Multiparametric magnetic resonance imaging-based assessment of the effect of adenomyosis on determining the depth of myometrial invasion in endometrial cancer.

Background: Accurate preoperative diagnosis of endometrial cancer (EC) with deep myometrial invasion (DMI) is critical to deciding whether to perform lymphadenectomy. However, the presence of adenomyosis makes distinguishing DMI from superficial myometrial invasion (SMI) on magnetic resonance imaging (MRI) challenging. We aimed to evaluate the accuracy of multiparametric MRI (mpMRI) in diagnosing DMI in EC coexisting with adenomyosis (EC-A) compared with EC without coexisting adenomyosis and to evaluate the effect of different adenomyosis subtypes on myometrial invasion (MI) depth in EC. Methods: Patients with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stage I EC who underwent preoperative MRI were consecutively included in this 2-center retrospective study. Institution 1 was searched from January 2017 to November 2022 and institution 2 was searched from June 2017 to March 2021. Patients were divided into 2 groups: group A, patients with EC-A; group B, EC patients without coexisting adenomyosis, matched 1:2 according to age ±5 years and tumor grade. A senior radiologist assessed the MRI adenomyosis classification in group A. Then, 2 radiologists (R1/R2) independently interpreted T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted contrast-enhanced (T1CE), and a combination of all images (mpMRI) respectively, and then assessed MI depth. Accuracy, sensitivity, specificity, and the areas under the receiver operating curve (AUC) were calculated. The chi-square test was used to compare the accuracy of diagnosing DMI. Interobserver agreement was evaluated using the Kappa test. Results: A total of 70 cases in group A and 140 cases in group B were included. The accuracy, sensitivity, and specificity of consensus were 94.3% [95% confidence interval (CI): 88.9-99.7%] vs. 92.1% (95% CI: 87.7-96.6%), 60.0% (95% CI: 17-92.7%) vs. 86.7% (95% CI: 68.4-95.6%), and 96.9% (95% CI: 88.4-95.5%) vs. 93.6% (95% CI: 86.8-97.2%) (group A vs. group B, respectively). There was no significant difference in the diagnostic accuracy of DMI on each sequence between the groups (Reviewer 1/Reviewer 2): PT2WI=0.14/0.17, PDWI=0.50/0.33, PT1CE=0.90/0.18, PmpMRI=0.50/0.37. The AUC for T2WI, DWI, T1CE, and mpMRI (Reviewer 1/Reviewer 2), respectively, were 0.54 (95% CI: 0.42-0.66)/0.78 (95% CI: 0.67-0.87), 0.63 (95% CI: 0.50-0.74)/0.77 (95% CI: 0.65-0.86), 0.69 (95% CI: 0.57-0.80)/0.79 (95% CI: 0.68-0.88), and 0.91 (95% CI: 0.82-0.97)/0.89 (95% CI: 0.79-0.95) (group A) and 0.83 (95% CI: 0.76-0.89)/0.85 (95% CI: 0.78-0.90), 0.83 (95% CI: 0.76-0.89)/0.86 (95% CI: 0.79-0.91), 0.88 (95% CI: 0.82-0.93)/0.86 (95% CI: 0.80-0.92), and 0.91 (95% CI: 0.85-0.95)/0.87 (95% CI: 0.80-0.92) (group B). Interobserver agreement was highest with mpMRI [κ=0.387/0.695 (case/control)]. The consensus results of MRI categorization of adenomyosis revealed no significant difference in the accuracy of diagnosing DMI by adenomyosis subtype (Pspatial relationship>0.99, Paffected area=0.52, Paffected pattern=0.58, Paffected size>0.99). Conclusions: The presence of adenomyosis or adenomyosis subtype had no significant effect on the interpretation of the depth of MI. T1CE can increase the contrast between adenomyosis and cancer foci; therefore, the information provided by T1CE should be valued.

Reduced functional connectivity induced by longitudinal alterations of structure and perfusion may be associated with cognitive impairment in patients on maintenance hemodialysis.

