Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI.

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.

Structure-function coupling in highly sampled individual brains.

Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 min of diffusion-weighted MRI for SC and 360 min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. METHODS: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. RESULTS: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. CONCLUSION: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies.

HYPOTHESIS: This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. BACKGROUND: The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. RESULTS: The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. CONCLUSIONS: In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

Restriction-induced time-dependent transcytolemmal water exchange: Revisiting the Kӓrger exchange model.

The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 µm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.

[Comparative study of ultra-high field diffusion-weighted MRI imaging between hepatocellular carcinoma and paracancerous, distant cancerous, and background liver tissues].

Objective: To investigate the differences in multi-b-value apparent diffusion coefficient (ADC) and exponential apparent diffusion coefficient (eADC) between hepatocellular carcinoma (HCC) and paracancerous liver tissue, distant cancerous liver tissue, and background liver tissues by ultra-high field 3.0T diffusion-weighted (DWI) MRI imaging. Methods: Sixty-eight consecutive HCC cases confirmed by surgical pathology from January 2018 to October 2021 were enrolled and divided into a cirrhosis (n=39) and a non-cirrhosis group (n=29) according to the presence or absence of cirrhosis.The average ADC and eADC of liver tissues of paracancerous (including proximal and distal), distant cancerous, and background were measured by DWI images with diffusion sensitivity factors (b) of 50, 100, 400, 600 s/mm2, and 1 000 s/mm2, respectively. The Kruskal-Wallis H test and Bonferroni method were used to test the differences between the measured values of the five tissues. The statistical differences were used to evaluate the diagnostic efficacy of the five tissues by parametric receiver operating characteristic (ROC) curve and area under the curve (AUC). Results: The comparison of average ADC and eADC among five types of tissues in the liver cirrhosis group showed that the average ADC and eADC measured at b values of 50, 100, 400, and 600 s/mm2 had statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distal paracancerous, distant cancerous, and background liver tissue, as well as the average ADC measured at b=1 000 s/mm2 between cancerous and proximal paracancerous tissue. The average ADC and eADC in the non-cirrhosis group had statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distant paracancerous, distant cancerous, and background liver tissue measured at b values of 50, 100, and 400 s/mm2, respectively. The average ADC and eADC measured at b=600 s/mm2 showed statistically significant differences (adjusted P<0.005) between cancerous and proximal paracancerous, distal paracancerous, and distant cancerous liver tissue, as well as the average ADC measured at b=1 000 s/mm2 between cancerous and distal paracancerous, and distant cancerous liver tissue. The average ADC and eADC in the cirrhosis group had no statistically significant difference between the proximal paracancerous and the distant cancerous, as well as the background liver tissue measured at b-values of 50, 100, 400, 600, and 1 000 s/mm2, respectively (adjusted P>0.005), while there were statistically significant differences (adjusted P<0.005) in the average ADC values in the non-cirrhosis group between the proximal paracancerous and the distant paracancerous and background liver tissues at b=50 s/mm2, as well as the average ADC and eADC values between the proximal paracancerous and the distant liver tissues at b=100 s/mm2. The average ADC and eADC values measured in the cirrhosis group and non-cirrhosis group had no statistically significant difference between the distant paracancerous, distant cancerous, and background liver tissue (adjusted P>0.005). The efficacy of average ADC and eADC in distinguishing five types of tissues (cancerous and proximal paracancerous, distant paracancerous, distant cancerous, and background liver tissue) showed that in the cirrhosis group, the diagnostic efficacy was best at b=50 s/mm2. The area under the ROC curve (AUC) of average ADC was 0.815, 0.828, 0.855, and 0.855, respectively, and the AUC of average eADC was 0.815, 0.830, 0.856, and 0.855, respectively. The diagnostic efficacy was best in the non cirrhosis group at b=100 s/mm2, with average ADC AUCs of 0.787, 0.823, 0.841, and 0.821, and average eADC AUCs of 0.836, 0.874, 0.893, and 0.873, respectively. The AUC of the average ADC in the non-cirrhosis group for distinguishing between proximal paracancerous and distant cancerous liver tissues, as well as proximal paracancerous and background liver tissues, with b=50 s/mm2, were 0.605 and 0.604, respectively. The average AUC of ADC and eADC for distinguishing between proximal paracancerous and distant liver tissues with b=100 s/mm2 were 0.619 and 0.620, respectively. Conclusion: The average ADC and eADC measured by multiple b-values are helpful in distinguishing HCC from proximal paracancerous, distal paracancerous, distant-cancerous, and background liver tissues in patients with cirrhosis and non-cirrhosis, while the average ADC and eADC at b=50 s/mm2 and 100 s/mm2 exhibit differences between the proximal paracancerous from the distant cancerous liver tissue and background liver tissue in patients with non-cirrhosis.

