A histopathological contribution to supratentorial glioma grading, definition of mixed gliomas and recognition of low grade glioma with Rosenthal fibers

Identification of association fibers using ex vivo diffusion tractography in Alexander disease brains

BACKGROUND AND PURPOSE

Alexander disease (AxD) is a neurodegenerative disorder caused by heterozygous Glial Fibrillary Acidic Protein mutation. The characteristic structural findings of AxD, such as leukodystrophic features, are well known, while association fibers of AxD remain uninvestigated. The aim of this study was to explore global and subcortical fibers in four brains with AxD using ex vivo diffusion tractography METHODS: High-angular-resolution diffusion magnetic resonance imaging (HARDI) tractography and diffusion-tensor imaging (DTI) tractography were used to evaluate long and short association fibers and compared to histological findings in brain specimens obtained from four donors with AxD and two donors without neurological disorders RESULTS: AxD brains showed impairment of long association fibers, except for the arcuate fasciculus and cingulum bundle, and abnormal trajectories of the inferior longitudinal and fronto-occipital fasciculi on HARDI tractography and loss of multidirectionality in subcortical fibers on DTI tractography. In histological studies, AxD brains showed diffuse low density on Klüver-Barrera and neurofilament staining and sporadic Rosenthal fibers on hematoxylin and eosin staining CONCLUSIONS: This study describes the spatial distribution of degenerations of short and long association fibers in AxD brains using combined tractography and pathological findings.

Clinical characteristics of Alexander disease

Alexander disease (ALXDRD) is a primary astrocyte disease caused by GFAP gene mutation. The clinical features of ALXDRD vary from infantile-onset cerebral white matter involvement to adult-onset brainstem involvement. Several studies revealed that the level of GFAP overexpression is correlated with disease severity, and basic research on therapies to reduce abnormal GFAP accumulation has recently been published. Therefore, the accumulation of clinical data to advance understanding of the natural history is essential for clinical trials expected in the future. This review focuses on the clinical characteristics of ALXDRD including the clinical symptoms, imaging findings and genetics to provide diagnostic information useful in daily clinical practice.

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

Multiparametric characterization of grade 2 glioma subtypes using magnetic resonance spectroscopic, perfusion, and diffusion imaging

BACKGROUND AND PURPOSE

The purpose of this study was to derive quantitative parameters from magnetic resonance (MR) spectroscopic, perfusion, and diffusion imaging of grade 2 gliomas according to the World Health Organization and to investigate how these multiple imaging modalities can contribute to evaluating their histologic subtypes and spatial characteristics.

MATERIALS AND METHODS

MR spectroscopic, perfusion, and diffusion images from 56 patients with newly diagnosed grade 2 glioma (24 oligodendrogliomas, 18 astrocytomas, and 14 oligoastrocytomas) were retrospectively studied. Metabolite intensities, relative cerebral blood volume (rCBV), and apparent diffusion coefficient (ADC) were statistically evaluated.

RESULTS

The 75th percentile rCBV and median ADC were significantly different between oligodendrogliomas and astrocytomas (P < .0001) and between oligodendrogliomas and oligoastrocytomas (P < .001). Logistic regression analysis identified both 75th percentile rCBV and median ADC as significant variables in the differentiation of oligodendrogliomas from astrocytomas and oligoastrocytomas. Group differences in metabolite intensities were not significant, but there was a much larger variation in the volumes and maximum values of metabolic abnormalities for patients with oligodendroglioma compared with the other tumor subtypes.

CONCLUSIONS

Perfusion and diffusion imaging provide quantitative MR parameters that can help to differentiate grade 2 oligodendrogliomas from grade 2 astrocytomas and oligoastrocytomas. The large variations in the magnitude and spatial extent of the metabolic lesions between patients and the fact that their values are not correlated with the other imaging parameters indicate that MR spectroscopic imaging may provide complementary information that is helpful in targeting therapy, evaluating residual disease, and assessing response to therapy.

Sub-classification of low-grade cerebellar astrocytoma: is it clinically meaningful?

OBJECTIVES

The objectives were to identify prognostic factors for the survival of children with cerebellar astrocytoma, and to evaluate the reproducibility and prognostic value of histological sub-classification and grading.

