Clinical application value of 3.0T MR diffusion tensor imaging in grade diagnosis of gliomas

The performance of diffusion tensor imaging parameters for the distinction between medulloblastoma and pilocytic astrocytoma

BACKGROUND

The aim of this study was to evaluate the performance of diffusion tensor imaging (DTI) parameters for the distinction between pediatric medulloblastomas and pilocytic astrocytomas.

METHODS

DTI was performed in 36 patients, who were divided into two groups: group 1 consisted of 26 patients with medulloblastoma, whereas group 2 consisted of 10 patients with pilocytic astrocytoma. The Mann-Whitney U Test was utilized to compare the tumoral fractional anisotropy (tFA) and diffusivity (tMD) values and the tumor to parenchyma ratios for these values (rFA and rMD, respectively) between these two groups. Receiver operating characteristic (ROC) curve analysis and the Youden Index were applied to compute the cut-off point, and then the area under the curve (AUC), sensitivity, and specificity were calculated.

RESULTS

The tFA and rFA values of group 1 were significantly higher than those of group 2 (P<0.05). In contrast, the tMD and rMD values of group 1 were significantly lower than those of group 2 (P<0.05). Among the FA parameters, a cut-off tFA value of 0.37 provided the best ability to discriminate between medulloblastomas and pilocytic astrocytomas, producing a sensitivity value of 84.6%, a specificity of 80%, and an AUC of 81.7%. The cut-off values for MD and rMD were determined to be 1.06 × 10-3 mm2/s and 1.33, respectively, and were determined to be the most efficacious parameters for the differential diagnosis between medulloblastoma and pilocytic astrocytoma, which generated sensitivity, specificity, and AUC values of 100%.

CONCLUSIONS

DTI parameters can play pivotal roles in the discrimination between medulloblastoma and pilocytic astrocytoma.

Nerve abnormalities in lumbar disc herniation: A systematic review and meta-analysis of diffusion tensor imaging

PURPOSE

The purpose of this study was to examine the values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in diffusion tensor imaging (DTI) for diagnosing patients with nerve impairment due to lumbar disc herniation (LDH).

METHODS

A literature search of databases (PubMed, Web of Science, Cochrane Library and Embase) was systematically performed to identify articles published before September 2021 that were relevant to this study. FA and ADC estimates of compressed nerve roots due to LDH and healthy controls in the same segment were compared, with either fixed or random effects models selected according to I2 heterogeneity. Additionally, subgroup analysis, sensitivity analysis, potential publication bias analysis and meta-regression analysis were also performed.

RESULTS

A total of 369 patients with LDH from 11 publications were included in this meta-analysis. The results showed significantly lower FA values (Weighted Mean Difference (WMD): -0.08, 95% confidence interval (CI): -0.09 to -0.07, P ≤ 0.001, I2 = 87.6%) and significantly higher ADC values (WMD: 0.25, 95% CI: 0.20 to 0.30, P ≤ 0.001, I2 = 71.4%) of the nerve on the compressed side due to LDH compared to the healthy side. Subgroup analysis indicated that different countries and magnetic field strengths may be associated with higher heterogeneity. Furthermore, meta-regression analysis further revealed that segment and field strength did not have a significant effect on the results, regardless of the FA or ADC values. Contrastingly, in FA, the year of publication, country, b value and directions showed an effect on the results.

CONCLUSIONS

This meta-analysis showed a significant decrease in FA and a significant increase in ADC in patients with nerve damage due to LDH. The results favourably support the presence of nerve impairment in patients with LDH.

