Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features.

BACKGROUND: Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers. PURPOSE: To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD. STUDY TYPE: Prospective. SUBJECTS: A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs. FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging. ASSESSMENT: Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients. STATISTICAL TESTS: The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves. RESULTS: The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691). DATA CONCLUSION: The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Decreased Regional Spontaneous Brain Activity and Cognitive Dysfunction in Patients with Coronary Heart Disease: a Resting-state Functional MRI Study.

OBJECTIVES: Chronic coronary heart disease (CHD) is correlated with an increased risk of cognitive impairment (CI), but the mechanisms underlying these changes remain unclear. The aim of the present study was to explore the potential changes in regional spontaneous brain activities and their association with CI, to explore the pathophysiological mechanisms underlying CI in patients with CHD. MATERIALS AND METHODS: A total of 71 CHD patients and 73 matched healthy controls (HCs) were included in this study. Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess the participants' cognitive functions. Regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation(fALFF) values were calculated to determine regional spontaneous brain activity. Coronary artery calcium (CAC) score provides a measure of the total coronary plaque burden. Mediation analyses were performed to test whether CHD's effects on cognitive decline are mediated by decreased regional spontaneous brain activity. RESULTS: Patients with CHD had significantly lower MMSE and MoCA scores than the HCs. Compared with the HCs, the patients with CHD demonstrated significantly decreased ReHo and fALFF values in the bilateral medial superior frontal gyrus (SFGmed), left superior temporal gyrus (TPOsup) and left middle temporal gyrus (TPOmid). Impaired cognitive performance was positively correlated with decreased activities in the SFGmed. Mediation analyses revealed that the decreased regional spontaneous brain activity in the SFGmed played a critical role in the relationship between the increase in CAC score and the MoCA and MMSE scores. CONCLUSION: The abnormalities of spontaneous brain activity in SFGmed may provide insights into the neurological pathophysiology underlying CHD associated with cognitive dysfunction.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network.

BACKGROUND: Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. PURPOSE: To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). STUDY TYPE: Prospective. POPULATION: A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. FIELD STRENGTH/SEQUENCE: A 3.0 T/resting-state functional MRI using the gradient echo sequence. ASSESSMENT: A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. STATISTICAL TESTS: Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. RESULTS: The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD. DATA CONCLUSION: Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network.

BACKGROUND: Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD. METHOD: Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively. RESULT: The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction). CONCLUSION: We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.

Altered dynamic amplitude of low-frequency fluctuations in patients with postpartum depression.

BACKGROUND: Postpartum depression (PPD) is a common mood disorder with increasing incidence year by year. However, the dynamic changes in local neural activity of patients with PPD remain unclear. In this study, we utilized the dynamic amplitude of low-frequency fluctuation (dALFF) method to investigate the abnormal temporal variability of local neural activity and its potential correlation with clinical severity in PPD. METHODS: Twenty-four patients with PPD and nineteen healthy primiparous mothers controls (HCs) matched for age, education level and body mass index were examined by resting-state functional magnetic resonance imaging (rs-fMRI). A sliding-window method was used to assess the dALFF, and a k-means clustering method was used to identify dALFF states. Two-sample t-test was used to compare the differences of dALFF variability and state metrics between PPD and HCs. Pearson correlation analysis was used to analyze the relationship between dALFF variability, states metrics and clinical severity. RESULTS: (1) Patients with PPD had lower variance of dALFF than HCs in the cognitive control network, cerebellar network and sensorimotor network. (2) Four dALFF states were identified, and patients with PPD spent more time on state 2 than the other three states. The number of transitions between the four dALFF states increased in the patients compared with that in HCs. (3) Multiple dALFF states were found to be correlated with the severity of depression. The variance of dALFF in the right middle frontal gyrus was negatively correlated with the Edinburgh postnatal depression scale score. CONCLUSION: This study provides new insights into the brain dysfunction of PPD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding the neurophysiological mechanisms of PPD.

Radiomics Analysis of Computed Tomography for Prediction of Thyroid Capsule Invasion in Papillary Thyroid Carcinoma: A Multi-Classifier and Two-Center Study.

