Preoperative grading of supratentorial nonenhancing gliomas by high b-value diffusion-weighted 3 T magnetic resonance imaging

Ultra high b-value diffusion weighted imaging enables better molecular grading stratification over histological grading in adult-type diffuse glioma

PURPOSE

Accurate preoperative radiological staging of adult-type diffuse glioma is crucial for effective prognostic stratification and selection of appropriate therapeutic interventions. The purpose of this study was to compare the effectiveness of apparent diffusion coefficient (ADC) maps generated from ultrahigh b-value diffusion-weighted imaging (DWI) for molecular grading with that for histological grading of adult-type diffuse glioma, and to evaluate the correlation between these ADC maps and molecular and histological biomarkers.

METHODS

This study retrospectively enrolled forty adult-type diffuse glioma patients, diagnosed using the 2021 WHO classification criteria. Preoperative imaging data, including multiple b-value DWI and conventional magnetic resonance imaging, were collected. Tumors were graded using both histological and molecular criteria. Histogram analysis was conducted to generate 14 parameters for each tumor. Receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate tumor grading and molecular status differentiation. Analysis of histological biomarkers was performed by calculating the Pearson and Spearman correlation coefficients of continuous and hierarchical variables, respectively.

RESULTS

The intensity-related parameters for molecular grading were found to be superior to those for histological grading for the identification of WHO grade 4 (WHO4) adult-type diffuse glioma. The AUC of both grading systems increased with increasing b-values, with ADC8000-based histogram parameters showing the best results (molecular grading, square root: AUC = 0.897; histological grading, median: AUC = 0.737). The intensity-related parameters could also differentiate molecular WHO4 gliomas from histologically lower-grade gliomas (ADC8000-based square root: AUC = 0.919), and different ADC8000-based kurtosis was observed between molecular and histological WHO4 gliomas (AUC = 0.833). Significant correlations between the Ki-67 index and molecular status prediction for IDH, CDKN2A, and EGFR were also demonstrated.

CONCLUSION

The histogram parameters derived from high b-value ADC maps were found to be more effective for differentiating molecular grades of WHO4 adult-type diffuse glioma than for differentiating histological grades.

Feasibility of evaluating the histologic and genetic subtypes of WHO grade II-IV gliomas by diffusion-weighted imaging

BACKGROUND

To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively.

METHODS

One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values.

RESULTS

The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values.

CONCLUSION

DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

Essential Management of Pediatric Brain Tumors

Brain tumors are the most common solid tumors in children and are associated with high mortality. The most common childhood brain tumors are grouped as low-grade gliomas (LGG), high grade gliomas (HGG), ependymomas, and embryonal tumors, according to the World Health Organization (WHO). Advances in molecular genetics have led to a shift from pure histopathological diagnosis to integrated diagnosis. For the first time, these new criteria were included in the WHO classification published in 2016 and has been further updated in the 2021 edition. Integrated diagnosis is based on molecular genomic similarities of the tumor subclasses, and it can better explain the differences in clinical courses of previously histopathologically identical entities. Important advances have also been made in pediatric neuro-oncology. A growing understanding of the molecular-genetic background of tumorigenesis has improved the diagnostic accuracy. Re-stratification of treatment protocols and the development of targeted therapies will significantly affect overall survival and quality of life. For some pediatric tumors, these advances have significantly improved therapeutic management and prognosis in certain tumor subgroups. Some therapeutic approaches also have serious long-term consequences. Therefore, optimized treatments are greatly needed. Here, we discuss the importance of multidisciplinary collaboration and the role of (pediatric) neurosurgery by briefly describing the most common childhood brain tumors and their currently recognized molecular subgroups.

