Asunto(s)

Neoplasias de la Mama , Imagen de Difusión por Resonancia Magnética , Humanos , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Detección Precoz del Cáncer , Tamizaje Masivo

Asunto(s)

Traumatismos Craneocerebrales , Humanos , Traumatismos Craneocerebrales/complicaciones , Trombosis/etiología , Trombosis/complicaciones , Imagen de Difusión por Resonancia Magnética/métodos , Niño , Embolia Intracraneal/etiología

RESUMEN

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.

Asunto(s)

Encéfalo , Líquido Cefalorraquídeo , Hidrodinámica , Imagen por Resonancia Magnética , Flujo Pulsátil , Humanos , Adulto , Anciano , Masculino , Femenino , Imagen por Resonancia Magnética/métodos , Líquido Cefalorraquídeo/fisiología , Líquido Cefalorraquídeo/diagnóstico por imagen , Encéfalo/irrigación sanguínea , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Flujo Pulsátil/fisiología , Circulación Cerebrovascular/fisiología , Hemodinámica/fisiología , Adulto Joven , Persona de Mediana Edad , Estudios Retrospectivos , Imagen de Difusión por Resonancia Magnética/métodos

RESUMEN

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.

Asunto(s)

Índice de Masa Corporal , Encéfalo , Humanos , Masculino , Adulto Joven , Femenino , Adulto , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Imagen de Difusión Tensora , Red Nerviosa/diagnóstico por imagen , Red Nerviosa/fisiología , Conectoma , Vías Nerviosas/diagnóstico por imagen , Vías Nerviosas/fisiología , Obesidad/diagnóstico por imagen , Obesidad/fisiopatología , Obesidad/patología , Imagen de Difusión por Resonancia Magnética

RESUMEN

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.

Asunto(s)

Imagen de Difusión por Resonancia Magnética , Cirrosis Hepática , Humanos , Cirrosis Hepática/diagnóstico por imagen , Cirrosis Hepática/patología , Imagen de Difusión por Resonancia Magnética/métodos , Femenino , Masculino , Persona de Mediana Edad , Adulto , Reproducibilidad de los Resultados , Variaciones Dependientes del Observador

RESUMEN

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.

Asunto(s)

Accidente Cerebrovascular Isquémico , Tomografía Computarizada por Rayos X , Humanos , Accidente Cerebrovascular Isquémico/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Imagen por Resonancia Magnética/métodos , Algoritmos , Imagen de Difusión por Resonancia Magnética/métodos , Isquemia Encefálica/diagnóstico por imagen , Masculino , Femenino , Anciano , Procesamiento de Imagen Asistido por Computador/métodos , Aprendizaje Profundo , Accidente Cerebrovascular/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Encéfalo/patología

RESUMEN

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.

Asunto(s)

Imagen de Difusión por Resonancia Magnética , Imagen de Difusión Tensora , Nervio Facial , Humanos , Imagen de Difusión Tensora/métodos , Nervio Facial/diagnóstico por imagen , Nervio Facial/anatomía & histología , Adulto , Masculino , Femenino , Imagen de Difusión por Resonancia Magnética/métodos , Adulto Joven , Anisotropía , Procesamiento de Imagen Asistido por Computador/métodos

RESUMEN

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.

Asunto(s)

Simulación por Computador , Imagen de Difusión por Resonancia Magnética , Agua , Imagen de Difusión por Resonancia Magnética/métodos , Agua/química , Humanos , Algoritmos , Difusión , Modelos Biológicos

RESUMEN

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.

Asunto(s)

Encéfalo , Imagen por Resonancia Magnética , Vías Nerviosas , Humanos , Masculino , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Adulto , Femenino , Imagen por Resonancia Magnética/métodos , Vías Nerviosas/fisiología , Vías Nerviosas/diagnóstico por imagen , Mapeo Encefálico/métodos , Adulto Joven , Imagen de Difusión por Resonancia Magnética , Descanso/fisiología , Sustancia Blanca/fisiología , Sustancia Blanca/diagnóstico por imagen

RESUMEN

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.

