Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma

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.

Dynamic susceptibility contrast and diffusion-weighted MRI in posterior fossa pilocytic astrocytoma and medulloblastoma

BACKGROUND AND PURPOSE

The utility of perfusion MRI in distinguishing between pilocytic astrocytoma (PA) and medulloblastoma (MB) is unclear. This study aimed to evaluate the diagnostic and prognostic performance of dynamic susceptibility contrast (DSC)-MRI parameters and apparent diffusion coefficient (ADC) values between PA and MB.

METHODS

Between January 2012 and August 2021, 49 (median, 7 years [range, 1-28 years]; 28 females) and 35 (median, 8 years [1-24 years]; 12 females) patients with pathologically confirmed PA and MB, respectively, were included. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and mean and minimal normalized ADC (nADCmean and nADCmin) values were calculated using volume-of-interest analyses. Diagnostic performance and Pearson's correlation with progression-free survival were also evaluated.

RESULTS

The MB group showed a significantly higher nrCBV and nrCBF (nrCBV: 1.69 [0.93-4.23] vs. 0.95 [range, 0.37-2.28], p = .0032; nrCBF: 1.62 [0.93-3.16] vs. 1.07 [0.46-2.26], p = .0084) and significantly lower nADCmean and nADCmin (nADCmean: 0.97 [0.70-1.68] vs. 2.21 [1.44-2.80], p < .001; nADCmin: 0.50 [0.19-0.89] vs. 1.42 [0.89-2.20], p < .001) than the PA group. All parameters exhibited good diagnostic ability (accuracy >0.80) with nADCmin achieving the highest score (accuracy = 1). A moderate correlation was found between nADCmean and progression-free survival for MB (r = 0.44, p = .0084).

CONCLUSIONS

DSC-MRI parameters and ADC values were useful for distinguishing between PA and MB. A lower ADC indicated an unfavorable MB prognosis, but the DSC-MRI parameters did not correlate with progression-free survival in either group.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Advanced MRI Findings in Medulloblastomas: Relationship to Genetic Subtypes, Histopathology, and Immunohistochemistry

BACKGROUND AND PURPOSE

For diagnosis of medulloblastoma, the updated World Health Organization classification now demands for genetic typing, defining more precisely the tumor biology, therapy, and prognosis. We investigated potential associations between magnetic resonance imaging (MRI) parameters including apparent diffusion coefficient (ADC) and neuropathologic features of medulloblastoma, focusing on genetic subtypes.

METHODS

This study was a retrospective single-center analysis of 32 patients (eight females, median age = 9 years [range, 1-57], mean 12.6 ± 11.3) from 2012 to 2019. Genetic subtypes (wingless [WNT]; sonic hedgehog [SHH]; non-WNT/non-SHH), histopathology, immunohistochemistry (p53, Ki67), and the following MRI parameters were correlated: tumor volume, location (midline, pontocerebellar, and cerebellar hemisphere), edema, hydrocephalus, metastatic disease (presence/absence and each), contrast-enhancement (minor, moderate, and distinct), cysts (none, small, and large), hemorrhage (none, minor, and major), and ADC_mean . The ADC_mean was calculated using manually set regions of interest within the solid tumor. Statistics comprised univariate and multivariate testing.

RESULTS

Out of 32 tumors, three tumors were WNT activated (9.4%), 13 (40.6%) SHH activated, and 16 (50.0%) non-WNT/non-SHH. Hemispherical location (n = 7/8, P = .003) and presence of edema (8/8; P < .001, specificity 100%, positive predictive value 100%) were significantly associated with SHH activation. The combined parameter "no edema + no metastatic disease + cysts" significantly discriminated WNT-activated from SHH-activated medulloblastoma (P = .036). ADC_mean (10^-6 mm² /s) was 484 for WNT-activated, 566 for SHH-activated, and 624 for non-WNT/non-SHH subtypes (P = .080). A significant negative correlation was found between ADC_mean and Ki67 (r = -.364, P = .040).

CONCLUSION

MRI analysis enabled noninvasive differentiation of SHH-activated medulloblastoma. ADC alone was not reliable for genetic characterization, but associated with tumor proliferation rate.

Pediatric Posterior Fossa Medulloblastoma: The Role of Diffusion Imaging in Identifying Molecular Groups

BACKGROUND AND PURPOSE

The molecular groups WNT activated (WNT), Sonic hedgehog activated (SHH), group 3, and group 4 are biologically and clinically distinct forms of medulloblastoma. We evaluated apparent diffusion coefficient (ADC) values' utility in differentiating/predicting medulloblastoma groups at the initial diagnostic imaging evaluation and prior to surgery.

METHODS

We retrospectively measured the ADC values of the enhancing, solid portion of the tumor (EST) and of the whole tumor (WT) and performed Kruskal-Wallis testing to compare the absolute tumor ADC values and cerebellar and thalamic ratios of three medulloblastoma groups (WNT, SHH, and group 3/group 4 combined).

RESULTS

Ninety-three children (65 males) were included. Fifty-seven children had group 3/group 4, 27 had SHH, and 9 had WNT medulloblastomas. The median absolute ADC values in the EST and WT were .719 × 10^-3 and .864 × 10^-3 mm² /s for group 3/group 4; .660 × 10^-3 and .965 × 10^-3 mm² /s for SHH; and .594 × 10^-3 and .728 × 10^-3 mm² /s for WNT medulloblastomas (P = .02 and .13). The median ratio of ADC values in the EST or the WT to normal cerebellar tissue was highest for group 3/group 4 and lowest for WNT medulloblastomas (P = .03 and .09), with similar results in pairwise comparisons of the corresponding thalamic ADC values (P = .02 and .06).

