White matter alterations in pediatric brainstem glioma: An national brain tumor registry of China study

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

Diffusion MRI is valuable in brainstem glioma genotyping with quantitative measurements of white matter tracts

OBJECTIVES

To investigate the value of diffusion MRI (dMRI) in H3K27M genotyping of brainstem glioma (BSG).

METHODS

A primary cohort of BSG patients with dMRI data (b = 0, 1000 and 2000 s/mm2) and H3K27M mutation information were included. A total of 13 diffusion tensor and kurtosis imaging (DTI; DKI) metrics were calculated, then 17 whole-tumor histogram features and 29 along-tract white matter (WM) microstructural measurements were extracted from each metric and assessed within genotypes. After feature selection through univariate analysis and the least absolute shrinkage and selection operator method, multivariate logistic regression was used to build dMRI-derived genotyping models based on retained tumor and WM features separately and jointly. Model performances were tested using ROC curves and compared by the DeLong approach. A nomogram incorporating the best-performing dMRI model and clinical variables was generated by multivariate logistic regression and validated in an independent cohort of 27 BSG patients.

RESULTS

At total of 117 patients (80 H3K27M-mutant) were included in the primary cohort. In total, 29 tumor histogram features and 41 WM tract measurements were selected for subsequent genotyping model construction. Incorporating WM tract measurements significantly improved diagnostic performances (p < 0.05). The model incorporating tumor and WM features from both DKI and DTI metrics showed the best performance (AUC = 0.9311). The nomogram combining this dMRI model and clinical variables achieved AUCs of 0.9321 and 0.8951 in the primary and validation cohort respectively.

CONCLUSIONS

dMRI is valuable in BSG genotyping. Tumor diffusion histogram features are useful in genotyping, and WM tract measurements are more valuable in improving genotyping performance.

CLINICAL RELEVANCE STATEMENT

This study found that diffusion MRI is valuable in predicting H3K27M mutation in brainstem gliomas, which is helpful to realize the noninvasive detection of brainstem glioma genotypes and improve the diagnosis of brainstem glioma.

KEY POINTS

• Diffusion MRI has significant value in brainstem glioma H3K27M genotyping, and models with satisfactory performances were built. • Whole-tumor diffusion histogram features are useful in H3K27M genotyping, and quantitative measurements of white matter tracts are valuable as they have the potential to improve model performance. • The model combining the most discriminative diffusion MRI model and clinical variables can help make clinical decision.

Cortical myelin and thickness mapping provide insights into whole-brain tumor burden in diffuse midline glioma

Systemic infiltration is a hallmark of diffuse midline glioma pathogenesis, which can trigger distant disturbances in cortical structure. However, the existence and effects of these changes have been underexamined. This study aimed to investigate whole-brain cortical myelin and thickness alternations induced by diffuse midline glioma. High-resolution T1- and T2-weighted images were acquired from 90 patients with diffuse midline glioma with H3 K27-altered and 64 patients with wild-type and 86 healthy controls. Cortical thickness and myelin content was calculated using Human Connectome Project pipeline. Significant differences in cortical thickness and myelin content were detected among groups. Short-term survival prediction model was constructed using automated machine learning. Compared with healthy controls, diffuse midline glioma with H3 K27-altered patients showed significantly reduced cortical myelin in bilateral precentral gyrus, postcentral gyrus, insular, parahippocampal gyrus, fusiform gyrus, and cingulate gyrus, whereas diffuse midline glioma with H3 K27 wild-type patients exhibited well-preserved myelin content. Furtherly, when comparing diffuse midline glioma with H3 K27-altered and diffuse midline glioma with H3 K27 wild-type, the decreased cortical thickness in parietal and occipital regions along with demyelination in medial orbitofrontal cortex was observed in diffuse midline glioma with H3 K27-altered. Notably, a combination of cortical features and tumor radiomics allowed short-term survival prediction with accuracy 0.80 and AUC 0.84. These findings may aid clinicians in tailoring therapeutic approaches based on cortical characteristics, potentially enhancing the efficacy of current and future treatment modalities.

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.