The application of B1 inhomogeneity-corrected variable flip angle T1 mapping for assessing liver fibrosis.

PURPOSE: The aim of this study was to evaluate the diagnostic accuracy of the B1 inhomogeneity-corrected variable flip angle (VFA) method using native T1 values in the staging of liver fibrosis. METHODS: Eighty-three patients who presented for liver biopsy due to varying degrees of liver damage, underwent MR examinations and had T1-mapping images of the liver acquired using the B1 inhomogeneity-corrected VFA VIBE method. Among them, 65 patients underwent Fibroscan, and their results were used to evaluate the elasticity of liver tissue. Additionally, T1-mapping images were collected from 19 normal control patients. Independent sample t-tests were used to analyze the correlation between T1 mapping and Fibroscan. The diagnostic efficacy of T1 mapping in patients with different stages of liver fibrosis was evaluated using receiver operating characteristic (ROC) curves. RESULTS: The consistency between different observer groups was intraclass correlation coefficient (ICC) =0.802. T1 mapping demonstrated significant differences between mid-stage liver fibrosis (S = 2) and late-stage liver fibrosis (S = 3), as well as moderate inflammation (G = 2) and severe inflammation (G = 3), P < 0.05. The Area Under Curve(AUC) values of T1 mapping for early liver fibrosis (S ≥ 1), significant liver fibrosis (S ≥ 2), advanced liver fibrosis (S ≥ 3), and end-stage liver fibrosis (S = 4) were 0.760, 0.709, 0.790, and 0.768, respectively. T1 mapping combined with Fibroscan had an AUC value of 0.860. CONCLUSIONS: The B1 inhomogeneity-corrected VFA T1 mapping may be useful for the staging of liver fibrosis. It has a superior diagnostic efficiency for diagnosing advanced fibrosis (≥S3), while native T1 values combined with Fibroscan have potential value for the staging of liver fibrosis.

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer.

PURPOSE: To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS: This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS: Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION: IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas.

Background: An accurate assessment of isocitrate dehydrogenase (IDH) status in patients with glioma is crucial for treatment planning and is a key factor in predicting patient outcomes. In this study, we investigated the potential value of whole-tumor histogram metrics derived from synthetic magnetic resonance imaging (MRI) in distinguishing IDH mutation status between astrocytoma and glioblastoma. Methods: In this prospective study, 80 glioma patients were enrolled from September 2019 to June 2022. All patients underwent pre- and post-contrast synthetic MRI scan protocol. Immunohistochemistry (IHC) staining or gene sequencing were used to assess IDH mutation status in tumor tissue samples. Whole-tumor histogram metrics, including T1, T2, proton density (PD), etc., were extracted from the quantitative maps, while radiological features were assessed by synthetic contrast-weighted maps. Basic clinical features of the patients were also evaluated. Differences in clinical, radiological, and histogram metrics between IDH-mutant astrocytoma and IDH-wildtype glioblastoma were analyzed using univariate analyses. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the diagnostic performance of metrics and models. Results: The histopathologic analysis revealed that of the 80 cases, 41 were classified as IDH-mutant astrocytoma and 39 as IDH-wildtype glioblastoma. Compared to IDH-wildtype glioblastoma, IDH-mutant astrocytoma showed significantly lower T1 [10th percentile (10th), mean, and median] and post-contrast PD (10th, 90th percentile, mean, median, and maximum) values as well as higher post-contrast T1 (cT1) (10th, mean, median, and minimum) values (all P<0.05). The combined model (T1-10th + cT1-10th + age) was developed by integrating the independent influencing factors of IDH-mutant astrocytoma using the multivariate logistic regression. The diagnostic performance of this model [AUC =0.872 (0.778-0.936), sensitivity =75.61%, and specificity =89.74%] was superior to the clinicoradiological model, which was constructed using age and enhancement degree (AUC =0.822 (0.870-0.898), P=0.035). Conclusions: The combined model constructed using histogram metrics derived from synthetic MRI could be a valuable preoperative tool to distinguish IDH mutation status between astrocytoma and glioblastoma, and subsequently, could assist in the decision-making process of pretreatment.

Application of multi-echo Dixon and MRS in quantifying hepatic fat content and staging liver fibrosis.