CONCLUSION

ADC analysis of a tumor's contrast-enhancing solid portion may aid preoperative molecular classification/prediction of pediatric medulloblastomas and may facilitate optimal surgical treatment planning, reducing surgery-induced morbidity.

Rheologically Essential Surfactant Proteins of the CSF Interacting with Periventricular White Matter Changes in Hydrocephalus Patients - Implications for CSF Dynamics and the Glymphatic System

Surfactant proteins (SP) are multi-systemic proteins playing crucial roles in the regulation of rheological properties of physiological fluids, host defense, and the clearance of potentially harmful metabolites. Hydrocephalus patients suffer from disturbed central nervous system (CNS) fluid homeostasis and exhibit remarkably altered SP concentrations within the cerebrospinal fluid (CSF). A connection between CSF-SPs, CSF flow, and ventricular dilatation, a morphological hallmark of hydrocephalus, has been reported previously. However, currently there are no studies investigating the link between rheologically active SPs and periventricular white matter changes caused by impaired CSF microcirculation in hydrocephalic conditions. Thus, the aim of this study was to assess their possible relationships. The present study included 47 individuals (27 healthy subjects and 20 hydrocephalus patients). CSF specimens were analyzed for concentrations of SP-A, SP-C, and SP-D by using enzyme-linked immunosorbent assays (ELISAs). Axial T2w turbo inversion recovery magnitude (TIRM) magnetic resonance imaging was employed in all cases. Using a custom-made MATLAB-based tool for quantification of magnetic resonance signal intensities in the brain, parameters related to disturbed deep white matter CSF microcirculation were estimated (TIRM signal intensity (SI)-mean, minimum, maximum, median, mode, standard deviation, and percentiles, p10th, p25th, p75th, p90th, as well as kurtosis, skewness, and entropy of the SI distribution). Subsequently, statistical analysis was performed (IBM SPSS 24™) to identify differences between hydrocephalic patients and healthy individuals and to further investigate the connections between CSF-SP changes and deep white matter signal intensities. SP-A (0.38 ± 0.23 vs. 0.76 ± 0.49 ng/ml) and SP-C (0.54 ± 0.28 vs. 1.27 ± 1.09 ng/ml) differed between healthy controls and hydrocephalus patients in a statistically significant manner. Also, corresponding quantification of white matter signal intensities revealed statistically significant differences between hydrocephalus patients and healthy individuals: SImean (370.41 ± 188.15 vs. 222.27 ± 99.86, p = 0.001), SImax (1115.30 ± 700.12 vs. 617.00 ± 459.34, p = 0.005), SImedian (321.40 ± 153.17 vs. 209.52 ± 84.86, p = 0.001), SImode (276.55 ± 125.63 vs. 197.26 ± 78.51, p = 0.011), SIstd (157.09 ± 110.07 vs. 81.71 ± 64.94, p = 0.005), SIp10 (229.10 ± 104.22 vs. 140.00 ± 63.12, p = 0.001), SIp25 (266.95 ± 122.62 vs. 175.63 ± 71.42, p = 0.002), SIp75 (428.80 ± 226.88 vs. 252.19 ± 110.91, p = 0.001), SIp90 (596.47 ± 345.61 vs. 322.06 ± 176.00, p = 0.001), skewness (1.19 ± 0.68 vs. 0.43 ± 1.19, p = 0.014), and entropy (5.36 ± 0.37 vs. 4.92 ± 0.51, p = 0.002). There were no differences regarding SP-D levels in hydrocephalus patients vs. healthy controls. In the acute hydrocephalic subgroup, correlations were as follows: SP-A showed a statistically significant correlation with SImax (r = 0.670, p = 0.024), SIstd (r = 0.697, p = 0.017), SIp90 (r = 0.621, p = 0.041), and inverse correlation with entropy (r = - 0.700, p = 0.016). SP-C correlated inversely with entropy (r = - 0.686, p = 0.020). For the chronic hydrocephalus subgroup, the following correlations were identified: SP-A correlated with kurtosis of the TIRM histogram (r = - 0.746, p = 0.021). SP-C correlated with SImean (r = - 0.688, p = 0.041), SImax (r = - 0.741, p = 0.022), SImedian (r = - 0.716, p = 0.030), SImode (r = - 0.765, p = 0.016), SIstd (r = - 0.671, p = 0.048), SIp25 (r = - 0.740, p = 0.023), SIp75 (r = - 0.672, p = 0.048), and SIp90 (r = - 0.667, p = 0.050). SP-D apparently does not play a major role in CSF fluid physiology. SP-A and SP-C are involved in different aspects of CNS fluid physiology. SP-A appears to play an essential compensatory role in acute hydrocephalus and seems less involved in chronic hydrocephalus. In contrary, SP-C profile and white matter changes are remarkably connected in CSF of chronic hydrocephalus patients. Considering the association between CSF flow phenomena, white matter changes, and SP-C profiles, the latter may especially contribute to the regulation of paravascular glymphatic physiology.