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.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Microstructural white matter alterations in Alzheimer's disease and amnestic mild cognitive impairment and its diagnostic value based on diffusion kurtosis imaging: a tract-based spatial statistics study

This prospective study aimed to explore the white matter microstructural alterations in Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) using the Tract-based Spatial Statistics (TBSS) method of diffusion kurtosis imaging (DKI).Diffusion images were collected from 45 AD patients, 42 aMCI patients, and 35 healthy controls (HC). The differences of DKI-derived parameters, including kurtosis fractional anisotropy (KFA), mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD), were compared across the three groups using the TBSS method. Correlation between the altered DKI-derived parameters and the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores were analyzed. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of different white matter parameters with the strongest correlations. As a result, compared with the HC group, KFA values decreased significantly in the aMCI group. Compared with both the HC and aMCI groups, the FA, KFA, and MK values decreased significantly and the MD value increased significantly in the AD group. FA, MD, KFA, and MK values of many white matter fiber tracts were significantly correlated with MMSE and MoCA scores. The area under the ROC curve (AUC) for the splenium of corpus callosum KFA values were highest for the diagnosis of aMCI and AD patients. In conclusion, the compactness and complexity of white matter microstructures were reduced in AD and aMCI patients. DKI can provide information about the severity of AD progression, and KFA might be more sensitive for the detection of white matter microstructural alterations.

Combined application of MRS and DWI can effectively predict cell proliferation and assess the grade of glioma: A prospective study

In order to assess combined application of MRS and DWI for prediction cell proliferation and grade diagnosis of glioma, We prospectively collected the Cho/Cr, Cho/NAA, Cr/NAA of MRS and tumor parenchyma ADC (ADC_T), contralateral mirror brain tissue ADC (ADC_H), rADC (rADC = ADC_T/ADC_H). According to postoperative pathology, the patients were divided into two groups: LGG group and HGG group, compared differences of age, gender, Ki67, MRS, DWI between two groups. Next, we analyzed the correlation between MRS, DWI and Ki67. On this basis, the sensitivity and specificity of MRS, DWI and MRS combined with DWI (MRS + DWI) in diagnosis of glioma grade were evaluated. The differences of Ki67, Cho/Cr, Cho/NAA, Cr/NAA, ADC_T, rADC between LGG group and HGG group were statistically significant (p = 0.000, 0.000, 0.000, 0.008, 0.000, and 0.000 respectively). From ROC curve, area under the curve (AUC), sensitivity and specificity of Cho/Cr, Cho/NAA, Cr/NAA, ADC_T, rADC, PRE (MRS + DWI) were (0.901, 86.7%, 85.7%), (0.876, 80.0%, 82.1%), (0.704, 63.3%, 71.4%), (0.862, 82.1%, 83.3%), (0.820, 75.0%, 76.7%), (0.920, 86.7%, 89.3%), respectively. Fisher's linear discriminant functions results suggest: Y₁ = -20.447 + 3.46•X₁ + 17.141•X₂ (LGG), Y₂ = -19.415 + 4.828•X₁ + 14.543•X₂ (HGG). Our study suggested that MRS and DWI can effectively predict cell proliferation preoperative. MRS combined with DWI can further improve sensitivity and specificity in assessing the grade of glioma.

Efficiency of High and Standard b Value Diffusion-Weighted Magnetic Resonance Imaging in Grading of Gliomas

Background

Glioma is the most common fatal malignant tumor of the CNS. Early detection of glioma grades based on diffusion-weighted imaging (DWI) properties is considered one of the most recent noninvasive promising tools in the assessment of glioma grade and could be helpful in monitoring patient prognosis and response to therapy.

Aim

This study aimed to investigate the accuracy of DWI at both standard and high b values (b = 1000 s/mm² and b = 3000 s/mm²) to distinguish high-grade glioma (HGG) from low-grade glioma (LGG) in clinical practice based on histopathological results.

Materials and Methods

Twenty-three patients with glioma had DWI at l.5 T MR using two different b values (b = 1000 s/mm² and b = 3000 s/mm²) at Al-Shifa Medical Complex after obtaining ethical and administrative approvals, and data were collected from March 2019 to March 2020. Minimum, maximum, and mean of apparent diffusion coefficient (ADC) values were measured through drawing region of interest (ROI) on a solid part at ADC maps. Data were analyzed by using the MedCalc analysis program, version 19.0.4, receiver operating characteristic (ROC) curve analysis was done, and optimal cutoff values for grading gliomas were determined. Sensitivity and specificity were also calculated.