METHODS

Children aged 0-14 years treated in Denmark for a cerebellar astrocytoma in the period 1960-1984 were included and followed until January 2001 or until their death. The histological specimens from each patient were reviewed for revised grading and classification according to three different classification schemes: the WHO, the Kernohan and the Daumas-Duport grading systems.

RESULTS

The overall survival rate was 81% after a follow-up time of 15-40 years. The significant positive prognostic factors for survival were "surgically gross-total removal" of the tumour at surgery and location of the tumour in the cerebellum proper as opposed to location in the fourth ventricle. No difference in survival time was demonstrated when we compared pilocytic astrocytoma and fibrillary astrocytoma. Moreover, we found that the Kernohan and the WHO classification systems had no predictive value and that the Daumas-Duport system is unsuitable as a prognostic tool for low-grade posterior fossa astrocytomas.

CONCLUSION

Discordant observations due to interobserver variability make histological sub-classification of low-grade cerebellar astrocytomas in children insufficient for predicting prognosis and biological behaviour. Similar survival rates in a population of paediatric low-grade cerebellar astrocytomas of grades I and II indicate that tumour grade has no prognostic significance within this group of patients. "Surgically gross-total removal", especially if the tumour is located in the fourth ventricle is of the highest importance for long-term survival. Histological sub-classification of the tumours has no predictive value.

Proton magnetic resonance spectroscopic imaging can predict length of survival in patients with supratentorial gliomas

OBJECTIVE

We compared the ability of proton magnetic resonance spectroscopic imaging ((1)H-MRSI) measures with that of standard clinicopathological measures to predict length of survival in patients with supratentorial gliomas.

METHODS

We developed two sets of leave-one-out logistic regression models based on either 1) intratumoral (1)H-MRSI features, including maximum values of a) choline and b) lactate-lipid, c) number of (1)H-MRSI voxels with low N-acetyl group values, and d) number of (1)H-MRSI voxels with high lactate-lipid values, all (a-d) of which were normalized to creatine in normal-appearing brain, or 2) standard clinicopathological features, including a) tumor histopathological grade, b) patient age, c) performance of surgical debulking, and d) tumor diagnosis (i.e., oligodendroglioma, astrocytoma). We assessed the accuracy of these two models in predicting patient survival for 6, 12, 24, and 48 months by performing receiver operating characteristic curve analysis. Cox proportional hazards analysis was performed to assess the extent to which patient survival could be explained by the above predictors. We then performed a series of leave-one-out linear multiple regression analyses to determine how well patient survival could be predicted in a continuous fashion.

RESULTS

The results of using the models based on (1)H-MRSI and clinicopathological features were equally good, accounting for 81 and 64% of the variability (r(2)) in patients' actual survival durations. All features except number of (1)H-MRSI voxels with lactate-lipid/creatine values of at least 1 were significant predictors of survival in the (1)H-MRSI model. Two features (tumor grade and debulking) were found to be significant predictors in the clinicopathological model. Survival as a continuous variable was predicted accurately on the basis of the (1)H-MRSI data (r = 0.77, P < 0.001; median prediction error, 1.7 mo).

CONCLUSION

Our results suggest that appropriate analysis of (1)H-MRSI data can predict survival in patients with supratentorial gliomas at least as accurately as data derived from more invasive clinicopathological features.

Atypical focal MRI lesions in a case of juvenile Alexander's disease

We present a juvenile case of Alexander's disease with atypical focal magnetic resonance imaging-detected lesions and elevated levels of lactate in cerebrospinal fluid. The diagnosis was based on the neuropathological finding of a diffuse accumulation of Rosenthal fibers within the brain and the spinal cord. The diagnosis was confirmed by detection of a mutation in exon 1 at nucleotide position 249 of glial fibrillary acidic protein cDNA, a finding previously reported in cases of infantile Alexander's disease.

Posterior fossa oligodendroglioma

Oligodendroglioma are the tumors of glial cells. They are rare in children and are more common in the cerebral hemispheres. A rare case of infratentorial oligodendroglioma in a female child is being reported here.