Diffusion Analysis of Intracranial Epidermoid, Head and Neck Epidermal Inclusion Cyst, and Temporal Bone Cholesteatoma.

BACKGROUND AND PURPOSE: Intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are typically slow-growing, benign conditions arising from ectodermal tissue. They exhibit increased signal on DWI. While much of the imaging literature describes these lesions as showing diffusion restriction, we aimed to investigate these qualitative signal intensities and interpretations of restricted diffusion with respect to normal brain structures. This study aimed to quantitatively evaluate the ADC values and histogram features of these lesions. MATERIALS AND METHODS: This retrospective study included children with histologically confirmed diagnoses of intracranial epidermoid tumors, temporal bone cholesteatomas, or head and neck epidermoid inclusion cysts. Lesions were segmented, and voxelwise calculation of ADC values was performed along with histogram analysis. ADC calculations were validated with a second analysis software to ensure accuracy. Normal brain ROIs-including the cerebellum, white matter, and thalamus-served as normal comparators. Correlational analysis and Bland-Altman plots assessed agreement among software tools for ADC calculations. Differences in the distribution of values between the lesions and normal brain tissues were assessed using the Wilcoxon rank sum and Kruskal-Wallis tests. RESULTS: Forty-eight pathology-proved cases were included in this study. Among them, 13 (27.1%) patients had intracranial epidermoid tumors, 14 (29.2%) had head and neck epidermoid inclusion cysts, and 21 (43.7%) had temporal bone cholesteatomas. The mean age was 8.67 (SD, 5.30) years, and 27 (52.9%) were female. The intraclass correlation for absolute agreement for lesional ADC between the 2 software tools was 0.997 (95% CI, 0.995-0.998). The intracranial epidermoid tumor, head and neck epidermoid inclusion cyst, and temporal bone cholesteatoma median ADC values were not significantly different (973.7 versus 875.7 versus 933.2 ×10-6 mm2/s, P = .265). However, the ADCs of the 3 types of lesions were higher than those of 3 normal brain tissue types (933 versus 766, × 10-6 mm2/s, P < .0001). CONCLUSIONS: The ADC values of intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are higher than those of normal brain regions. It is not accurate to simply classify these lesions as exhibiting restricted diffusion or reduced diffusivity without considering the tissue used for comparison. The observed hyperintensity on DWI compared with the brain is likely attributable to a relatively higher contribution of the T2 shinethrough effect.

Can Automated 3-Dimensional Dixon-Based Methods Be Used in Patients With Liver Iron Overload?

PURPOSE: Accurate quantification of liver iron concentration (LIC) can be achieved via magnetic resonance imaging (MRI). Maps of liver T2*/R2* are provided by commercially available, vendor-provided, 3-dimensional (3D) multiecho Dixon sequences and allow automated, inline postprocessing, which removes the need for manual curve fitting associated with conventional 2-dimensional (2D) gradient echo (GRE)-based postprocessing. The main goal of our study was to investigate the relationship among LIC estimates generated by 3D multiecho Dixon sequence to values generated by 2D GRE-based R2* relaxometry as the reference standard. METHODS: A retrospective review of patients who had undergone MRI scans for estimation of LIC with conventional T2* relaxometry and 3D multiecho Dixon sequences was performed. A 1.5 T scanner was used to acquire the magnetic resonance studies. Acquisition of standard multislice multiecho T2*-based sequences was performed, and R2* values with corresponding LIC were estimated. The comparison between R2* and corresponding LIC estimates obtained by the 2 methods was analyzed via the correlation coefficients and Bland-Altman difference plots. RESULTS: This study included 104 patients (51 male and 53 female patients) with 158 MRI scans. The mean age of the patients at the time of scan was 15.2 (SD, 8.8) years. There was a very strong correlation between the 2 LIC estimation methods for LIC values up to 3.2 mg/g (LIC quantitative multiecho Dixon [qDixon; from region of interest R2*] vs LIC GRE [in-house]: r = 0.83, P < 0.01; LIC qDixon [from segmentation volume R2*] vs LIC GRE [in-house]: r = 0.92, P < 0.01); and very weak correlation between the 2 methods at liver iron levels >7 mg/g. CONCLUSION: Three-dimensional-based multiecho Dixon technique can accurately measure LIC up to 7 mg/g and has the potential to replace 2D GRE-based relaxometry methods.

Rate of Change of Liver Iron Content by MR Imaging Methods: A Comparison Study.