Hemodialysis (HD) leads to cognitive impairment; however, the pathophysiology of maintenance HD remains unclear. This study aimed to investigate the longitudinal alterations in gray matter volume (GMV) and cerebral blood flow (CBF) in patients on HD at follow-up compared with baseline, examine the alterations in functional connectivity (FC) by defining co-changed brain regions as seed points, and investigate the correlation between the co-changed brain regions and neuropsychological test scores. Twenty-seven patients with HD and 30 healthy controls were enrolled in this study. All participants underwent high-resolution T1-weighted imaging, arterial spin labeling, and functional MR imaging to measure GMV, CBF, and FC. The patients on HD were assessed at baseline and 3 years subsequently. The right and left medial superior frontal gyrus (SFGmed.L) exhibited significantly lower GMV and CBF in patients on HD at follow-up compared with baseline and lower FC between the SFGmed.L and left middle temporal gyrus (MTG.L). FC between the SFGmed.L and MTG.L was positively correlated with neuropsychological test scores in the HD group at follow-up. Reduced GMV and CBF may result in decreased FC between the SFGmed.L and MTG.L, which may be associated with cognitive impairment in patients on maintenance HD. Our findings provide unique insights into the pathological mechanisms of patients on maintenance HD with cognitive impairment.

Amygdala structural and functional reorganization as an indicator of affective dysfunction in patients with tinnitus.

The aim of this study was to systematically investigate structural and functional alterations in amygdala subregions using multimodal magnetic resonance imaging (MRI) in patients with tinnitus with or without affective dysfunction. Sixty patients with persistent tinnitus and 40 healthy controls (HCs) were recruited. Based on a questionnaire assessment, 26 and 34 patients were categorized into the tinnitus patients with affective dysfunction (TPAD) and tinnitus patients without affective dysfunction (TPWAD) groups, respectively. MRI-based measurements of gray matter volume, fractional anisotropy (FA), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC) were conducted within 14 amygdala subregions for intergroup comparisons. Associations between the MRI properties and clinical characteristics were estimated via partial correlation analyses. Compared with that of the HCs, the TPAD and TPWAD groups exhibited significant structural and functional changes, including white matter integrity (WMI), fALFF, ReHo, DC, and FC alterations, with more pronounced WMI changes in the TPAD group, predominantly within the left auxiliary basal or basomedial nucleus (AB/BM), right central nucleus, right lateral nuclei (dorsal portion), and left lateral nuclei (ventral portion containing basolateral portions). Moreover, the TPAD group exhibited decreased FC between the left AB/BM and left middle occipital gyrus and right superior frontal gyrus (SFG), left basal nucleus and right SFG, and right lateral nuclei (intermediate portion) and right SFG. In combination, these amygdalar alterations exhibited a sensitivity of 65.4% and specificity of 96.9% in predicting affective dysfunction in patients with tinnitus. Although similar structural and functional amygdala remodeling were observed in the TPAD and TPWAD groups, the changes were more pronounced in the TPAD group. These changes mainly involved alterations in functionality and white matter microstructure in various amygdala subregions; in combination, these changes could serve as an imaging-based predictor of emotional disorders in patients with tinnitus.

Mendelian randomization analyses support causal relationships between tinnitus of different stages and severity and structural characteristics of specific brain regions.

This study aims to delineate the causal relationships between idiopathic tinnitus in different stages and severity and the morphological properties in specific brain regions. We utilized a two-sample bidirectional Mendelian randomization (MR) analysis to ascertain the causal effects of brain structural attributes on varying severities and stages of tinnitus. Our approach involved harnessing genetic variables derived from extensive genome-wide association studies as instrumental variables, centered mainly on pertinent single-nucleotide polymorphisms associated with tinnitus. Subsequently, we integrated this data with brain structural imaging inputs to facilitate the MR analysis. We also applied reverse MR analysis to pinpoint the critical brain regions implicated in the onset of tinnitus. Our analysis revealed a demonstrable causal relationship between tinnitus and brain structural alterations, including changes primarily within the auditory cortex and hub regions of the limbic system, as well as portions of the frontal-temporal-occipital circuit. We found that individuals exhibiting cortical thickness alterations in the bilateral peri-calcarine and right superior occipital gyrus might have previously experienced tinnitus. Changes in the cortical areas of the right rectus, left inferior frontal gyrus, and right pars-orbitalis appeared unrelated to tinnitus. Furthermore, moderate tinnitus patients showed more pronounced structural alterations. This study substantiates that tinnitus could instigate substantial structural alterations mainly within the auditory-limbic-frontal-visual system, while the reciprocal causality was not supported. Moreover, the data underscores that moderate, rather than severe, tinnitus precipitates the most significant structural changes. Morphological alterations in several specific brain areas either indicate a history of tinnitus or bear no relation to it.