Correlation of Diffusion Tensor Imaging Parameters with the Pathological Grade of Brain Glioma and Expression of Vascular Endothelial Growth Factor and Ki-67

Background: Most brain gliomas are high-grade and likely to spread locally. Consequently, these patients commonly have a poor prognosis. Accurate identification of the malignancy grade of brain glioma before treatment is of great clinical significance. Objectives: This study aimed to explore the correlation of diffusion tensor imaging (DTI) parameters, fractional anisotropy (FA), and apparent diffusion coefficient (ADC) with the pathological grade of brain glioma and expression of vascular endothelial growth factor (VEGF) and Ki-67. Patients and Methods: A total of 116 patients were selected for this study from January 2018 to December 2019. All the participants underwent magnetic resonance imaging (MRI) and DTI before surgery, and the FA and ADC values were measured for the regions of interest. Surgically resected tumor specimens were collected for immunohistochemical assay. Finally, the FA and ADC values and positive expression rates of VEGF and Ki-67 were compared. Results: A significantly higher FA, besides the positive expression of VEGF and Ki-67, was reported in the high-grade group, whereas a lower ADC was found in this group compared to the low-grade group (P < 0.05). Areas of normal white matter and peritumoral edema had higher FA values, whereas lower ADCs were measured in these areas compared to the cerebrospinal fluid (P < 0.05). The FA of tumor parenchymal area was positively correlated with the World Health Organization (WHO) WHO class of tumors (r = 0.588, P = 0.028), and the expression of VEGF and Ki-67 was positively correlated with the WHO grade (r = 0.843, P = 0.002 and r = 0.743, P = 0.006, respectively). The FA of tumor parenchymal area was positively correlated with the expression of VEGF and Ki-67 (r = 0.654, P = 0.008 and r = 0.567, P = 0.012, respectively). However, the ADC of tumor parenchymal area was not significantly correlated with the WHO grade, VEGF expression, or Ki-67 expression (r = 0.143, P = 0.156, r = 0.232, P = 0.116, and r = 0.054, P = 0.179, respectively). Conclusion: The FA value, as a DTI parameter, is valuable for assessing the malignancy grade of tumor cells and can provide a proper reference for formulating treatment regimens for brain gliomas.

ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

PURPOSE

To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades.

MATERIALS AND METHODS

Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC).

RESULTS

Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively).

CONCLUSIONS

High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.

Differentiating Nonenhancing Grade II Gliomas from Grade III Gliomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI

BACKGROUND

Contrast enhancement in a brain tumor on magnetic resonance imaging is typically indicative of a high-grade glioma. However, a significant proportion of nonenhancing gliomas can be either grade II or III. While gross total resection remains the primary goal, imaging biomarkers may guide management when surgery is not possible, especially for nonenhancing gliomas. The utility of diffusion tensor imaging and dynamic susceptibility contrast magnetic resonance imaging was evaluated in differentiating nonenhancing gliomas.

METHODS

Retrospective analysis was performed on imaging data from 72 nonenhancing gliomas, including grade II (n = 49) and III (n = 23) gliomas. Diffusion tensor imaging and dynamic susceptibility contrast data were used to generate fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity as well as cerebral blood volume, cerebral blood flow, and mean transit time maps. Univariate and multivariate logistic regression and area under the curve analyses were used to measure sensitivity and specificity of imaging parameters. A subanalysis was performed to evaluate the utility of imaging parameters in differentiating between different histologic groups.

RESULTS

Logistic regression analysis indicated that tumor volume and relative mean transit time could differentiate between grade II and III nonenhancing gliomas. At a cutoff value of 0.33, this combination provided an area under the curve of 0.71, 70.6% sensitivity, and 64.3% specificity. Logistic regression analyses demonstrated much higher sensitivity and specificity in the differentiation of astrocytomas from oligodendrogliomas or identification of grades within these histologic subtypes.

CONCLUSIONS

Diffusion tensor imaging and dynamic susceptibility contrast imaging can aid in differentiation of nonenhancing grade II and III gliomas and between histologic subtypes.

The role of diffusion tensor imaging metrics in the discrimination between cerebellar medulloblastoma and brainstem glioma

BACKGROUND

Differentiation between cerebellar medulloblastoma and brainstem glioma is necessary for certain clinical circumstances. We aimed to evaluate the function of diffusion tensor imaging (DTI) metrics in the differentiation between cerebellar medulloblastomas and brainstem gliomas in children.