Objective: To investigate the application of computed tomography (CT)-based radiomics model for prediction of thyroid capsule invasion (TCI) in papillary thyroid carcinoma (PTC). Methods: This retrospective study recruited 412 consecutive PTC patients from two independent institutions and randomly assigned to training (n=265), internal test (n=114) and external test (n=33) cohorts. Radiomics features were extracted from non-contrast (NC) and artery phase (AP) CT scans. We also calculated delta radiomics features, which are defined as the absolute differences between the extracted radiomics features. One-way analysis of variance and least absolute shrinkage and selection operator were used to select optimal radiomics features. Then, six supervised machine learning radiomics models (k-nearest neighbor, logistic regression, decision tree, linear support vector machine [L-SVM], Gaussian-SVM, and polynomial-SVM) were constructed. Univariate was used to select clinicoradiological risk factors. Combined models including optimal radiomics features and clinicoradiological risk factors were constructed by these six classifiers. The prediction performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: In the internal test cohort, the best combined model (L-SVM, AUC=0.820 [95% CI 0.758-0.888]) performed better than the best radiomics model (L-SVM, AUC = 0.733 [95% CI 0.654-0.812]) and the clinical model (AUC = 0.709 [95% CI 0.649-0.783]). Combined-L-SVM model combines 23 radiomics features and 1 clinicoradiological risk factor (CT-reported TCI). In the external test cohort, the AUC was 0.776 (0.625-0.904) in the combined-L-SVM model, showing that the model is stable. DCA demonstrated that the combined model was clinically useful. Conclusions: Our combined model based on machine learning incorporated with CT radiomics features and the clinicoradiological risk factor shows good predictive ability for TCI in PTC.

Extrathyroidal Extension Prediction of Papillary Thyroid Cancer With Computed Tomography Based Radiomics Nomogram: A Multicenter Study.

Objectives: To develop and validate a Computed Tomography (CT) based radiomics nomogram for preoperative predicting of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC) patients. Methods: A total of 153 patients were randomly assigned to training and internal test sets (7:3). 46 patients were recruited to serve as an external test set. A radiologist with 8 years of experience segmented the images. Radiomics features were extracted from each image and Delta-radiomics features were calculated. Features were selected by using one way analysis of variance and the least absolute shrinkage and selection operator in the training set. K-nearest neighbor, logistic regression, decision tree, linear-support vector machine (linear -SVM), gaussian-SVM, and polynomial-SVM were used to build 6 radiomics models. Next, a radiomics signature score (Rad-score) was constructed by using the linear combination of selected features weighted by their corresponding coefficients. Finally, a nomogram was constructed combining the clinical risk factors with Rad-scores. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were performed on the three sets to evaluate the nomogram's performance. Results: 4 radiomics features were selected. The six models showed the certain value of radiomics, with area under the curves (AUCs) from 0.642 to 0.701. The nomogram combining the Rad-score and clinical risk factors (radiologists' interpretation) showed good performance (internal test set: AUC 0.750; external test set: AUC 0.797). Calibration curve and DCA demonstrated good performance of the nomogram. Conclusion: Our radiomics nomogram incorporating the radiomics and radiologists' interpretation has utility in the identification of ETE in PTC patients.

Pregnancy leads to changes in the brain functional network: a connectome analysis.

Pregnancy leads to long-lasting changes in human brain structure; however, little is known regarding alterations in the topological organization of functional networks. In this study, we investigated the effect of pregnancy on human brain function networks. Resting-state fMRI data was collected from eighteen primiparous mothers and twenty-four nulliparous control women of similar age, education level and body mass index (BMI). The functional brain network and topological properties were calculated by using GRETNA toolbox. The demographic data differences between two groups were computed by the independent two sample t-test. We tested group differences in network metrics' area under curve (AUC) using non-parametric permutation test of 1,000 permutations and corrected for false discovery rate (FDR). Differences in regional networks between groups were evaluated using non-parametric permutation tests by network-based statistical analysis (NBS). Compared with the nulliparous control women, a hub node changed from left inferior temporal gyrus to right precentral gyrus in primiparous mothers, while primiparous mothers showed enhanced network global efficiency (p = 0.247), enhanced local efficiency (p = 0.410), larger clustering coefficient (p = 0.410), but shorter characteristic path length (p = 0.247), smaller normalized clustering coefficient (p = 0.111), and shorter normalized characteristic path length (p = 0.705). Although both groups of functional networks have small-world property (σ > 1), the σ values of primiparous mothers were decreased significantly. NBS evaluation showed the majority of altered connected sub-network in the primiparous mothers occurred in the bilateral frontal lobe gyrus (p < 0.05). Altered functional network metrics and an abnormal sub-network were found in primiparous mothers, suggested that pregnancy may lead to changes in the brain functional network.

Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis.

BACKGROUND: Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis. METHODS: High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork. RESULTS: The PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network. CONCLUSIONS: This study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder.

Abnormalities of cortical structures in patients with postpartum depression: A surface-based morphometry study.

BACKGROUND: Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. However, the cortical structural alterations in patients with PPD remain unclear. This study investigated the cortical structural alterations of PPD patients through multidimensional structural patterns and their potential correlations with clinical severity. METHODS: High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women matched for age, educational level, and body mass index. The severity of PPD was assessed by using the Hamilton Depression Scale (HAMD) and Edinburgh Postnatal Depression Scale (EPDS) scores. Cortical morphological parameters including cortical thickness, surface area, and mean curvature were calculated using the surface-based morphometric (SBM) method. General linear model (GLM) analyses were performed to evaluate the relationship of cortical morphological parameters with clinical scales. RESULTS: In the present study, PPD patients showed a thinner cortical thickness in the right inferior parietal lobule compared with the healthy controls. Increased surface area was observed in the left superior frontal gyrus, caudal middle frontal gyrus, middle temporal gyrus, insula, and right supramarginal cortex in PPD patients. Likewise, PPD patients exhibited a higher mean curvature in the left superior and right inferior parietal lobule. Furthermore, increased cortical surface area in the left insula had a positive correlation with EPDS scores, and higher mean curvature in the left superior parietal lobule was negatively correlated with EPDS scores. LIMITATIONS: First, SBM cannot reflect the changes of subcortical structures that are considered to play a role in the development of PPD. Second, the sample size of this study is small. These positive results should be interpreted with caution. Third, this cross-sectional study does not involve a comparison of structural MRI before and after pregnancy. CONCLUSIONS: The complex cortical structural alterations of patients with PPD mainly involved the prefrontal and parietal regions. The morphometric alterations in these specific regions may provide promising markers for assessing the severity of PPD.

Altered structural covariance of hippocampal subregions in patients with Alzheimer's disease.

BACKGROUND AND PURPOSE: Different atrophy of hippocampus subregions is a valuable indicator of patients with Alzheimer's disease (AD). To explore the relationship among the hippocampal subregions of patients with AD, altered gray matter structural covariance of hippocampal subregions in patients with AD was studied. MATERIALS AND METHODS: Participants were selected from the Open Access Series of Imaging Studies Database. Pearson correlations among the volume of the hippocampal subregions were generated as structural covariance network. Topological metrics for all selected sparsity ranges were calculated in the healthy controls (HCs) and patients with AD by using the GRETNA software package. Spearman correlation analysis was performed to statistically analyze the volume and Mini-mental State Examination (MMSE) scores of the hippocampal subregions of the patients with AD, with age and gender as interference covariates and corrected for false discovery rate (FDR) (p < 0.05). RESULTS: The structural covariance network properties of the hippocampal subregions of patients with AD changed. The clustering coefficient (Cp) and network efficiency (Ne) decreased, characteristic path length (Lp) increased, and the hub nodes changed. The volumes of left parasubiculum, right granule cell layer of dentate gyrus (GC-DG), right molecular layer of the hippocampus (molecular_layer_HP), right Cornu Ammonis (CA) regions CA1 of the hippocampus proper, right fimbria and right CA4 were significantly correlated with the MMSE scores. CONCLUSIONS: The structural covariance network of the hippocampal subregions of patients with AD was reorganized, and the transmission efficiency was weakened. This study explored the changes in these subregions from the network level, which may provide a new perspective and theoretical basis for the neurobiological mechanisms of patients with AD.

