Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. PURPOSE: To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. STUDY TYPE: Retrospective. POPULATION: A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. FIELD STRENGTH/SEQUENCE: 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). ASSESSMENT: Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. RESULTS: Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). CONCLUSION: MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma.

OBJECTIVES: To evaluate the potential diagnostic value of MR elastography (MRE)-based stiffness to noninvasively predict the microvascular invasion (MVI) grade in hepatocellular carcinoma (HCC). METHODS: One hundred eighty-five patients with histopathology-proven HCC who underwent MRI and MRE examinations before hepatectomy were retrospectively enrolled. According to the three-tiered MVI grading system, the MVI was divided into negative-MVI (n = 89) and positive-MVI (n = 96) groups, and the latter group was categorized into mild-MVI (n = 49) and severe-MVI (n = 47) subgroups. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to determine the predictors associated with MVI grade and analyze their performances, respectively. RESULTS: Among the 185 patients, tumor size ≥ 50 mm (p = 0.031), tumor stiffness (TS)/liver stiffness (LS) > 1.47 (p = 0.001), TS > 4.33 kPa (p < 0.001), and nonsmooth tumor margin (p = 0.006) were significant independent predictors for positive-MVI. Further analyzing the subgroups, tumor size ≥ 50 mm (p < 0.001), TS > 5.35 kPa (p = 0.001), and AFP level > 400 ng/mL (p = 0.044) were independently associated with severe-MVI. The models incorporating MRE and clinical-radiological features together performed better for evaluating positive-MVI (AUC: 0.846) and severe-MVI (AUC: 0.802) than the models using clinical-radiological predictors alone (AUC: positive-/severe-MVI, 0.737/0.743). Analysis of recurrence-free survival and overall survival showed the predicted positive-MVI/severe-MVI groups based on combined models had significantly poorer prognoses than predicted negative-MVI/mild-MVI groups, respectively (all p < 0.05). CONCLUSIONS: MRE-based stiffness was an independent predictor for both the positive-MVI and severe-MVI. The combination of MRE and clinical-radiological models might be a useful tool for evaluating HCC patients' prognoses underwent hepatectomy by preoperatively predicting the MVI grade. KEY POINTS: • The severe-microvascular invasion (MVI) grade had the highest tumor stiffness (TS), followed by mild-MVI and non-MVI, and there were significances among the three different MVI grades. • MR elastography (MRE)-based stiffness value was an independent predictor of positive-MVI and severe-MVI in hepatocellular carcinoma (HCC) preoperatively. • When combined with clinical-radiological models, MRE could significantly improve the predictive performance for MVI grade. Patients with predicted positive-MVI/severe-MVI based on the combined models had worse recurrence-free survival and overall survival than those with negative-MVI/mild-MVI, respectively.

Transdiagnostic time-varying dysconnectivity across major psychiatric disorders.

Dynamic functional connectivity (DFC) analysis can capture time-varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting-state functional magnetic resonance imaging and a sliding-window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k-means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4-group differences (SZ, BD, MDD, and HC groups; q < .05, false-discovery rate [FDR]-corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR-corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state-dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

Diffusion-weighted imaging or dynamic contrast-enhanced curve: a retrospective analysis of contrast-enhanced magnetic resonance imaging-based differential diagnoses of benign and malignant breast lesions.

OBJECTIVE: To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions. METHODS: A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p < 0.05 was considered as statistically different. External validation was used. RESULTS: In the training set, the sensitivities, specificities, and AUCs of the DWI/TIC model were 95.2%/95.8%, 70.8%/47.9%, and 0.932/0.891 for masses, and 94.2%/90.4%, 47.4%/47.4%, and 0.798 (95% CI, 0.686-0.884)/0.802 (95% CI, 0.691-0.887) for NMEs, respectively. The AUC of the DWI model was significantly higher than that of the TIC model (p < 0.05) for masses. In the validation set, the AUCs of the DWI/TIC model were 0.896/0.861 for masses (p < 0.05) and 0.936/0.836 for NMEs (p > 0.05). CONCLUSIONS: Combined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions. KEY POINTS: • Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.

Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging.