This study associated the liver proton density fat fraction (PDFF), measured by multi-echo Dixon (ME-Dixon) and breath-hold single-voxel high-speed T2-corrected multi-echo 1H magnetic resonance spectroscopy (HISTO) at 1.5 T, with serum biomarkers and liver fibrosis stages. This prospective study enrolled 75 patients suspected of liver fibrosis and scheduled for liver biopsy and 23 healthy participants with normal liver function. The participant underwent ME-Dixon and HISTO scanning. The agreement of PDFF measured by ME-Dixon (PDFF-D) and HISTO (PDFF-H) were compared. Correlations between PDFF and serum fat biomarkers (total cholesterol, triglyceride, and high- and low-density lipoproteins) and the liver fibrosis stages were assessed. PDFF were compared among the liver fibrosis stages (F0-F4) based on clinical liver biopsies. The Bland-Altman plot showed agreement between PDFF-D and PDFF-H(LoA, - 4.44 to 6.75), which have high consistency (ICC 0.752, P < 0.001). The correlations with the blood serum markers were mild to moderate (PDFF-H: r = 0.261-0.410, P < 0.01; PDFF-D: r = 0.265-0.367, P < 0.01). PDFF-D, PDFF-H, and steatosis were distributed similarly among the liver fibrosis stages. PDFF-H showed a slight negative correlation with the liver fibrosis stages (r = - 0.220, P = 0.04). Both ME-Dixon and HISTO sequences measured liver fat content noninvasively. Liver fat content was not directly associated with liver fibrosis stages.

Imaging and Hemodynamic Characteristics of Vulnerable Carotid Plaques and Artificial Intelligence Applications in Plaque Classification and Segmentation.

Stroke is a massive public health problem. The rupture of vulnerable carotid atherosclerotic plaques is the most common cause of acute ischemic stroke (AIS) across the world. Currently, vessel wall high-resolution magnetic resonance imaging (VW-HRMRI) is the most appropriate and cost-effective imaging technique to characterize carotid plaque vulnerability and plays an important role in promoting early diagnosis and guiding aggressive clinical therapy to reduce the risk of plaque rupture and AIS. In recent years, great progress has been made in imaging research on vulnerable carotid plaques. This review summarizes developments in the imaging and hemodynamic characteristics of vulnerable carotid plaques on the basis of VW-HRMRI and four-dimensional (4D) flow MRI, and it discusses the relationship between these characteristics and ischemic stroke. In addition, the applications of artificial intelligence in plaque classification and segmentation are reviewed.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning.

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Alterations of Thalamic Nuclei Volumes and the Intrinsic Thalamic Structural Network in Patients with Multiple Sclerosis-Related Fatigue.

Fatigue is a debilitating and prevalent symptom of multiple sclerosis (MS). The thalamus is atrophied at an earlier stage of MS and although the role of the thalamus in the pathophysiology of MS-related fatigue has been reported, there have been few studies on intra-thalamic changes. We investigated the alterations of thalamic nuclei volumes and the intrinsic thalamic network in people with MS presenting fatigue (F-MS). The network metrics comprised the clustering coefficient (Cp), characteristic path length (Lp), small-world index (σ), local efficiency (Eloc), global efficiency (Eglob), and nodal metrics. Volumetric analysis revealed that the right anteroventral, right central lateral, right lateral geniculate, right pulvinar anterior, left pulvinar medial, and left pulvinar inferior nuclei were atrophied only in the F-MS group. Furthermore, the F-MS group had significantly increased Lp compared to people with MS not presenting fatigue (NF-MS) (2.9674 vs. 2.4411, PAUC = 0.038). The F-MS group had significantly decreased nodal efficiency and betweenness centrality of the right mediodorsal medial magnocellular nucleus than the NF-MS group (false discovery rate corrected p < 0.05). The F-MS patients exhibited more atrophied thalamic nuclei, poorer network global functional integration, and disrupted right mediodorsal medial magnocellular nuclei interconnectivity with other nuclei. These findings might aid the elucidation of the underlying pathogenesis of MS-related fatigue.

Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC. METHODS: RNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 HCC cells. The expressions of SCARB1 in hepatocellular carcinoma tissue in 46 patients were detected by immunohistochemistry, and the correlation between its expressions and disease free survival of patient was calculated. RESULTS: Through a series of analyses, seven prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis, found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Results of IHC staining showed that SCARB1 was highly expressed in cancer tissues compared to adjacent normal liver tissues and its expression was related to hepatocellular carcinoma differentiation status. The Kaplan-Meier survival showed a poor percent survival in the SCARB1 high group compared to that in the SCARB1 low group. CONCLUSION: This study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.