Asunto(s)

Carcinoma Hepatocelular , Imagen de Difusión por Resonancia Magnética , Neoplasias Hepáticas , Hígado , Humanos , Neoplasias Hepáticas/diagnóstico por imagen , Carcinoma Hepatocelular/diagnóstico por imagen , Imagen de Difusión por Resonancia Magnética/métodos , Hígado/diagnóstico por imagen , Hígado/patología , Cirrosis Hepática/diagnóstico por imagen , Masculino , Femenino , Persona de Mediana Edad

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Imagen de Difusión por Resonancia Magnética , Glioma , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Imagen de Difusión por Resonancia Magnética/métodos , Femenino , Masculino , Persona de Mediana Edad , Adulto , Anciano , Glioma/diagnóstico por imagen , Glioma/patología , Diagnóstico Diferencial , Clasificación del Tumor , Adulto Joven , Linfoma/diagnóstico por imagen , Linfoma/patología , Linfoma/diagnóstico , Meningioma/diagnóstico por imagen , Meningioma/patología , Curva ROC , Papiloma del Plexo Coroideo/diagnóstico por imagen , Papiloma del Plexo Coroideo/patología , Sensibilidad y Especificidad , Estudios Retrospectivos , Adolescente

RESUMEN

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.

Asunto(s)

Imagen de Difusión por Resonancia Magnética , Lengua , Humanos , Imagen de Difusión por Resonancia Magnética/métodos , Lengua/fisiología , Lengua/diagnóstico por imagen , Movimiento/fisiología , Músculo Esquelético/fisiología , Músculo Esquelético/diagnóstico por imagen , Adulto

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Isocitrato Deshidrogenasa , Antígeno Ki-67 , Mutación , Máquina de Vectores de Soporte , Humanos , Isocitrato Deshidrogenasa/genética , Glioma/diagnóstico por imagen , Glioma/genética , Glioma/metabolismo , Antígeno Ki-67/metabolismo , Antígeno Ki-67/genética , Persona de Mediana Edad , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Masculino , Estudios Retrospectivos , Adulto , Anciano , Imagen de Difusión por Resonancia Magnética/métodos , Imagen Multimodal , Adulto Joven , Imagen por Resonancia Magnética/métodos , Curva ROC , Medios de Contraste

RESUMEN

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.

Asunto(s)

Imagen de Difusión por Resonancia Magnética , Humanos , Imagen de Difusión por Resonancia Magnética/métodos , Pronóstico , Masculino , Femenino , Anciano , Persona de Mediana Edad , Accidente Cerebrovascular Isquémico/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Accidente Cerebrovascular/diagnóstico por imagen

RESUMEN

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=-0.12, CI=-0.14,-0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =-0.18, CI=-0.20,-0.15, P<0.00001).

Asunto(s)

Apolipoproteína E4 , Heterocigoto , Lipoproteínas LDL , Sustancia Blanca , Humanos , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Apolipoproteína E4/genética , Femenino , Masculino , Anciano , Lipoproteínas LDL/sangre , Anciano de 80 o más Años , Estudios de Cohortes , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/sangre , Enfermedad de Alzheimer/patología , Imagen de Difusión por Resonancia Magnética , Espectroscopía de Resonancia Magnética , Factores de Riesgo

RESUMEN

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Asunto(s)

Coma , Imagen Multimodal , Recuperación de la Función , Humanos , Persona de Mediana Edad , Coma/diagnóstico por imagen , Coma/fisiopatología , Adulto , Masculino , Femenino , Anciano , Recuperación de la Función/fisiología , Adolescente , Adulto Joven , Neuroimagen/métodos , Imagen por Resonancia Magnética , Estudios Prospectivos , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Estudios de Cohortes , Tomografía Computarizada por Tomografía de Emisión de Positrones , Imagen de Difusión por Resonancia Magnética

RESUMEN

AIM: Ki-67 is a marker of cell proliferation and is increasingly being used as a primary outcome measure in preoperative window studies of endometrial cancer (EC). This study explored the feasibility of using apparent diffusion coefficient (ADC) values in noninvasive prediction of Ki-67 expression levels in EC patients before surgery, and constructs a nomogram by combining clinical data. MATERIAL AND METHODS: This study retrospectively analyzed 280 EC patients who underwent preoperative magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in our hospital from January 2017 to February 2023. Evaluate the potential nonlinear relationship between ADC values and Ki-67 expression using the nomogram. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: The results of this study showed a nonlinear correlation between ADCmin and Ki-67 expression (nonlinear P = 0.019), and the nonlinear correlation between ADCmean and Ki-67 expression (nonlinear P = 0.019). In addition, this study constructed the nomogram by incorporating ADCmax, International Federation of Gynecology and Obstetrics (FIGO), and chemotherapy. The area under the curve (AUC) values of the ROC for nomogram, ADCmax, FIGO, chemotherapy and grade in the training set were 0.783, 0.718, 0.579, 0.636, and 0.654, respectively. In the validation set, the AUC values for nomogram, ADCmax, FIGO, chemotherapy, and grade were 0.820, 0.746, 0.558, 0.542, and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model. CONCLUSION: A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki-67 expression in EC patients before surgery.

