BACKGROUND: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors.
METHODS: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13).
RESULTS: The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2.
CONCLUSIONS: The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

UNASSIGNED: The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots.
UNASSIGNED: The research datasets were acquired from the Scopus database. The search terms used were \'reward\' AND \'human\' AND \'diffusion imaging\' OR \'diffusion tensor imaging\' OR \'diffusion MRI\' OR \'diffusion-weighted imaging\' OR \'tractography\' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing\'s Publish or Perish and VOSviewer software.
UNASSIGNED: The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while the top keywords in human reward research were \'diffusion MRI\', \'adolescence\', \'depression\' and \'reward-related brain areas\'.
UNASSIGNED: These findings may serve as researchers\' references to find collaborative authors, relevant journals, cooperative countries/institutions, and hot topics related to dMRI and reward research.

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups.

暂无摘要。

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3-6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems.

Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density.

Understanding the brain\'s mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management.

UNASSIGNED: Double-negative prostate cancer, an androgen receptor-independent prostate cancer without features of neuroendocrine tumors, is refractory to treatment but could be an ideal candidate for individualized treatment.
UNASSIGNED: An 85-year-old patient with metastatic castration-resistant prostate cancer without prostate-specific antigen progression presented with local recurrence and liver and lung metastases 6 months after orchiectomy and apalutamide. A liver tumor biopsy led to a diagnosis of double-negative prostate cancer. FoundationOne® CDx showed BRCA2 mutation and high tumor mutation burden. Olaparib and pembrolizumab were administered sequentially, and the patient responded to each treatment for 5 months until radiographic progression.
UNASSIGNED: Sequential use of olaparib and pembrolizumab may be effective for double-negative prostate cancer with BRCA2 mutations and high tumor mutation burden.

暂无摘要。

OBJECTIVE: To determine the diagnostic efficiencies of multiple diffusion-weighted imaging (DWI) techniques for hepatic fibrosis (HF) staging under the premise of high inter-examiner reliability.
METHODS: Participants with biopsy-confirmed HF were recruited and divided into the early HF (EHF) and advanced HF (AHF) groups; healthy volunteers (HVs) served as controls. Two examiners analyzed intravoxel incoherent motion (IVIM) using the IVIM-DWI and diffusion kurtosis imaging (DKI) models. Intravoxel incoherent motion-DWI, DKI, and diffusion tensor imaging parameters with intraclass correlation coefficients (ICCs) of ≥0.6 were used to create regression models: HVs vs. EHF and EHF vs. AHF.
RESULTS: We enrolled 48 HVs, 59 EHF patients, and 38 AHF patients. Mean, radial, and axial kurtosis; fractional anisotropy; mean, radial, and axial diffusivity; and α exhibited excellent reliability (ICCs: 0.80-0.98). Fractional anisotropy of kurtosis, f, and apparent diffusion coefficient showed good reliability (ICCs: 0.69-0.92). The real (0.58-0.67), pseudo- (0.27-0.76), and distributed diffusion coefficients (0.58-0.67) showed low reliability. In the HVs versus (vs.) EHF model, α (p=0.008) and ADC (p=0.011) presented statistical differences (area under curve [AUC]: 0.710). In the EHF vs. AHF model, α (p=0.04) and distributed diffusion coefficient (p=0.02) presented significant differences (AUC: 0.758).
CONCLUSIONS: Under the premise of high inter-examiner reliability, DWI and IVIM-derived stretched-exponential model parameters may help stage HF.