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.

A Segmentation Framework With Unsupervised Learning-Based Label Mapper for the Ventricular Target of Intracranial Germ Cell Tumor.

Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153.01 s, and the average target segmentation accuracy can reach 84.69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.

Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas.

PURPOSE: To establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). MATERIALS AND METHODS: A primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. RESULTS: 35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. CONCLUSION: dMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.

An Unsupervised Convolution Neural Network for Deformable Registration of Mono/Multi-Modality Medical Images.

Image registration is a fundamental and crucial step in medical image analysis. However, due to the differences between mono-mode and multi-mode registration tasks and the complexity of the corresponding relationship between multimode image intensity, the existing unsupervised methods based on deep learning can hardly achieve the two registration tasks simultaneously. In this paper, we proposed a novel approach to register both mono- and multi-mode images $\color{blue}{\text{in a differentiable }}$. By approximately calculating the mutual information in a $\color{blue}{\text{differentiable}}$ form and combining it with CNN, the deformation field can be predicted quickly and accurately without any prior information about the image intensity relationship. The registration process is implemented in an unsupervised manner, avoiding the need for the ground truth of the deformation field. We utilize two public datasets to evaluate the performance of the algorithm for mono-mode and multi-mode image registration, which confirms the effectiveness and feasibility of our method. In addition, the experiments on patient data also demonstrate the practicability and robustness of the proposed method.