PURPOSE: To establish and validate a radiomics model to estimate the malignancy of mediastinal lymph nodes (LNs) based on contrast-enhanced CT imaging. METHOD: In total, 201 pathologically confirmed mediastinal LNs from 129 patients were enrolled and assigned to training and test sets. Radiomics features were extracted from the region of interest (ROI) delineated on venous-phase CT imaging of LN. Feature selection was performed with least absolute shrinkage and selection operator (LASSO) binary logistic regression. Multivariate logistic regression was performed with the backward stepwise elimination. A model was fitted to associate mediastinal LN malignancy with selected features. The performance of the model was assessed and compared to that of five other machine learning algorithms (support vector machine, naive Bayes, random forest, decision tree, K-nearest neighbor) using receiver operating characteristic (ROC) curves. Calibration curves and Hosmer-Lemeshow tests were used to assess the calibration degree. Decision curve analysis (DCA) was used to assess the clinical usefulness of the logistic regression model in both the training and test sets. Stratified analysis was performed for different scanners and slice thicknesses. RESULT: Among the six machine learning methods, the logistic regression model with the eight strongest features showed a significant association with mediastinal LN status and the satisfactory diagnostic performance for distinguishing malignant LNs from benign LNs. The accuracy, sensitivity, specificity and area under the ROC curve (AUC) were 0.850/0.803, 0.821/0.806, 0.893/0.800, and 0.922/0.850 in the training/test sets, respectively. The Hosmer-Lemeshow test showed that the P value was > 0.05, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA showed that the model would obtain more benefit when the threshold probability was between 30% and 90% in the test set. Stratified analysis showed that the performance was not affected by different scanners or slice thicknesses. There was no significant difference (DeLong test, P > 0.05) between any two subgroups, which showed the generalization of the radiomics score across different factors. CONCLUSION: The model we built could help assist the preoperative estimation of mediastinal LN malignancy based on contrast-enhanced CT imaging, with stability for different scanners and slice thicknesses.

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

Objective: To explore whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). Methods: Thirty-eight drug-naive patients with PI, and 44 healthy controls (HC) underwent resting-state functional MR imaging. Voxel-wise functional connectivity strength (FCS), large-scale functional connectivity (large-scale FC) and regional homogeneity (ReHo) were calculated for each participant. We used support vector machine (SVM) with the three types of metrics as features separately to classify patients from healthy controls. Then we evaluated its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS) in the patients' group. Results: The best classifier could reach up to an accuracy of 81.5%, with a sensitivity of 84.9%, specificity of 79.1%, and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right anterior insular cortex (BA48), left precuneus (BA7), and left middle frontal gyrus (BA8) showed high classification weights. In addition, the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) were the overlapping regions between MVPA and group comparison. Correlation analysis showed that FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS, respectively. Conclusion: The current study suggests abnormal FCS in right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) might serve as a potential neuromarkers for PI.

A failed top-down control from the prefrontal cortex to the amygdala in generalized anxiety disorder: Evidence from resting-state fMRI with Granger causality analysis.

In generalized anxiety disorder (GAD), abnormal top-down control from the prefrontal cortex (PFC) to the amygdala is a widely accepted hypothesis through which an "emotional dysregulation model" may be explained. However, whether and how the PFC directly exerts abnormal top-down control on the amygdala remains largely unknown. We aimed to investigate the amygdala-based effective connectivity by using Granger causality analysis (GCA). Thirty-five drug-naive patients with GAD and thirty-six healthy controls (HC) underwent resting-state functional MR imaging. We used seed-based Granger causality analysis to examine the effective connectivity between the bilateral amygdala and the whole brain. The amygdala-based effective connectivity was compared between the HC and GAD groups. The results showed that, in the HC group, the left middle frontal gyrus exerted an inhibitory influence on the right amygdala, while in the GAD group, this influence was disrupted (single voxel P < 0.001, Gaussian random field corrected with P < 0.01). Our findings support and advance the "insufficient top-down control" hypothesis by identifying a failed top-down control from the prefrontal cortex to the amygdala in GAD.

Aberrant Effective Connectivity of the Right Anterior Insula in Primary Insomnia.

