diffusion and enhancement. RESULTS:Patients with >200 days to progression are more likely to have no elevated relative cerebral blood volume (rCBV) ratio (p = 0.0004), no tumor restriction (p = 0.024), and no DSE (p = 0.052). The elevated mean rCBV ratio (p<0.001) and tumor type restricted diffusion (p = 0.002) were significantly associated with a higher risk of progression. All cases with rCBV ratio of >1.5 progressed in 275 days or earlier. Tumors tended to progress at the area where patients with post-operative MRIs showed elevated perfusion (p = 0.006), tumor-type restricted diffusion (p = 0.005) and DSE (p = 0.008). CONCLUSIONS:Post-operative analysis of rCBV, tumor type restricted diffusion and enhancement characteristics are predictive of time to progression, risk of progression and where tumor progression is likely to occur.
| S-EPMC6422263 | biostudies-literature
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.
| S-EPMC4248278 | biostudies-other
Project description:Pediatric high-grade gliomas (pHGGs) are aggressive neoplasms representing approximately 20% of brain tumors in children. Current therapies offer limited disease control, and patients have a poor prognosis. Empiric use of targeted therapy, especially at progression, is increasingly practiced despite a paucity of data regarding temporal and therapy-driven genomic evolution in pHGGs. To study the genetic landscape of pHGGs at recurrence, we performed whole exome and methylation analyses on matched primary and recurrent pHGGs from 16 patients. Tumor mutational profiles identified three distinct subgroups. Group 1 (n?=?7) harbored known hotspot mutations in Histone 3 (H3) (K27M or G34V) or IDH1 (H3/IDH1 mutants) and co-occurring TP53 or ACVR1 mutations in tumor pairs across the disease course. Group 2 (n?=?7), H3/IDH1 wildtype tumor pairs, harbored novel mutations in chromatin modifiers (ZMYND11, EP300 n?=?2), all associated with TP53 alterations, or had BRAF V600E mutations (n?=?2) conserved across tumor pairs. Group 3 included 2 tumors with NF1 germline mutations. Pairs from primary and relapsed pHGG samples clustered within the same DNA methylation subgroup. ATRX mutations were clonal and retained in H3G34V and H3/IDH1 wildtype tumors, while different genetic alterations in this gene were observed at diagnosis and recurrence in IDH1 mutant tumors. Mutations in putative drug targets (EGFR, ERBB2, PDGFRA, PI3K) were not always shared between primary and recurrence samples, indicating evolution during progression. Our findings indicate that specific key driver mutations in pHGGs are conserved at recurrence and are prime targets for therapeutic development and clinical trials (e.g. H3 post-translational modifications, IDH1, BRAF V600E). Other actionable mutations are acquired or lost, indicating that re-biopsy at recurrence will provide better guidance for effective targeted therapy of pHGGs.
| S-EPMC5663045 | biostudies-literature
Project description:PURPOSE:Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models' ability to differentiate between high-grade and low-grade gliomas. METHODS:This retrospective study included 22 patients with diffuse gliomas (age, 23-74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. RESULTS:The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67?±?0.67 vs. -?0.18?±?0.63, 0.68?±?0.74 vs. -?0.08?±?0.66, 0.63?±?0.72 vs. -?0.15?±?0.73; P?=?0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77?±?0.08 vs. 0.88?±?0.04; P?=?0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64?±?3.44 vs. 7.14?±?1.70%: P?<?0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). CONCLUSION:The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.
| S-EPMC7311374 | biostudies-literature
Project description:ObjectiveHigh-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.MethodsWe retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.ResultsThe results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.InterpretationThe habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.
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Project description:Gross total resection (GTR) of glioma is critical for improving the survival rate of glioma patients. One of the greatest challenges for achieving GTR is the difficulty in discriminating low grade tumor or peritumor regions that have an intact blood brain barrier (BBB) from normal brain tissues and delineating glioma margins during surgery. Here we present a highly sensitive, label-free terahertz reflectometry imaging (TRI) that overcomes current key limitations for intraoperative detection of World Health Organization (WHO) grade II (low grade), and grade III and IV (high grade) gliomas. We demonstrate that TRI provides tumor discrimination and delineation of tumor margins in brain tissues with high sensitivity on the basis of Hematoxylin and eosin (H&E) stained image. TRI may help neurosurgeons to remove gliomas completely by providing visualization of tumor margins in WHO grade II, III, and IV gliomas without contrast agents, and hence, improve patient outcomes.
| S-EPMC5080552 | biostudies-literature
Project description:The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p-value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10-3 mm2/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors.
| S-EPMC8947183 | biostudies-literature