Objective: Magnetic resonance imaging (MRI) can accurately quantify liver iron concentration (LIC), eliminating the need for an invasive liver biopsy. Currently, the most widely used relaxometry methods for iron quantification are R2 and R2*, which are based on T2 and T2* acquisition sequences, respectively. We compared the rate of change of LIC as measured by the R2-based, FDA-approved commercially available third-party software with the rate of change of LIC measured by in-house analysis using R2*-relaxometry-based MR imaging in patients undergoing follow-up MRI scans for liver iron estimation. Methods: We retrospectively included patients who had undergone serial MRIs for liver iron estimation. The MR studies were performed on a 1.5T scanner; standard multi-slice, multi-echo T2- and T2*-based sequences were acquired, and LIC was estimated. The comparison between the rate of change of LIC by R2 and R2* values was performed via correlation coefficients and Bland−Altman difference plots. Results: One hundred and eighty-nine MR abdomen studies for liver iron evaluation from 81 patients (male: 38; female: 43) were included in the study. Fifty-nine patients had two serial scans, eighteen patients had three serial scans, three patients had four serial scans, and one patient had five serial scans. The average time interval between the first and last scans for each patient was 13.3 months. The average rates of change of LIC via R2 and R2* methods were −0.0043 ± 0.0214 and −0.0047 ± 0.012 mg/g per month, respectively. There was no significant difference in the rate of change of LIC observed between the two methods. Linearity between the rate of change of LIC measured by R2 (LIC R2) and R2* (LIC R2*) was strong, showing a correlation coefficient of r = 0.72, p < 0.01. A Bland−Altman plot between the rate of change of the two methods showed that the majority of the plotted variables were between two standard deviations. Conclusion: There was no significant difference in the rate of change of LIC detected between the R2 method and the R2* method that uses a gradient echo (GRE) sequence acquired with breath-hold. Since R2* is relatively faster and less prone to motion artifacts, R2*-derived LIC is recommended for iron homeostasis follow-up in patients with liver iron overload.

The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups.

BACKGROUND: Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce. OBJECTIVE: This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features. MATERIALS AND METHODS: In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups. RESULTS: We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found. CONCLUSION: ADC histogram analysis can be implemented as a complementary tool in the preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively.

Reference values for MRI-derived psoas and paraspinal muscles and macroscopic fat infiltrations in paraspinal muscles in children.

BACKGROUND: Sarcopenia, defined as loss of skeletal muscle mass, is a novel term associated with adverse outcomes in children. Magnetic Resonance Imaging (MRI) is a safe and precise technique for measuring tissue compartments and is commonly used in most routine paediatric imaging protocols. Currently, there is a lack of MRI-derived normative data which can help in determining the level of sarcopenia. This study aimed to introduce reference values of total psoas muscle area (tPMA), total paraspinal muscle area (tPSMA), and total macroscopic fat infiltrations of the PSMA (tMFI). METHODS: In this retrospective study, the local database was searched for abdominal and pelvic region MRI studies of children aged from 1 to 18 years (mean age (standard deviation (SD)) of 9.8 (5.5) years) performed in the years 2010-2021. Children with chronic diseases and a history of surgical interventions were excluded from the analysis. Finally, a total of 465 healthy children (n = 233 girls, n = 232 boys) were enrolled in the study. The values of the tPMA, tPMSA, and tMFI were measured in square centimetres (cm2 ) at the level of the L4/L5 intervertebral disc as the sum of the left and right regions. Age-specific and sex-specific muscle, fat, and body mass index percentile charts were constructed using the LMS method. Inter-observer agreement and intra-observer reproducibility were assessed using the Bland-Altman plots. RESULTS: Both tPMA and tPSMA showed continuous increases in size (in cm2 ) throughout all age groups. At the age of 18, the median tPMA areas reached 26.37 cm2 in girls and 40.43 cm2 in boys. Corresponding tPSMA values were higher, reaching the level of 40.76 cm2 in girls and 56.66 cm2 in boys. The mean value of tMFI within the paraspinal muscles was 5.0% (SD 3.65%) of their total area in girls and 3.5% (SD 2.25%) in boys with the actual difference between sexes up to 0.96 cm2 . Excellent intra-observer reproducibility and inter-observer agreement were noted. Actual mean differences for tPMA were at the level of 0.43 and 0.39 cm2 , respectively. Mean bias for tPSMA was 0.1 cm2 for inter-observer and 0.05 cm2 for intra-observer measurements. CONCLUSIONS: Our findings demonstrate novel and highly reproducible sex-specific MRI-derived reference values of tPMS, tPSMA, and tMFI at the level of the L4/L5 intervertebral disc for children from 1 to 18 years old, which may guide a clinician in the assessment of sarcopenia, a prognostic outcome marker in children.

Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : A Large Retrospective Study and Brief Review of Literature.