Results

The obtained results showed the ADC_mean, ADC_ratio, ADC_max, and ADC_min were performed to differentiate between LGG and HGG at both standard and high b values. Moreover, ADC values were inversely proportional to glioma grade, and these differences are more obvious at high b value. Minimum ADC values using standard b value were 1.13 ± 0.17 × 10⁻³ mm²/s, 0.89 ± 0.85 × 10⁻³ mm²/s, and 0.82 ± 0.17 × 10⁻³ mm²/s for grades II, III, and IV, respectively. Concerning high b value, ADC_min values were 0.76 ± 0.07 × 10⁻³ mm²/s, 0.61 ± 0.01 × 10⁻³ mm²/s, and 0.48 ± 0.07 × 10⁻³ mm²/s for grades II, III, and IV, respectively. ADC values were inversely correlated with results of glioma grades, and the correlation was stronger at ADC₃₀₀₀ (r = −0.722, P ≤ 0.001). The ADC₃₀₀₀ achieved the highest diagnostic accuracy with an area under the curve (AUC) of 0.618, 100% sensitivity, 85.7% specificity, and 85.7% accuracy for glioma grading at a cutoff point of ≤0.618 × 10⁻³ mm²/s. The high b value showed stronger agreement with histopathology compared with standard b value results (k = 0.89 and 0.79), respectively.

Conclusion

The ADC values decrease with an increase in tumor cellularity. Meanwhile, high b value provides better tissue contrast by reflecting more tissue diffusivity. Therefore, ADC-derived parameters at high b value are more useful in the grading of glioma than those obtained at standard b value. They might be a better surrogate imaging sequence in the preoperative evaluation of gliomas.

Magnetic resonance imaging evaluation of brain glioma before postoperative radiotherapy

PURPOSE

To investigate the magnetic resonance imaging (MRI) images of brain glioma before postoperative radiotherapy, and to provide reference for the delineation of postoperative radiotherapy target area.

METHODS

Retrospective analysis was performed on 106 cases of brain glioma confirmed by surgery and pathology in our hospital, including 70 cases of high-grade glioma (HGG) and 36 cases of low-grade glioma (LGG). The MRI images of the lesions within 1 month before and after surgery were analyzed, the apparent diffusion coefficient (ADC) values in the near and far tumor areas were measured, respectively, and the corresponding rADC values were calculated.

RESULTS

The incidence of residual tumors of postoperative HGG and LGG was 0, 15.7% (0/36, 11/70), respectively. The incidence of postoperative reactive enhancement was 11.0% and 52.9% (4/36 and 37/70), respectively. About 30.6% and 81.4% (11/36 and 57/70) of patients with adjacent meningeal enhancement were found in the operative area.

CONCLUSIONS

The MRI images of HGG and LGG before postoperative radiotherapy had certain characteristics, providing a favorable guidance for the delineation of the target area of radiotherapy and the formulation of treatment plan.

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose

To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas.

Material and methods

A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas.

Results

There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10^-3 mm²/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%.

Conclusion

TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis

OBJECTIVE

Quantitative apparent diffusion coefficient (ADC) values of diffusion weighted imaging (DWI) could be applied to grade gliomas. This meta-analysis was conducted to assess the accuracy of ADC analysis in differentiating high-grade (HGGs) from low-grade gliomas (LGGs).

METHODS

PubMed, Cochrane library, Science Direct, and Embase were searched to identify suitable studies up to September 1, 2018. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS 2). We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic accuracy ratio (DOR) with 95% confidence intervals (CI), and determined the accuracy of the data by using the summary receiver operating characteristic (SROC) and calculating the area under the curve (AUC) to identity the accuracy of ADC analysis in grading gliomas.

RESULTS

Eighteen studies including 1172 patients were included and analyzed. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC with 95% CIs of DWI with b values of 1000 s/mm for separating HGGs from LGGs were 0.81 (95% CI 0.75-0.86), 0.87 (95% CI 0.81-0.91), 6.1 (95% CI 4.2-8.9), 0.22 (95% CI 0.17-0.29), 28 (95% CI 17-45), and 0.91 (95% CI 0.88-0.93), respectively. DWI with b values of 3000 s/mm showed slightly higher accuracy than that of 1000 (sensitivity 0.80, specificity 0.90 and AUC 0.92). Meta-regression analyses showed that field strengths and b values had significant impacts on diagnostic efficacy. Deeks testing confirmed no significant publication bias in all studies.

