Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process.

Background: Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process. Methods: A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa. Results: The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05). Conclusion: FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions.

Association Between Perivascular Adipose Tissue Density and Atherosclerosis in the Descending Thoracic Aorta.

Radiodensity measured by computed tomography (CT) in Hounsfield Units (HU) is emerging as a clinical tool for detecting perivascular adipose tissue (PVAT) inflammation. In the present study, we hypothesized that PVAT radiodensity might predict the risk of descending thoracic aorta atherosclerosis. A total of 73 subjects who underwent CT angiography to investigate aortic disease were retrospectively analyzed. PVAT radiodensity, aortic complex plaque (ACP), mean plaque-burden score (MPBS), and plaque density were measured, and the association between them was analyzed. Perivascular adipose tissue radiodensity (HU) in patients with different aortic plaques grades (grade 1, 2, 3, and 4) were -93.71 ± 2.50, -93.63 ± 3.93, -90.24 ± 4.49, and -89.90 ± 5.18, respectively, and the difference was significant (P = .010). In the regression analysis, PVAT radiodensity was an independent predictor of ACP, with an OR of 1.263. In the linear analysis, PVAT radiodensity was an independent predictor of MPBS, with a ß-coefficient of .073. In the univariate analysis, only the PVAT radiodensity was significantly associated with plaque density, with a ß-coefficient of -1.666. In conclusion, PVAT density was independently related to descending thoracic aorta atherosclerosis.

Magnetic Resonance Imaging Assessment of Abdominal Ectopic Fat Deposition in Correlation With Cardiometabolic Risk Factors.

Purpose: Ectopic fat accumulation and abdominal fat distribution may have different cardiometabolic risk profiles. This study aimed to assess the associations between various magnetic resonance imaging (MRI)-acquired fat depots and cardiometabolic risk factors. Methods: A total of 320 subjects with median age of 59 years, 148 men and 172 women, were enrolled in the study. Visceral adipose tissue (VAT) area and fat fraction (FF), subcutaneous adipose tissue (SAT) area and FF at the L1-L2 levels, preperitoneal adipose tissue (pPAT) area and FF, hepatic FF, pancreatic FF, and intramuscular FF were assessed by MRI FF maps. The associations of various MRI-acquired fat depots with blood pressure, glucose, and lipid were examined using sex-stratified linear regression. Logistic regression stratified by sex was used to analyze the association of various MRI-acquired fat depots with the risk of hypertension, T2DM, and dyslipidemia. Results: The intraclass correlation coefficient (ICC) values were >0.9, which suggested good interobserver and intraobserver agreement. VAT area, V/S, hepatic fat, pancreatic fat, and pPAT rather than SAT area were significantly associated with multiple cardiometabolic risk factors (all p < 0.05). However, the patterns of these correlations varied by sex and specific risk factors. Also, VAT and SAT FF were only significantly associated with multiple cardiometabolic risk factors in women (all p < 0.05). Conclusions: VAT, hepatic fat, pancreatic fat, and pPAT were associated with cardiovascular metabolic risk factors independent of BMI. The patterns of these correlations were related to gender. These findings further the understanding of the association between ectopic fat deposition and cardiometabolic risk factors and help to better understand the obesity heterogeneity.

Clinical and imaging features of desmoid tumors of the extremities.