Comparative analysis of vestibular endolymphatic hydrops grading methods and hearing loss in Ménière's disease: a retrospective MRI study using 3D-real inversion recovery sequence.

PURPOSE: To compare the correlation between different grading methods of vestibular endolymphatic hydrops (EH) and the severity of hearing loss in Ménière's disease (MD), and evaluate the diagnostic value of these methods in diagnosing MD. METHODS: This retrospective study included 30 patients diagnosed with MD from June 2021 to August 2023. All patients underwent inner ear MR gadolinium-enhanced imaging using three-dimensional (3D)-real inversion recovery sequences and pure-tone audiometry. The EH levels were independently evaluated according to the classification methods outlined by Nakashima et al. (Acta Otolaryngol Suppl 5-8, 2009. https://doi.org/10.1080/00016480902729827 ) (M1), Fang et al. (J Laryngol Otol 126:454-459, 2012. https://doi.org/10.1017/S0022215112000060 ) (M2), Barath et al. (Am J Neuroradiol 35:1387-1392, 2014. https://doi.org/10.3174/ajnr.A3856 ), (M3), Liu et al. (Front Surg 9:874971, 2022. https://doi.org/10.3389/fsurg.2022.874971 ), (M4), and Bernaerts et al. (Neuroradiology 61:421-429, 2019. https://doi.org/10.1007/s00234-019-02155-7 ) (M5), with a subsequent comparison of interobserver agreement. After achieving a consensus, an analysis was performed to explore the correlations between vestibular EH grading using different methods, the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. The diagnostic capabilities of these methods for MD were then compared. RESULTS: The interobserver consistency of M2-M5 was superior to that of M1. The EH grading based on M4 showed a significant correlation with the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. M1, M2, M3, and M5 correlated with some parameters. A receiver operating characteristic curve analysis indicated that M5 significantly outperformed M1, M2, M3, and M4 in terms of diagnostic efficiency for MD. CONCLUSION: M4 showed the strongest correlation with the degree of hearing loss in patients with MD, whereas M5 showed the highest diagnostic performance.

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.

PURPOSE: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm. METHODS: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI. RESULTS: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727). CONCLUSION: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To date, there is no research reporting the effectiveness and stability of ML in identifying PCIS onset time. We aimed to build diffusion-weighted imaging-based ML models to identify the onset time of PCIS patients. METHODS: Consecutive PCIS patients within 24 h of definite symptom onset were included (112 in the training set and 49 in the independent test set). Images were processed as follows: volume of interest segmentation, image feature extraction, and feature selection. Five ML models, naïve Bayes, logistic regression, tree ensemble, k-nearest neighbor, and random forest, were built based on the training set to estimate the stroke onset time (binary classification: ≤ 4.5 h or > 4.5 h). Relative standard deviations (RSD), receiver operating characteristic (ROC) curves, and the calibration plot was performed to evaluate the stability and performance of the five models. RESULTS: The random forest model had the best performance in the test set, with the highest area under the curve (AUC, 0.840; 95% CI: 0.706, 0.974). This model also achieved the highest accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (83.7%, 64.3%, 91.4%, 75.0%, and 86.5%, respectively). Furthermore, the model had high stability (RSD = 0.0094). CONCLUSION: The PCIS case-based ML model was effective for estimating the symptom onset time and achieved considerably high specificity and stability.

Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization.