Coupled pulsatile vascular and paravascular fluid dynamics in the human brain.

BACKGROUND: Cardiac pulsation propels blood through the cerebrovascular network to maintain cerebral homeostasis. The cerebrovascular network is uniquely surrounded by paravascular cerebrospinal fluid (pCSF), which plays a crucial role in waste removal, and its flow is suspected to be driven by arterial pulsations. Despite its importance, the relationship between vascular and paravascular fluid dynamics throughout the cardiac cycle remains poorly understood in humans. METHODS: In this study, we developed a non-invasive neuroimaging approach to investigate the coupling between pulsatile vascular and pCSF dynamics within the subarachnoid space of the human brain. Resting-state functional MRI (fMRI) and dynamic diffusion-weighted imaging (dynDWI) were retrospectively cardiac-aligned to represent cerebral hemodynamics and pCSF motion, respectively. We measured the time between peaks (∆TTP) in d d ϕ f M R I and dynDWI waveforms and measured their coupling by calculating the waveforms correlation after peak alignment (correlation at aligned peaks). We compared the ∆TTP and correlation at aligned peaks between younger [mean age: 27.9 (3.3) years, n = 9] and older adults [mean age: 70.5 (6.6) years, n = 20], and assessed their reproducibility within subjects and across different imaging protocols. RESULTS: Hemodynamic changes consistently precede pCSF motion. ∆TTP was significantly shorter in younger adults compared to older adults (-0.015 vs. -0.069, p < 0.05). The correlation at aligned peaks were high and did not differ between younger and older adults (0.833 vs. 0.776, p = 0.153). The ∆TTP and correlation at aligned peaks were robust across fMRI protocols (∆TTP: -0.15 vs. -0.053, p = 0.239; correlation at aligned peaks: 0.813 vs. 0.812, p = 0.985) and demonstrated good to excellent within-subject reproducibility (∆TTP: intraclass correlation coefficient = 0.36; correlation at aligned peaks: intraclass correlation coefficient = 0.89). CONCLUSION: This study proposes a non-invasive technique to evaluate vascular and paravascular fluid dynamics. Our findings reveal a consistent and robust cardiac pulsation-driven coupling between cerebral hemodynamics and pCSF dynamics in both younger and older adults.

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

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. CONCLUSION: 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.

Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging.

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.

The macroscale routing mechanism of structural brain connectivity related to body mass index.

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.

Monoexponential and advanced diffusion-weighted imaging for hepatic fibrosis staging based on high inter-examiner reliability.

OBJECTIVES: 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). CONCLUSION: Under the premise of high inter-examiner reliability, DWI and IVIM-derived stretched-exponential model parameters may help stage HF.

APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges.

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.

Dysfunction of thalamocortical circuits in early-onset schizophrenia.

Previous studies have demonstrated that the thalamus is involved in multiple functional circuits in participants with schizophrenia. However, less is known about the thalamocortical circuit in the rare subtype of early-onset schizophrenia. A total of 110 participants with early-onset schizophrenia (47 antipsychotic-naive patients) and 70 matched healthy controls were recruited and underwent resting-state functional and diffusion-weighted magnetic resonance imaging scans. A data-driven parcellation method that combined the high spatial resolution of diffusion magnetic resonance imaging and the high sensitivity of functional magnetic resonance imaging was used to divide the thalamus. Next, the functional connectivity between each thalamic subdivision and the cortex/cerebellum was investigated. Compared to healthy controls, individuals with early-onset schizophrenia exhibited hypoconnectivity between subdivisions of the thalamus and the frontoparietal network, visual network, ventral attention network, somatomotor network and cerebellum, and hyperconnectivity between subdivisions of thalamus and the parahippocampal and temporal gyrus, which were included in limbic network. The functional connectivity between the right posterior cingulate cortex and 1 subdivision of the thalamus (region of interest 1) was positively correlated with the general psychopathology scale score. This study showed that the specific thalamocortical dysconnection in individuals with early-onset schizophrenia involves the prefrontal, auditory and visual cortices, and cerebellum. This study identified thalamocortical connectivity as a potential biomarker and treatment target for early-onset schizophrenia.

Analysis of diffusion changes in cerebral tissues of Parki̇nson's patients who underwent subthalamic nucleus deep brain stimulation: Correlation of improvements in motor and neuropsychiatric symptoms.