BACKGROUND: Desmoid fibroma is a rare soft tissue tumor originating from the aponeurosis, fascia, and muscle, and it is also known as aponeurotic fibroma, invasive fibroma, or ligamentous fibroma. AIM: To investigate the clinical and imaging features of desmoid tumors of the extremities. METHODS: Thirteen patients with desmoid fibroma of the extremities admitted to our hospital from October 2016 to March 2021 were included. All patients underwent computed tomography (CT), magnetic resonance imaging (MRI), and pathological examination of the lesion. Data on the diameter and distribution of the lesion, the relationship between the lesion morphology and surrounding structures, MRI and CT findings, and pathological features were statistically analyzed. RESULTS: The lesion diameter ranged from 1.7 to 8.9 cm, with an average of 5.35 ± 2.39 cm. All lesions were located in the deep muscular space, with the left and right forearm each accounting for 23.08% of cases. Among the 13 patients with desmoid fibroma of the extremities, the lesions were "patchy" in 1 case, irregular in 10, and quasi-round in 2. The boundary between the lesion and surrounding soft tissue was blurred in 10 cases, and the focus infiltrated along the tissue space and invaded the adjacent structures. Furthermore, the edge of the lesion showed "beard-like" infiltration in 2 cases; bone resorption and damage were found in 8, and bending of the bone was present in 2; the boundary of the focus was clear in 1. According to the MRI examination, the lesions were larger than 5 cm (61.54%), round or fusiform in shape (84.62%), had an unclear boundary (76.92%), showed uniform signal (69.23%), inhomogeneous enhancement (84.62%), and "root" or "claw" infiltration (69.23%). Neurovascular tract invasion was present in 30.77% of cases. CT examination showed that the desmoid tumors had slightly a lower density (69.23%), higher enhancement (61.54%), and unclear boundary (84.62%); a CT value < 50 Hu was present in 53.85% of lesions, and the enhancement was uneven in 53.85% of cases. Microscopically, fibroblasts and myofibroblasts were arranged in strands and bundles, without obvious atypia but with occasional karyotyping; cells were surrounded by collagen tissue. There were disparities in the proportion of collagen tissue in different regions, with abundant collagen tissue and few tumor cells in some areas, similar to the structure of aponeuroses or ligaments, and tumor cells invading the surrounding tissues. CONCLUSION: Desmoid tumors of the extremities have certain imaging features on CT and MRI. The two imaging techniques can be combined to improve the diagnostic accuracy, achieve a comprehensive diagnosis of the disease in the clinical practice, and reduce the risk of missed diagnosis or misdiagnosis. In addition, their use can ensure timely diagnosis and treatment.

Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method.

BACKGROUND: Accurate and early assessment of the hepatic fat content is crucial for patients with nonalcoholic fatty liver disease (NAFLD). For years, magnetic resonance imaging (MRI) has been considered the optimal noninvasive method for the assessment of fat accumulation. To avoid time-consuming manual placement of multiple regions of interest (ROI), the use of whole-liver segmentation has been proposed to measure liver fat, mainly for heterogeneous fat deposition. However, it remains uncertain whether the hepatic mean fat fraction (FF) obtained by whole-liver segmentation with the inclusion of intrahepatic vasculature is consistent with the traditional ROI sampling method. In this study, we assessed the accuracy of hepatic mean FF obtained by whole-liver segmentation in patients of NAFLD with different severities using the ROI sampling method as a reference standard. METHODS: Hepatic FFs were measured by whole-liver segmentation and the ROI sampling method (reference standard) using MRI scanning with the iterative decomposition of water and fat with echo an asymmetry at least-square estimation-iron quantification (IDEAL-IQ) sequence. SPSS version 25.0 software was used to analyze the correlation and consistency of data between the two methods. RESULTS: There was a strong correlation in hepatic FF between whole-liver segmentation and the ROI sampling method in healthy, mild, and moderate steatosis patients (r = 0.943, 0.990, and 0.961, respectively). Bland-Altman analysis showed a small bias of +0.50±0.27 and +0.05±0.30, which indicated a small overestimation when using whole-liver segmentation in healthy subjects and mild NAFLD patients. The 95% limits of agreement ranged from +1.02 to -0.03, and from +0.65 to -0.55, respectively. However, a small bias of -0.96±0.77 was also evident, which indicated a small underestimation when using whole-liver segmentation in moderate NAFLD patients. The 95% limits of agreement ranged from +0.56 to -2.48. CONCLUSIONS: Due to inclusion of the intrahepatic vasculature, whole-liver segmentation has some effects on hepatic FF assessment in patients with different NAFLD severities; yet, it does not significantly affect the assessment of whole-liver FF in MRI FF maps.

Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis.

PURPOSE: To assess the vascular heterogeneity and aggressiveness of pituitary macroadenomas (PM) using texture analysis based on Dynamic Contrast-Enhanced MRI (DCE-MRI). METHOD: Fifty patients with pathologically confirmed PM, including 32 patients with aggressive PM (aggressive group) and 18 patients with non-aggressive PM (non-aggressive group), were included in this study. The preoperative DCE-MRI and clinical data were collected from all patients. The features based on Ktrans, Ve, and Kep were generated using Omni-Kinetics software. Independent-samples t-test and Mann-Whitney U test were used for comparison between two groups. Logistic regression analysis was used to determine the optimal model for distinguishing aggressive and non-aggressive PM. RESULTS: Six features related to tumor morphology, 24 features in Ktrans, 20 features in Ve, and 3 features in Kep were significantly different between the aggressive and non-aggressive groups. Volume count, gray-level non-uniformity in Ktrans, voxel value sum in Ve and run-length non-uniformity in Kep (AUC = 0.816, 0.903, 0.785, 0.813) were considered the best feature for tumor diagnosis. After modeling, the diagnosis efficiency of mean model and total model was desirable (AUC = 0.859 and 0.957), and the diagnostic efficiency of morphological, Ktrans, Ve and Kep features model was improved (AUC = 0.845, 0.951, 0.847, 0.804). CONCLUSIONS: Texture analysis based on DCE-MRI elucidates the vascular heterogeneity and aggressiveness of pituitary adenoma. The total model could be used as a new noninvasive method for predicting the aggressiveness of pituitary macroadenoma.