To explore the causal relationship between age and brain health (cortical atrophy, white matter integrity, white matter hyperintensities and cerebral microbleeds in various brain regions) related multiparameter imaging features using two-sample Mendelian randomization. Age was determined as chronological age of the subject. Cortical volume, white matter micro-integrity, white matter hyperintensity volume and cerebral microbleeds of each brain region were included as phenotypes for brain health. Age and imaging of brain health related genetic data were analysed to determine the causal relationship using inverse-variance weighted model, validated by heterogeneity and horizontal pleiotropy variables. Age is causally related to increased volumes of white matter hyperintensities (ß = 0.151). For white matter micro-integrity, fibres of the inferior cerebellar peduncle (axial diffusivity ß = -0.128, orientation dispersion index ß = 0.173), cerebral peduncle (axial diffusivity ß = -0.136), superior fronto-occipital fasciculus (isotropic volume fraction ß = 0.163) and fibres within the limbic system were causally deteriorated. We also detected decreased cortical thickness of multiple frontal and temporal regions (P < 0.05). Microbleeds were not related with aging (P > 0.05). Aging is a threat of brain health, leading to cortical atrophy mainly in the frontal lobes, as well as the white matter degeneration especially abnormal hyperintensity and deteriorated white matter integrity around the hippocampus.

Optimizing LI-RADS: ancillary features screened from LR-3/4 categories can improve the diagnosis of HCC on MRI.

OBJECTIVE: To determine the high-efficiency ancillary features (AFs) screened from LR-3/4 lesions and the HCC/non-HCC group and the diagnostic performance of LR3/4 observations. MATERIALS AND METHODS: We retrospectively analyzed a total of 460 patients (with 473 nodules) classified into LR-3-LR-5 categories, including 311 cases of hepatocellular carcinoma (HCC), 6 cases of non-HCC malignant tumors, and 156 cases of benign lesions. Two faculty abdominal radiologists with experience in hepatic imaging reviewed and recorded the major features (MFs) and AFs of the Liver Imaging Reporting and Data System (LI-RADS). The frequency of the features and diagnostic performance were calculated with a logistic regression model. After applying the above AFs to LR-3/LR-4 observations, the sensitivity and specificity for HCC were compared. RESULTS: The average age of all patients was 54.24 ± 11.32 years, and the biochemical indicators ALT (P = 0.044), TBIL (P = 0.000), PLT (P = 0.004), AFP (P = 0.000) and Child‒Pugh class were significantly higher in the HCC group. MFs, mild-moderate T2 hyperintensity, restricted diffusion and AFs favoring HCC in addition to nodule-in-nodule appearance were common in the HCC group and LR-5 category. AFs screened from the HCC/non-HCC group (AF-HCC) were mild-moderate T2 hyperintensity, restricted diffusion, TP hypointensity, marked T2 hyperintensity and HBP isointensity (P = 0.005, < 0.001, = 0. 032, p < 0.001, = 0.013), and the AFs screened from LR-3/4 lesions (AF-LR) were restricted diffusion, mosaic architecture, fat in mass, marked T2 hyperintensity and HBP isointensity (P < 0.001, = 0.020, = 0.036, < 0.001, = 0.016), which were not exactly the same. After applying AF-HCC and AF-LR to LR-3 and LR-4 observations in HCC group and Non-HCC group, After the above grades changed, the diagnostic sensitivity for HCC were 84.96% using AF-HCC and 85.71% using AF-LR, the specificity were 89.26% using AF-HCC and 90.60% using AF-LR, which made a significant difference (P = 0.000). And the kappa value for the two methods of AF-HCC and AF-LR were 0.695, reaching a substantial agreement. CONCLUSION: When adjusting for LR-3/LR-4 lesions, the screened AFs with high diagnostic ability can be used to optimize LI-RADS v2018; among them, AF-LR is recommended for better diagnostic capabilities.

Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.

OBJECTIVES: The aim of the study was to externally validate two AI models for the classification of prostate mpMRI sequences and segmentation of the prostate gland on T2WI. MATERIALS AND METHODS: MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and sequences. RESULTS: 20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001). CONCLUSION: The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI. CLINICAL RELEVANCE STATEMENT: These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies.

Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD.

PURPOSE: Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis. METHODS: A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLD was assessed via receiver operating characteristic (ROC) curve analysis. RESULTS: Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = - 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = - 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = - 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10-3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively. CONCLUSION: According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

Quantitative Comparison of Liver Volume, Proton Density Fat Fraction, and Time Burden between Automatic Whole Liver Segmentation and Manual Sampling MRI Strategies for Diagnosing Metabolic Dysfunction-associated Steatotic Liver Disease in Obese Patients.

BACKGROUND: The performance of automatic liver segmentation and manual sampling MRI strategies needs be compared to determine interchangeability. OBJECTIVE: To compare automatic liver segmentation and manual sampling strategies (manual whole liver segmentation and standardized manual region of interest) for performance in quantifying liver volume and MRI-proton density fat fraction (MRI-PDFF), identifying steatosis grade, and time burden. METHODS: Fifty patients with obesity who underwent liver biopsy and MRI between December 2017 and November 2018 were included. Sampling strategies included automatic and manual whole liver segmentation and 4 and 9 large regions of interest. Intraclass correlation coefficient (ICC), Bland-Altman, linear regression, receiver operating characteristic curve, and Pearson correlation analyses were performed. RESULTS: Automatic whole liver segmentation liver volume and manual whole liver segmentation liver volume showed excellent agreement (ICC=0.97), high correlation (R2=0.96), and low bias (3.7%, 95% limits of agreement, -4.8%, 12.2%) in liver volume. There was the best agreement (ICC=0.99), highest correlation (R2=1.00), and minimum bias (0.84%, 95% limits of agreement, -0.20%, 1.89%) between automated whole liver segmentation MRI-PDFF and manual whole liver segmentation MRI-PDFF. There was no difference of each paired comparison of receiver operating characteristic curves for detecting steatosis (P=0.07-1.00). The minimum time burden for automatic whole liver segmentation was 0.32 s (0.32-0.33 s). CONCLUSION: Automatic measurement has similar effects to manual measurement in quantifying liver volume, MRI-PDFF, and detecting steatosis. Time burden of automatic whole liver segmentation is minimal among all sampling strategies. Manual measurement can be replaced by automatic measurement to improve quantitative efficiency.

The staging of nonalcoholic fatty liver disease fibrosis: A comparative study of MR elastography and the quantitative DCE-MRI exchange model.

Objectives: To evaluate the efficacy and image processing time of the dynamic contrast-enhanced MRI (DCE-MRI) exchange model in liver fibrosis staging and compare it to the efficacy of magnetic resonance elastography (MRE). Methods: The subjects were 45 patients with nonalcoholic fatty liver disease (NAFLD) who underwent MRE and DCE-MRI in our hospital. Liver biopsy results were available for all patients. Spearman rank correlation coefficients were used to compare the correlations among MRE, DCE-MRI and liver fibrosis parameters. Quantitative DCE-MRI parameters, MRE-derived liver stiffness measurement (LSM), and the results of a combined DCE-MRI + MRE logistic regression model were compared in terms of the area under the receiver operating characteristic curve (AUC). We also compared the scanning and postprocessing times of the MRE and DCE-MRI techniques. Results: The correlation coefficients between the following parameters of interest and liver fibrosis were as follows: capillary permeability-surface area product (PS; DCE-MRI parameter), -0.761; portal blood flow (Fp; DCE-MRI parameter), -0.754; MRE-LSM, 0.835. Some DCE-MRI parameters (PS, Fp) had slightly greater AUC values than MRE-LSM for diagnosing the presence or absence of liver fibrosis, and the combined model had the highest AUC value for all stages except F4, but there was no significant difference in the diagnostic efficacy of the DCE-MRI, MRE, and combined models for any stage of fibrosis. The average scanning times for MRE and DCE-MRI were 17 s and 330 s, respectively, and the average postprocessing times were 45.5 s and 342.7 s, respectively. Conclusions: In the absence of MRE equipment, DCE-MRI represents an alternative technique. However, MRE is a quicker and simpler method for assessing fibrosis than DCE-MRI in the clinic.