OBJECTIVE: Daytime cognitive impairment is an essential symptom of primary insomnia (PI). However, the underlying neural substrate remains largely unknown. Many studies have shown that the right anterior insula (rAI) as a key node of salience network (SN) plays a critical role in switching between the executive control network (ECN) and the default mode network (DMN) for better performance of cognitively demanding tasks. Aberrant effective connectivity (directional functional connectivity) of rAI with ECN or DMN may be one reason for daytime cognitive impairment in PI patients. Up to now, no effective connectivity study has been conducted on patients with PI during resting state. Our aim is to investigate the effective connectivity between the rAI and the other voxels in the whole brain in PI. MATERIALS AND METHODS: Fifty drug-naive patients with PI and forty age- and sex-matched healthy controls were scanned using resting-state functional MRI. Seed-based Granger causality analysis was used to examine effective connectivity between the rAI, including ventral and dorsal part, and the whole brain. The effective connectivity was compared between the two groups and was correlated with clinical characteristics. RESULTS: Compared with controls, patients showed decreased effective connectivity from the rAI to the bilateral precuneus, the left postcentral gyrus (extending to bilateral precuneus) and the bilateral cerebellum posterior lobe, and decreased effective connectivity from the bilateral orbitofrontal cortex (OFC) to the rAI (single voxel P < 0.001, AlphaSim corrected with P < 0.01). In addition, effective connectivity from the ventral rAI to the left postcentral gyrus and from the left OFC to the ventral rAI were significantly negatively correlated with Insomnia Severity Index scores (r = -0.28/P = 0.046 and r = -0.29/P = 0.038, respectively). CONCLUSION: The present study is the first to reveal aberrant effective connectivity between the SN hub (rAI) and the posterior DMN hub (precuneus) as well as decision-making region (OFC) and sensori-motor region in PI. These findings suggest an aberrant salience processing system of the rAI in PI patients.

Abnormal whole-brain functional connectivity in patients with primary insomnia.

The investigation of the mechanism of insomnia could provide the basis for improved understanding and treatment of insomnia. The aim of this study is to investigate the abnormal functional connectivity throughout the entire brain of insomnia patients, and analyze the global distribution of these abnormalities. Whole brains of 50 patients with insomnia and 40 healthy controls were divided into 116 regions and abnormal connectivities were identified by comparing the Pearson's correlation coefficients of each pair using general linear model analyses with covariates of age, sex, and duration of education. In patients with insomnia, regions that relate to wakefulness, emotion, worry/rumination, saliency/attention, and sensory-motor showed increased positive connectivity with each other; however, regions that often restrain each other, such as regions in salience network with regions in default mode network, showed decreased positive connectivity. Correlation analysis indicated that some increased positive functional connectivity was associated with the Self-Rating Depression Scale, Insomnia Severity Index, and Pittsburgh Sleep Quality Index scores. According to our findings, increased and decreased positive connectivities suggest function strengthening and function disinhibition, respectively, which offers a parsimonious explanation for the hyperarousal hypothesis in the level of the whole-brain functional connectivity in patients with insomnia.

Abnormal spontaneous regional brain activity in primary insomnia: a resting-state functional magnetic resonance imaging study.

OBJECTIVE: Investigating functional specialization is crucial for a complete understanding of the neural mechanisms of primary insomnia (PI). Resting-state functional magnetic resonance imaging (fMRI) is a useful tool to explore the functional specialization of PI. However, only a few studies have focused on the functional specialization of PI using resting-state fMRI and results of these studies were far from consistent. Thus, the current study aimed to investigate functional specialization of PI using resting-state fMRI with amplitude of low frequency fluctuations (ALFFs) algorithm. METHODS: In this study, 55 PI patients and 44 healthy controls were included. ALFF values were compared between the two groups using two-sample t-test. The relationship of abnormal ALFF values with clinical characteristics and duration of insomnia was investigated using Pearson's correlation analysis. RESULTS: PI patients showed lower ALFF values in the left orbitofrontal cortex/inferior frontal gyrus, right middle frontal gyrus, left inferior parietal lobule, and bilateral cerebellum posterior lobes, while higher ALFF values in the right middle/inferior temporal that extended to the right occipital lobe. In addition, we found that the duration of PI negatively correlated with ALFF values in the left orbitofrontal cortex/inferior frontal gyrus, and the Pittsburgh Sleep Quality Index score negatively correlated with ALFF values in the left inferior parietal lobule. CONCLUSION: The present study added information to limited studies on functional specialization and provided evidence for hyperarousal hypothesis in PI.