Relationship between the sectional area of the rectus capitis posterior minor and the to be named ligament from 3D MR imaging.

BACKGROUND: To evaluate the maximal sectional area (SA) of the rectus capitis posterior minor (RCPmi) muscle and its potential correlation with to be named ligament (TBNL) in the suboccipital area using 3D MR imaging. METHODS: A total of 365 subjects underwent sagittal 3D T2WI MR imaging of the RCPmi and TBNL. Among them, 45 subjects were excluded due to a particular clinical history or poor image quality. Finally, 320 subjects met the inclusion criteria, including 138 men and 182 women. The 624 RCPmi muscles were classified into positive and negative groups according to their attachment to the TBNL. Two experienced radiologists manually measured the maximum SA of the RCPmi muscle on the parasagittal image with a 30° deviation from the median sagittal plane. The correlations between the SA and the subject's age, height, BMI, gender, handedness, and age-related disc degeneration were tested by Spearman analysis. The SA differences between different groups were compared using independent samples t-test. RESULTS: A total of 123 RCPmi-TBNL attachments were identified in the positive group, while 501 RCPmi muscles were identified in the negative group. The SA of the 624 RCPmi muscles was 62.71 ± 28.72 mm2 and was poorly correlated with the subject's age, BMI, or handedness, with no correlation with age-related disc degeneration. A fair correlation was found between the SA and the body height in the whole group, and poor correlation in each male/female group. The SA of the RCPmi muscle in males was significantly bigger than that in women ([75.54 ± 29.17] vs. [52.74 ± 24.07] mm2). The SA of RCPmi muscle in the positive group was significantly smaller than that in the negative group ([55.95 ± 26.76] mm2 vs. [64.37 ± 28.97] mm2). CONCLUSIONS: Our results revealed a significantly smaller SA of the RCPmi in subjects with RCPmi-TBNL attachment. Besides, a larger SA of the RCPmi was correlated with the male gender. These findings suggest that the SA of the RCPmi ought to be interpreted with care for each patient since there could be considerable variations.

Large-scale cortical volume correlation networks reveal disrupted small world patterns in Parkinson's disease.

To date, the most frequently reported neuroimaging biomarkers in Parkinson's disease (PD) are direct brain imaging measurements focusing on local disrupted regions. However, the notion that PD is related to abnormal functional and structural connectivity has received support in the past few years. Here, we employed graph theory to analyze the structural co-variance networks derived from 50 PD patients and 48 normal controls (NC). Then, the small world properties of brain networks were assessed in the structural networks that were constructed based on cortical volume data. Our results showed that both the PD and NC groups had a small world architecture in brain structural networks. However, the PD patients had a higher characteristic path length and clustering coefficients compared with the NC group. With regard to the nodal centrality, 11 regions, including 3 association cortices, 5 paralimbic cortices, and 3 subcortical regions were identified as hubs in the PD group. In contrast, 10 regions, including 7 association cortical regions, 2 paralimbic cortical regions, and the primary motor cortex region, were identified as hubs. Moreover, the regional centrality was profoundly affected in PD patients, including decreased nodal centrality in the right inferior occipital gyrus and the middle temporal gyrus and increased nodal centrality in the right amygdala, the left caudate and the superior temporal gyrus. In addition, the structural cortical network of PD showed reduced topological stability for targeted attacks. Together, this study shows that the coordinated patterns of cortical volume network are widely altered in PD patients with a decrease in the efficiency of parallel information processing. These changes provide structural evidence to support the concept that the core pathophysiology of PD is associated with disruptive alterations in the coordination of large-scale brain networks that underlie high-level cognition.