Asunto(s)

Imagen de Difusión por Resonancia Magnética , Neoplasias Endometriales , Antígeno Ki-67 , Nomogramas , Humanos , Femenino , Neoplasias Endometriales/diagnóstico por imagen , Neoplasias Endometriales/metabolismo , Neoplasias Endometriales/cirugía , Neoplasias Endometriales/patología , Antígeno Ki-67/metabolismo , Persona de Mediana Edad , Estudios Retrospectivos , Imagen de Difusión por Resonancia Magnética/métodos , Anciano , Valor Predictivo de las Pruebas , Adulto , Cuidados Preoperatorios/métodos , Estudios de Factibilidad , Biomarcadores de Tumor/metabolismo

RESUMEN

Kidney diseases pose a global healthcare burden, with millions requiring renal replacement therapy. Ischemia/reperfusion injury (IRI) is a common pathology of acute kidney injury, causing hypoxia and subsequent inflammation-induced kidney damage. Accurate detection of acute kidney injury due to IRI is crucial for timely intervention. We used longitudinal, multi-parametric magnetic resonance imaging (MRI) employing arterial spin labelling (ASL), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE)-MRI to assess IRI induced changes in both the injured and healthy contralateral kidney, in a unilateral IRI mouse model (n = 9). Multi-parametric MRI demonstrated significant differences in kidney volume (p = 0.001), blood flow (p = 0.002), filtration coefficient (p = 0.038), glomerular filtration rate (p = 0.005) and apparent diffusion coefficient (p = 0.048) between the injured kidney and contralateral kidney on day 1 post-IRI surgery. Identification of the injured kidney using principal component analysis including most of the imaging parameters demonstrated an area under the curve (AUC) of 0.97. These results point to the utility of multi-parametric MRI in early detection of IRI-induced kidney damage suggesting that the combination of various MRI parameters may be suitable for monitoring the extent of injury in this model.

Asunto(s)

Lesión Renal Aguda , Modelos Animales de Enfermedad , Riñón , Imágenes de Resonancia Magnética Multiparamétrica , Daño por Reperfusión , Animales , Daño por Reperfusión/diagnóstico por imagen , Daño por Reperfusión/patología , Ratones , Lesión Renal Aguda/diagnóstico por imagen , Lesión Renal Aguda/etiología , Lesión Renal Aguda/patología , Riñón/diagnóstico por imagen , Riñón/patología , Imágenes de Resonancia Magnética Multiparamétrica/métodos , Masculino , Tasa de Filtración Glomerular , Ratones Endogámicos C57BL , Imagen de Difusión por Resonancia Magnética/métodos

RESUMEN

BACKGROUND: To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC). METHODS: This study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network-based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA). RESULTS: The optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935-1.000) and 0.885 (95% CI: 0.731-0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC-training = 0.671, 0.873, 0.833, and 0.814, AUC-test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively. CONCLUSIONS: The predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.

Asunto(s)

Aprendizaje Profundo , Imagen de Difusión por Resonancia Magnética , Neoplasias Endometriales , Inestabilidad de Microsatélites , Radiómica , Adulto , Anciano , Femenino , Humanos , Persona de Mediana Edad , Imagen de Difusión por Resonancia Magnética/métodos , Neoplasias Endometriales/genética , Neoplasias Endometriales/diagnóstico por imagen , Neoplasias Endometriales/patología , Estudios Retrospectivos , Curva ROC

RESUMEN

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.

Asunto(s)

Colina , Neoplasias Endometriales , Humanos , Femenino , Neoplasias Endometriales/diagnóstico por imagen , Neoplasias Endometriales/patología , Neoplasias Endometriales/metabolismo , Colina/metabolismo , Persona de Mediana Edad , Anciano , Medición de Riesgo/métodos , Imagen de Difusión por Resonancia Magnética/métodos , Recurrencia Local de Neoplasia/diagnóstico por imagen , Recurrencia Local de Neoplasia/metabolismo , Adulto , Espectroscopía de Resonancia Magnética/métodos , Metástasis Linfática/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Radiómica