CONCLUSIONS

This meta-analysis suggested that ADC analysis of DWI have high accuracy in differentiating HGGs from LGGs. Standardized methodology is warranted to guide the use of this technique for clinical decision-making.

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.

Early postoperative delineation of residual tumor after low-grade glioma resection by probabilistic quantification of diffusion-weighted imaging

OBJECTIVE

In WHO grade II low-grade gliomas (LGGs), early postoperative MRI (epMRI) may overestimate residual tumor on FLAIR sequences. Consequently, MRI at 3-6 months follow-up (fuMRI) is used for delineation of residual tumor. This study sought to evaluate if integration of apparent diffusion coefficient (ADC) maps permits an accurate estimation of residual tumor early on epMRI.

METHODS

From a consecutive cohort, 43 cases with an initial surgery for an LGG, and complete epMRI (< 72 hours after resection) and fuMRI including ADC maps, were retrospectively identified. Residual FLAIR hyperintense tumor was manually segmented on epMRI and corresponding ADC maps were coregistered. Using an expectation maximization algorithm, residual tumor segments were probabilistically clustered into areas of residual tumor, ischemia, or normal white matter (NWM) by fitting a mixture model of superimposed Gaussian curves to the ADC histogram. Tumor volumes from epMRI, clustering, and fuMRI were statistically compared and agreement analysis was performed.

RESULTS

Mean FLAIR hyperintensity suggesting residual tumor was significantly larger on epMRI compared to fuMRI (19.4 ± 16.5 ml vs 8.4 ± 10.2 ml, p < 0.0001). Probabilistic clustering of corresponding ADC histograms on epMRI identified subsegments that were interpreted as mean residual tumor (7.6 ± 10.2 ml), ischemia (8.1 ± 5.9 ml), and NWM (3.7 ± 4.9 ml). Therefore, mean tumor quantification error between epMRI and fuMRI was significantly reduced (11.0 ± 10.6 ml vs -0.8 ± 3.7 ml, p < 0.0001). Mean clustered tumor volumes on epMRI were no longer significantly different from the fuMRI reference (7.6 ± 10.2 ml vs 8.4 ± 10.2 ml, p = 0.16). Correlation (Pearson r = 0.96, p < 0.0001), concordance correlation coefficient (0.89, 95% confidence interval 0.83), and Bland-Altman analysis suggested strong agreement between both measures after clustering.

CONCLUSIONS

Probabilistic segmentation of ADC maps facilitates accurate assessment of residual tumor within 72 hours after LGG resection. Multiparametric image analysis detected FLAIR signal alterations attributable to surgical trauma, which led to overestimation of residual LGG on epMRI compared to fuMRI. The prognostic value and clinical impact of this method has to be evaluated in larger case series in the future.

Noninvasively evaluating the grading and IDH1 mutation status of diffuse gliomas by three-dimensional pseudo-continuous arterial spin labeling and diffusion-weighted imaging

PURPOSE

To noninvasively evaluate the value of three-dimensional pseudo-continuous arterial spin labeling (3D pCASL) and diffusion-weighted imaging (DWI) in diffuse gliomas grading as well as isocitrate dehydrogenase (IDH) 1 mutation status.

METHODS

Fifty-six patients with pathologically confirmed diffuse gliomas with preoperative 3D pCASL and DWI were enrolled in this study. The Student's t test and Mann-Whitney U test were used to evaluate differences in parameters of DWI and 3D pCASL between low and high grade as well as between mutant and wild-type IDH1 diffuse gliomas; receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance. Subsequently, a multivariate stepwise logistic regression analysis was used to identify the independent parameters. Besides, Kruskal-Wallis H test was used to examine the differences among grades II, III, and IV diffuse gliomas.