PURPOSE: This study aimed to evaluate the application of apparent diffusion coefficient (ADC) histogram analysis to differentiate posterior fossa tumors (PFTs) in children. METHODS: A total of 175 pediatric patients with PFT, including 75 pilocytic astrocytomas (PA), 59 medulloblastomas, 16 ependymomas, and 13 atypical teratoid rhabdoid tumors (ATRT), were analyzed. Tumors were visually assessed using DWI trace and conventional MRI images and manually segmented and post-processed using parametric software (pMRI). Furthermore, tumor ADC values were normalized to the thalamus and cerebellar cortex. The following histogram metrics were obtained: entropy, minimum, 10th, and 90th percentiles, maximum, mean, median, skewness, and kurtosis to distinguish the different types of tumors. Kruskal Wallis and Mann-Whitney U tests were used to evaluate the differences. Finally, receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for differentiating the various PFTs. RESULTS: Most ADC histogram metrics showed significant differences between PFTs (p < 0.001) except for entropy, skewness, and kurtosis. There were significant pairwise differences in ADC metrics for PA versus medulloblastoma, PA versus ependymoma, PA versus ATRT, medulloblastoma versus ependymoma, and ependymoma versus ATRT (all p < 0.05). Our results showed no significant differences between medulloblastoma and ATRT. Normalized ADC data showed similar results to the absolute ADC value analysis. ROC curve analysis for normalized ADCmedian values to thalamus showed 94.9% sensitivity (95% CI: 85-100%) and 93.3% specificity (95% CI: 87-100%) for differentiating medulloblastoma from ependymoma. CONCLUSION: ADC histogram metrics can be applied to differentiate most types of posterior fossa tumors in children.

R2 relaxometry based MR imaging for estimation of liver iron content: A comparison between two methods.

PURPOSE: To compare the reproducibility and accuracy of R2-relaxometry MRI for estimation of liver iron concentration (LIC) between in-house analysis and FDA-approved commercially available third party results. METHODS: All MR studies were performed on a 1.5T scanner. Multi-echo spin-echo scans with a fixed TR and increasing TE values of 6 ms, 9 ms, 12 ms, 15 ms, and 18 ms (spaced at 3 ms intervals) were used. Post-processing of the images to calculate mean relaxivity, R2, included drawing of regions of interest to include the whole liver on mid-slice. The relationship between liver R2 values and estimated LIC calculated with in-house analysis and values reported by an external company (FerriScan®, Resonance Health, Australia) were assessed with correlation coefficients and Bland-Altman difference plots. Continuous variables are presented as mean ± standard deviation. Significance was set at p value < 0.05. RESULTS: 474 studies from 175 patients were included in the study (mean age 10.4 ± 4.2 years (range 1-18 years); 254 studies from girls, 220 studies from boys). LIC ranged from 0.6 to 43 mg/g dry tissue, covering a broad range from normal levels to extremely high iron levels. Linearity between proprietary and in-house methods was excellent across the observed range for R2 (31.5 to 334.8 s-1); showing a correlation coefficient of r = 0.87, p < 0.001. Bland-Altman R2 difference plot between the two methods shows a mean bias of + 21.5 s-1 (range - 47.0 to + 90.0 s-1 between two standard deviations). LIC reported by FerriScan® compared with LIC estimated in-house with R2 as reported by FerriScan® agreed strongly, (r = 1.0, p < 0.001). CONCLUSION: R2 relaxometry MR imaging for liver iron concentration estimation is reproducible between proprietary FDA-approved commercial software and in-house analysis methods.

Biexponential R2* relaxometry for estimation of liver iron concentration in children: A better fit for high liver iron states.

BACKGROUND: R2* relaxometry's capacity to calculate liver iron concentration (LIC) is limited in patients with severe overload. Hemosiderin increases in these patients, which exhibits a non-monoexponential decay that renders a failed R2* analysis. PURPOSE/HYPOTHESIS: To evaluate a biexponential R2* relaxometry model in children with different ranges of iron overload. STUDY TYPE: Retrospective. POPULATION: In all, 181 children with different conditions associated with iron overload. FIELD STRENGTH/SEQUENCE: 1.5T, T2 *-weighted gradient echo sequence. ASSESSMENT: Bi- and monoexponential R2* relaxometry were measured in the liver using two regions of interest (ROIs) using a nonproprietary software: one encompassing the whole liver parenchyma (ROI-1) and the other only the periphery (ROI-2). These were drawn by a single trained observer. The residuals for each fitting model were estimated. A ratio between the residuals of the mono- and biexponential models was calculated to identify the best fitting model. Patients with 1) residual ratio ≥1.5 and 2) R2*fast ≥R2*slow were considered as having a predominant biexponential behavior. STATISTICAL TESTS: Nonparametric tests, Bland-Altman plots, linear correlation, intraclass correlation coefficient. Patients were divided according to their LIC into stable (n = 23), mild (n = 58), moderate (n = 61), and severe (n = 39). RESULTS: The biexponential model was more suitable for patients with severe iron overload when compared with the other three LIC categories (P < 0.001) for both ROIs. For ROI-1, 37 subjects met criteria for a predominant biexponential behavior. The slow component (5.7%) had a lower fraction than the fast component (94.2%). For ROI-2, 22 subjects met criteria for a predominant biexponential behavior. The slow component (4.7%) had a lower fraction than the fast component (95.2%). The intraobserver variability between both ROIs was excellent. DATA CONCLUSION: The biexponential R2* relaxometry model is more suitable in children with severe iron overload. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1191-1198.