OBJECTIVE: Parkinson's disease (PD) as a neurodegenerative disorder characterized by a reduction in both the quantity and functionality of dopaminergic neurons. This succinctly highlights the central pathological feature of PD and its association with dopaminergic neuron degeneration, which underlies the motor and non-motor symptoms of the disease. This study aims to elucidate the nuances of apparent diffusion coefficient (ADC) changes in different cerebral regions by after the bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) surgery of PD, as well as to investigate their potential interactions with the motor and neuropsychiatric spectrum. METHODS: Patients who underwent STN-DBS surgery for PD between 2017 and 2019 were included in this study. The results of diffusion magnetic resonance imaging (MRI), Unified Parkinson Disease Rating Scale (UPDRS) III scores, Beck and Hamilton depression tests were recorded before and at the 3rd month of postoperative stimulation. The data obtained were evaluated with the Wilcoxon signed rank test. Result of the statistical tests were within the 95 % confidence interval and p values were significant below 0.05. RESULTS: Our study was conducted with a total of 13 patients, 8 men and 5 women. As a result of measurements made in a total of 32 different regions, especially in the motor and neuropsychiatric areas of the brain, an increase in ADC values was found in all areas. ADC changes of eight localizations such as left corpus callosum, right corona radiata, left corona radiata, hippocampus, right insula, left superior cerebellar peduncle, left caudate nucleus and left putamen were statistically significant. UPDRS III scores improved by 57 % (p <0.05), and Beck and Hamilton depression scores by 25 % and 33 %, respectively (p> 0.05). CONCLUSIONS: This article implicate that bilateral STN-DBS surgery potentially exerts beneficial effects on both motor and neuropsychiatric symptomatology in individuals with PD. We believe that this therapeutic mechanism is hypothesized to involve modulation of diffusion alterations within distinct cerebral tissues.

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.

BACKGROUND AND PURPOSE: Radiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism. MATERIALS AND METHODS: From August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent test cohort (n = 69). MR spectroscopy (MRS) was also used in 230 patients. Nuclear MRS measured choline metabolites in 70 tissue samples. The performance was compared against European Society for Medical Oncology (ESMO) risk groups. A P < .05 denoted statistical significance. RESULTS: Rad-Score achieved 71.1% accuracy in the training and 71.0% in the testing cohorts. Incorporating clinical parameters of age, tumor type, size, and grade, Rad-Signature reached accuracies of 73.2% in training and 75.4% in testing cohorts, closely matching the performance to the post-operatively based ESMO's 70.7% and 78.3%. Rad-Score was significantly associated with increased total choline levels on MRS (P = .034) and tissue levels (P = .019). CONCLUSIONS: Development of a preoperative radiomics risk score, comparable to ESMO clinical standard and associated with altered choline metabolism, shows translational relevance for radiomics in high-risk EC patients. TRIAL REGISTRATION: This study was registered in ClinicalTrials.gov on 2015-08-01 with Identifier NCT02528864.

A "glympse" into neurodegeneration: Diffusion MRI and cerebrospinal fluid aquaporin-4 for the assessment of glymphatic system in Alzheimer's disease and other dementias.

The glymphatic system (GS) is a whole-brain perivascular network, consisting of three compartments: the periarterial and perivenous spaces and the interposed brain parenchyma. GS dysfunction has been implicated in neurodegenerative diseases, particularly Alzheimer's disease (AD). So far, comprehensive research on GS in humans has been limited by the absence of easily accessible biomarkers. Recently, promising non-invasive methods based on magnetic resonance imaging (MRI) along with aquaporin-4 (AQP4) quantification in the cerebrospinal fluid (CSF) were introduced for an indirect assessment of each of the three GS compartments. We recruited 111 consecutive subjects presenting with symptoms suggestive of degenerative cognitive decline, who underwent 3 T MRI scanning including multi-shell diffusion-weighted images. Forty nine out of 111 also underwent CSF examination with quantification of CSF-AQP4. CSF-AQP4 levels and MRI measures-including perivascular spaces (PVS) counts and volume fraction (PVSVF), white matter free water fraction (FW-WM) and mean kurtosis (MK-WM), diffusion tensor imaging analysis along the perivascular spaces (DTI-ALPS) (mean, left and right)-were compared among patients with AD (n = 47) and other neurodegenerative diseases (nAD = 24), patients with stable mild cognitive impairment (MCI = 17) and cognitively unimpaired (CU = 23) elderly people. Two runs of analysis were conducted, the first including all patients; the second after dividing both nAD and AD patients into two subgroups based on gray matter atrophy as a proxy of disease stage. Age, sex, years of education, and scanning time were included as confounding factors in the analyses. Considering the whole cohort, patients with AD showed significantly higher levels of CSF-AQP4 (exp(b) = 2.05, p = .005) and FW-WM FW-WM (exp(b) = 1.06, p = .043) than CU. AQP4 levels were also significantly higher in nAD in respect to CU (exp(b) = 2.98, p < .001). CSF-AQP4 and FW-WM were significantly higher in both less atrophic AD (exp(b) = 2.20, p = .006; exp(b) = 1.08, p = .019, respectively) and nAD patients (exp(b) = 2.66, p = .002; exp(b) = 1.10, p = .019, respectively) compared to CU subjects. Higher total (exp(b) = 1.59, p = .013) and centrum semiovale PVS counts (exp(b) = 1.89, p = .016), total (exp(b) = 1.50, p = .036) and WM PVSVF (exp(b) = 1.89, p = .005) together with lower MK-WM (exp(b) = 0.94, p = .006), mean and left ALPS (exp(b) = 0.91, p = .043; exp(b) = 0.88, p = .010 respectively) were observed in more atrophic AD patients in respect to CU. In addition, more atrophic nAD patients exhibited higher levels of AQP4 (exp(b) = 3.39, p = .002) than CU. Our results indicate significant changes in putative MRI biomarkers of GS and CSF-AQP4 levels in AD and in other neurodegenerative dementias, suggesting a close interaction between glymphatic dysfunction and neurodegeneration, particularly in the case of AD. However, the usefulness of some of these biomarkers as indirect and standalone indices of glymphatic activity may be hindered by their dependence on disease stage and structural brain damage.