RESULTS

All parameters but CBF_mean showed significant differences between low- and high-grade diffuse gliomas. In ROC analysis, the AUC of CBF_max, rCBF_mean, rCBF_max, ADC_mean, and ADC_min were 0.701, 0.730, 0.746, 0.810, and 0.856 respectively. Only the value of ADC_min was identified as the independent parameter in the differentiation of low- from high-grade diffuse gliomas. All parameters but CBF_mean showed significant differences among the three grades. And the values of CBF_mean, CBF_max, rCBF_mean, and ADC_mean showed significant differences between mutant and wild-type IDH1 in grade II-III astrocytoma.

CONCLUSION

Both 3D pCASL and DWI could be useful tools for distinguishing low- from high-grade diffuse gliomas and have the potential to differentiate different IDH1 mutation statuses of astrocytoma.

Multi-model Analysis of Diffusion-weighted Imaging of Normal Testes at 3.0 T: Preliminary Findings

RATIONALE AND OBJECTIVES

This study aimed to establish diffusion quantitative parameters (apparent diffusion coefficient [ADC], DDC, α, D_app, and K_app) in normal testes at 3.0 T.

MATERIALS AND METHODS

Sixty-four healthy volunteers in two age groups (A: 10-39 years; B: ≥ 40 years) underwent diffusion-weighted imaging scanning at 3.0 T. ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, α, D_app, and K_app were calculated using the mono-exponential, stretched-exponential, and kurtosis models. The correlations between parameters and the age were analyzed. The parameters were compared between the age groups and between the right and the left testes.

RESULTS

The average ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, α, D_app, and K_app values did not significantly differ between the right and the left testes (P > .05 for all). The following significant correlations were found: positive correlations between age and testicular ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, and D_app (r = 0.516, 0.518, 0.518, 0.521, and 0.516, respectively; P < .01 for all) and negative correlations between age and testicular α and K_app (r = -0.363, -0.427, respectively; P < .01 for both). Compared to group B, in group A, ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, and D_app were significantly lower (P < .05 for all), but α and K_app were significantly higher (P < .05 for both).

CONCLUSIONS

Our study demonstrated the applicability of the testicular mono-exponential, stretched-exponential, and kurtosis models. Our results can help establish a baseline for the normal testicular parameters in these diffusion models. The contralateral normal testis can serve as a suitable reference for evaluating the abnormalities of the other side. The effect of age on these parameters requires further attention.

Imaging in neuro-oncology

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

Apparent diffusion coefficient maps obtained from high b value diffusion-weighted imaging in the preoperative evaluation of gliomas at 3T: comparison with standard b value diffusion-weighted imaging

OBJECTIVE

To assess whether ADC maps obtained from high b value DWI were more valuable in preoperatively evaluating the grade, Ki-67 index and outcome of gliomas.

METHODS

Sixty-three patients with gliomas, who underwent preoperative multi b value DWI at 3 T, were enrolled. The ADC₁₀₀₀, ADC₂₀₀₀ and ADC₃₀₀₀ maps were generated. Receiver operating characteristic analyses were conducted to determine the area under the curve (AUC) in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG). Pearson correlation coefficients (R value) were calculated to investigate the correlation between parameters with the Ki-67 proliferation index. Survival analysis was conducted by using Cox regression.

RESULTS

The AUC of the mean ADC₁₀₀₀ value (0.820) was lower than that of the mean ADC₂₀₀₀ value (0.847) and mean ADC₃₀₀₀ value (0.875) in differentiating HGG from LGG. The R value of the mean ADC₁₀₀₀ value (-0.499) was less negative than that of the mean ADC₂₀₀₀ value (-0.530) and mean ADC₃₀₀₀ value (-0.567). The mean ADC₃₀₀₀ value was an independent prognosis factor for gliomas (p = 0.008), while the mean ADC₁₀₀₀ and ADC₂₀₀₀ values were not.

CONCLUSION

ADC maps obtained from high b value DWI might be a better imaging biomarker in the preoperative evaluation of gliomas.

KEY POINTS

• ADC ₃₀₀₀ maps could improve the differentiation between HGG and LGG. • The mean ADC ₃₀₀₀ value had a closer correlation with the Ki-67 index. • The mean ADC ₃₀₀₀ value was an independent prognosis factor for gliomas.