Association Between Vestibular Aqueduct Morphology and Meniere's Disease.

OBJECTIVE: To investigate the relationship between vestibular aqueduct (VA) morphology and Meniere's disease (MD) using ultrahigh-resolution computed tomography (U-HRCT). METHODS: Retrospective data were collected from 34 patients (40 ears) diagnosed with MD in our hospital who underwent temporal bone U-HRCT with isotropic 0.05-mm resolution, magnetic resonance with gadolinium-enhanced, and pure-tone audiometry; 34 age- and sex-matched controls (68 ears) who underwent U-HRCT were also included. VA patency was qualitatively classified as locally not shown (grade 1), locally faintly shown (grade 2), or clearly shown throughout (grade 3). The width of the outer orifice and VA length and angle were quantitatively measured. Differences in VA morphology between the MD and control groups were analyzed. The correlations between VA morphology and the degrees of hearing loss and endolymphatic hydrops (EH) were also analyzed. RESULTS: VA was classified as grades 1-3 in 11, 17, and 12 ears in the MD group and 5, 26, and 37 ears in the control group, respectively. The patency differed significantly between the groups (p < 0.01). The width of the outer orifice and length of VA were significantly smaller in the MD group than those in the control group (p < 0.05). Both VA patency and length were correlated with the degree of EH in the cochlea and the vestibule (p < 0.05). No difference was found between VA morphology and the degree of hearing loss (p > 0.05). CONCLUSION: The morphological characteristics of VA were found to be associated with the occurrence of MD and the degree of EH. LEVEL OF EVIDENCE: 4 Laryngoscope, 2024.

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

OBJECTIVES: Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. METHODS: In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. RESULTS: VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p < 0.05). The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the prediction model were 82.61%, 95.45%, 69.89%, and 0.855 (0.792-0.917), respectively. The positive predictive value and negative predictive value were 70.79% and 93.88%, respectively. No significant differences in the AUC of the prediction model were found in different subgroups (all p > 0.05). CONCLUSIONS: The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. CRITICAL RELEVANCE STATEMENT: The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. KEY POINTS: • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC.

Superiority of spleen stiffness on two-dimensional magnetic resonance elastography over liver stiffness and serum tests in assessing portal hypertension in chronic liver disease.

Background: The value of magnetic resonance elastography (MRE) in portal hypertension (PH) has yet to be determined in the context of chronic liver disease (CLD). This study examined the value of MRE for the prediction of hepatic venous pressure gradient (HVPG) and high-risk esophageal varices (EVs) in a CLD cohort with a generally high HVPG. Methods: Patients with CLD who underwent both HVPG measurement and two-dimensional MRE examination at Beijing Friendship Hospital between April 2018 and March 2022 were prospectively included. Two-dimensional MRE was performed within the liver and spleen. Endoscopy results and laboratory parameters were collected. Some selected published serum markers were calculated, including fibrosis 4, aspartate aminotransferase-to-platelet ratio index, and King's score. The efficacy of the parameters for assessing PH was analyzed by using the Pearson correlation coefficient, linear and logistic regression, and receiver operating characteristic curve analyses. Results: A total of 48 patients were included. The mean HVPG was 16.8±5.8 mmHg. Among these patients, 47 patients had PH (HVPG >5 mmHg), and 43 patients had clinically significant PH (HVPG ≥10 mmHg). Among the parameters associated with HVPG, the strongest correlation was found for spleen stiffness (SS) (R=0.638; P<0.001). In multiple regression analyses, SS was independently associated with an elevated HVPG and high-risk EVs. The areas under the receiver operating characteristic curve of SS for identifying patients with an HVPG ≥16 mmHg, HVPG ≥20 mmHg, and high-risk EVs were 0.790, 0.822, and 0.886, respectively, which were higher than those of liver stiffness (LS) and serum markers but slightly inferior to that of fibrosis 4 (area under the receiver operating characteristic curve =0.844) in identifying an HVPG ≥16 mmHg. SS cutoff values of 9.5, 10.05, and 9.9 kPa were selected to rule out the presence of an HVPG ≥16 mmHg, HVPG ≥20 mmHg, and high-risk EVs (sensitivity: 100%, 100%, and 100%, respectively; specificity: 45.5%, 50%, and 60%, respectively). Conclusions: In patients with generally high HVPG, SS measured by two-dimensional MRE may be a better predictor of HVPG values and high-risk EVs than LS and serum markers.