Activation patterns of rotator-cuff muscles from quantitative IVIM DWI after physical testing.

BACKGROUND: The diagnostic value of clinical rotator cuff (RC) tests is controversial, with only sparse evidence available about their anatomical specificity. We prospectively assessed regional RC muscle activation patterns by means of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) after the execution of common clinical RC tests. METHODS: Ten healthy subjects (five males, five females) underwent three sessions of diffusion-weighted 3-T shoulder MRI before and after testing the supraspinatus (SSP, Jobe test, session 1), subscapularis (SSC, lift-off test, session 2, at least 1 week later), and infraspinatus muscle (ISP, external rotation test, session 3, another week later). IVIM parameters (perfusion fraction, f; pseudo-diffusion coefficient. D*; and their product, fD*) were measured in regions of interest placed in images of the SSP, SSC, ISP, and deltoid muscle. The Wilcoxon signed-rank test was used for group comparisons; p-values were adjusted using the Bonferroni correction. RESULTS: After all tests, fD* was significantly increased in the respective target muscles (SSP, SSC, or ISP; p ≤ 0.001). After SSP testing, an additional significant increase of fD* was observed in the deltoid, the SSC, and the ISP muscle (p < 0.001). After the SSC and ISP tests, no significant concomitant increase of any parameter was observed in the other RC muscles. CONCLUSION: IVIM revealed varying activation patterns of RC muscles for different clinical RC tests. For SSP testing, coactivation of the deltoid and other RC muscles was observed, implying limited anatomical specificity, while the tests for the SSC and ISP specifically activated their respective target muscle. RELEVANCE STATEMENT: Following clinical RC tests, IVIM MRI revealed that SSP testing led to shoulder muscle coactivation, while the SSC and ISP tests specifically activated the target muscles. KEY POINTS: In this study, intravoxel incoherent motion MRI depicted muscle activation following clinical rotator cuff tests. After supraspinatus testing, coactivation of surrounding shoulder girdle muscles was observed. Subscapularis and infraspinatus tests exhibited isolated activation of their respective target muscles.

Prediction of PD-L1 and Ki-67 status in primary central nervous system diffuse large B-cell lymphoma by diffusion and perfusion MRI: a preliminary study.

OBJECTIVE: To assess whether diffusion and perfusion MRI derived parameters could non-invasively predict PD-L1 and Ki-67 status in primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL). METHODS: We retrospectively analyzed DWI, DSC-PWI, and morphological MRI (mMRI) in 88 patients with PCNS-DLBCL. The mMRI features were compared using chi-square tests or Fisher exact test. Minimum ADC (ADCmin), mean ADC(ADCmean), relative minimum ADC (rADCmin), relative mean ADC (rADCmean), and relative maximum CBV (rCBVmax) values were compared in PCNS-DLBCL with different molecular status by using the Mann-Whitney U test. The diagnostic performances were evaluated by receiver operating characteristic curves. RESULTS: PCNS-DLBCL with high PD-L1 expression demonstrated a significantly higher ADCmin value than those with low PD-L1. The ADCmean and rADCmean values were significantly lower in PCNS-DLBCL with high Ki-67 status compared with those in low Ki-67 status. Other ADC, CBV parameters, and mMRI features did not show any association with these molecular statuses The diagnostic efficacy of ADC values in assessing PD-L1 and Ki-67 status was relatively low, with area under the curves (AUCs) values less than 0.7. CONCLUSIONS: DWI-derived ADC values can provide some relevant information about PD-L1 and Ki-67 status in PCNS-DLBCL, but may not be sufficient to predict their expression due to the rather low diagnostic performance.