PROCEDURE

The institutional review board approved this prospective study. Brain magnetic resonance imaging (MRI), including DTI, was assessed in 40 patients, who were divided into two groups: a medulloblastoma group (group 1, n = 25) and a brainstem glioma group (group 2, n = 15). The Mann-Whitney U test was utilized to compare tumoral fractional anisotropy (FA) and diffusivity (MD) values and tumor-to-parenchyma ratios for these values (rFA and rMD, respectively) between the two groups. Receiver-operating characteristic (ROC) curve analysis and the Youden index were exploited to calculate the cutoff value, along with the area under the curve (AUC), sensitivity, and specificity.

RESULTS

The FA value for medulloblastomas was significantly higher than that for brainstem gliomas (P < 0.05). In contrast, the MD and rMD values for medulloblastoma were significantly lower than those for brainstem gliomas (P < 0.05). A cutoff MD value of 0.97 was identified as the most effective factor for the differential diagnosis between medulloblastomas and brainstem gliomas, which reached a sensitivity of 96%, a specificity of 100%, and an AUC of 99.5%.

CONCLUSION

DTI metrics play a significant role in the differentiation between medulloblastoma and brainstem glioma in pediatric patients.

Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Background

To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features.

Methods

mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed.

Results

In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful.

Conclusions

Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes.

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

Multicenter sharing is an effective method to increase the data size for glioma research, but the data inconsistency among different institutions hindered the efficiency. This paper proposes a histogram specification with automatic selection of reference frames for magnetic resonance images to alleviate this problem (HSASR). The selection of reference frames is automatically performed by an optimized grid search strategy with coarse and fine search. The search range is firstly narrowed by coarse search of intraglioma samples, and then the suitable reference frame in histogram is selected by fine search within the sample selected by coarse search. Validation experiments are conducted on two datasets GliomaHPPH2018 and BraTS2017 to perform glioma grading. The results demonstrate the high performance of the proposed method. On the mixed dataset, the average AUC, accuracy, sensitivity, and specificity are 0.9786, 94.13%, 94.64%, and 93.00%, respectively. It is about 15% higher on all indicators compared with those without HSASR and has a slight advantage over the result of a manually selected reference frame by radiologists. Results show that our methods can effectively alleviate multicenter data inconsistencies and lift the performance of the prediction model.

The correlation of fractional anisotropy parameters with Ki-67 index, and the clinical implication in grading of non-enhancing gliomas and neuronal-glial tumors

PURPOSE

To investigate the correlation between the FA parameters and Ki-67 labeling index, and their diagnostic performance in grading supratentorial non-enhancing gliomas and neuronal-glial tumors (GNGT).

METHODS

This institutional review board-approved, Health Insurance Portability and Accountability (HIPAA) compliant retrospective study enrolled 35 patients, including 19 with low grade GNGT and 16 with high grade GNGT. The mean FA, maximal FA and mean maximal FA values derived from diffusion tensor imaging were measured. The correlation between the FA parameters and the Ki-67 labeling index was assessed by Spearman rank test. The receiver operating characteristic curve analysis and multivariate logistic regression analysis were performed to detect the optimal imaging parameters in grading GNGT.

RESULTS

The three FA parameters of low grade GNGT were significantly lower than the high grade GNGT (p < 0.001). The mean FA, maximal FA and mean maximal FA had significant positive correlation with Ki-67 labeling index (p = 0.001, p < 0.001, p < 0.001 respectively). The maximal FA showed a higher sensitivity and specificity in grading of non-enhancing GNGT with specificity of 78.9%, sensitivity of 100.0%, respectively.

CONCLUSIONS

The FA parameters correlated with Ki-67 labeling index, and were useful surrogates in preoperative grading supratentorial non-enhancing GNGT.