Diffusion tensor imaging changes in patients with glioma-associated seizures.

INTRODUCTION: Structural white matter changes associated with certain epilepsy subtypes have been demonstrated using diffusion tensor imaging (DTI). This observational study aims to identify potential water diffusion abnormalities in glioma patients with associated seizures. METHODS: Two cohorts from two centers were analyzed independently: (A) Prospectively recruited patients diagnosed with glioma who received preoperative DTI to measure mean diffusivity (MD) and fractional anisotropy (FA) in regions-of-interest (ROIs) including the marginal tumor zone (TU), adjacent peritumoral white matter as well as distant ipsilateral and contralateral white matter and cortex. Data were compared between patients with and without seizures and tested for statistical significance. (B) A retrospective cohort using an alternative technical approach sampling ROIs in contrast enhancement, necrosis, non-enhancing tumor, marginal non-enhancing tumor zone, peritumoral tissue, edema and non-tumorous tissue. RESULTS: (A) The prospective study cohort consisted of 23 patients with 12 (52.2%) presenting with a history of seizures. There were no significant seizure-associated differences in MD or FA for non-tumor white matter or cortical areas. MD-TU was significantly lower in patients with seizures (p = 0.005). (B) In the retrospective cohort consisting of 46 patients with a seizure incidence of 50.0%, significantly decreased normalized values of MD were observed for non-enhancing tumor regions of non-glioblastoma multiforme (GBM) cases in patients with seizures (p = 0.022). CONCLUSION: DTI analyses in glioma patients demonstrated seizure-associated diffusion restrictions in certain tumor-related areas. No other structural abnormalities in adjacent or distant white matter or cortical regions were detected.

Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.

PURPOSE: We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1+-inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. METHODS: Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. RESULTS: In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. CONCLUSION: The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.

Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets.

BACKGROUND: An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. MATERIAL AND METHODS: Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. RESULTS: Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. CONCLUSION: The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Correction: Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.

[This corrects the article DOI: 10.1371/journal.pone.0228113.].

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.

INTRODUCTION: In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. MATERIALS AND METHODS: Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric. RESULTS: The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities. CONCLUSION: Spatial features should be integrated to improve lesion outcome prediction using machine learning models.