Behind BMI: The Potential Indicative Role of Abdominal Ectopic Fat on Glucose Metabolism.

INTRODUCTION: The purpose of this study was to compare the difference in abdominal fat distribution between different metabolic groups and find the ectopic fat with the most risk significance. METHODS: A total of 98 subjects were enrolled; there were 53 cases in the normal glucose metabolism group and 45 cases in the abnormal glucose metabolism group. Chemical shift-encoded magnetic resonance imaging was applied for quantification of pancreatic fat fraction (PFF) and hepatic fat fraction (HFF), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT). The correlation and the difference of fat distribution between different metabolism groups were analyzed. The receiver operating characteristic (ROC) curve was used to analyze the suggestive effect of different body fat fraction. RESULTS: Correlation analysis showed that body mass index (BMI) had the strongest correlation with fasting insulin (r = 0.473, p < 0.001), HOMA-IR (r = 0.363, p < 0.001), and C-reactive protein (r = 0.245, p < 0.05). Pancreatic fat has a good correlation with fasting blood glucose (r = 0.247, p < 0.05) and HbA1c (r = 0.363, p < 0.001). With the increase of BMI, PFF, VAT, and SAT showed a clear upward trend, but liver fat was distributed relatively more randomly. The pancreatic fat content in the abnormal glucose metabolism group is significantly higher than that in the normal group, and pancreatic fat is also a reliable indicator of abnormal glucose metabolism, especially in the normal and overweight groups (the area under the curve was 0.859 and 0.864, respectively). CONCLUSION: MR-based fat quantification techniques can provide additional information on fat distribution. There are differences in fat distribution among people with different metabolic status. People with more severe pancreatic fat deposition have a higher risk of glucose metabolism disorders.

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.

AIM: To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists. MATERIALS AND METHODS: A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared. RESULTS: After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062). CONCLUSION: Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Association between white matter hyperintensities and altered cerebral blood flow in maintenance hemodialysis patients: a longitudinal study.

OBJECTIVES: To explore changes in cerebral blood flow (CBF) and white matter in hemodialysis patients. METHODS: Thirty-three hemodialysis patients who underwent two brain MRI at an interval of three years and 33 age- and sex-matched healthy controls (HC) underwent structural and arterial spin-labeling MRI examinations. Intergroup differences in CBF in the gray matter, white matter, and whole matter, and regional white matter hyperintensities (WMH) were analyzed. Based on the changes in CBF between the baseline and follow-up groups, the hemodialysis patients were divided into two subgroups: an increased CBF group and a decreased CBF group. Differences in CBF and WMH between the subgroups and HC were analyzed. RESULTS: Patients undergoing hemodialysis exhibited increased cerebral watershed (CW) WMH, deep WMH, and periventricular WMH (P < 0.01). The CBF of patients with decreased CBF was higher than that of HC at baseline (,P < 0.01) and lower than that of HC at follow-up (P < 0.01). Compared with the increased CBF group, obvious development of deep WMH was found in the decreased CBF group for the gray matter, white matter, and whole matter (P < 0.01). CONCLUSIONS: WMH in hemodialysis patients were distributed in the deep white matter, periventricular white matter and CW, and progressed with the extension of hemodialysis duration. CBF in hemodialysis patients could manifest as both increased and decreased, and WMH in patients with decreased CBF developed severely with prolongation of hemodialysis duration. ADVANCES IN KNOWLEDGE: These findings provide a basis for exploring neuropathological changes of hemodialysis patients.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

PURPOSE: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.