Evaluation of the anatomical variations of the coronary venous system in patients with coronary artery calcification using 256-slice computed tomography.

The factors that determine the anatomical variations of the coronary venous system (CVS) are poorly understood. The objective of this study was to evaluate the anatomical variations of the CVS in patients with coronary artery calcification. 196 patients underwent non-contrast CT and coronary CT angiography using 256-slice CT. All subjects were divided into four groups based on their coronary artery calcium score (CACS): 50 patients with CACS = 0 Agatston unit (AU), 52 patients with CACS = 1-100 AU, 44 patients with CACS = 101-400 AU, and 50 patients with CACS > 400 AU. The presence of the following cardiac veins was evaluated: the coronary sinus (CS), great cardiac vein (GCV), posterior interventricular vein (PIV), posterior vein of the left ventricle (PVLV), left marginal vein (LMV), anterior interventricular vein (AIV), and small cardiac vein (SCV). Vessel diameters were also measured. We found that the CS, GCV, PIV, and AIV were visualized in all patients, whereas the PVLV and LMV were identified in a certain proportion of patients: 98% and 96% in the CACS = 0 AU group, 100% and 78.8% in the CACS = 1-100 AU group, 93.2% and 77.3% in the CACS = 101-400 AU group, and 98% and 78% in the CACS > 400 AU group, respectively. The LMV was less often identified in the last three groups than in the first group (p < 0.05). The frequency of having either one PVLV or LMV was higher in the last three groups than in the first group (p < 0.05). No significant differences in vessel diameters were observed between the groups. It was concluded that patients with coronary artery calcification were less likely to have the LMV, which might hamper the left ventricular lead implantation in cardiac resynchronization therapy.

Abnormal information flow in postpartum depression: A resting-state functional magnetic resonance imaging study.

BACKGROUND: Postpartum depression (PPD) is a common mental disorder among women. However, the brain information flow alteration in patients with PPD remains unclear. This study investigated the brain information flow characteristics of patients with PPD and their value for clinical evaluation by using support vector regression (SVR). METHODS: Structural and resting-state functional magnetic resonance imaging data were acquired from 21 patients with PPD and 23 age-, educational level-, body mass index-, and menstruation-matched healthy controls. The preferred information flow direction between local brain regions and the preferred information flow direction index within local brain regions based on non-parametric multiplicative regression granger causality analysis were calculated to determine the global and local brain functional characteristics of the patients with PPD. Pearson's correlation analyses were performed to evaluate the relationship of the information flow characteristics with clinical scales. A predictive model for the mental state of the patients with PPD was established using SVR based on information flow characteristics. RESULTS: The information flow patterns in the amygdala, cingulum gyrus, insula, hippocampus, frontal lobe, parietal lobe, and occipital lobe changed significantly in the patients with PPD. The preferred information flow direction between the amygdala and the temporal and frontal lobes significantly correlated with clinical scales. Prediction analysis shows that the information flow patterns can be used to assess depression in patients with PPD. LIMITATION: This exploratory study has a small sample size with no longitudinal research. CONCLUSION: The change in information flow pattern in the amygdala may play an important role in the neuropathological mechanism of PPD and may provide promising markers for clinical evaluation.

Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.

BACKGROUND: To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. METHODS: A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. RESULTS: Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. CONCLUSIONS: The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

Aberrant Resting-State Brain Function in Adolescent Depression.

To explore the changes of brain function and conduct clinical differential diagnosis based on support vector machine (SVM) in adolescent patients with depression. A total of 24 adolescent patients with depression according to CCMD-3 and DSM-5 and 23 gender, education level, body mass index, and age matched healthy controls were assessed with 17-item Hamilton Depression Rating Scale (HAMD). HAMD scores were requested from ≥17 of patients. Three-dimensional T1 and resting-state functional magnetic resonance imaging data were acquired from all participants. The data were analyzed using SPM 12 and REST1.8. Two-sample t-test was conducted to compare regional homogeneity (ReHo) values among the groups of participants. Finally, based on SVM classification, clinical differential diagnosis of the patients was carried out. The receiver operator characteristic (ROC) curve were used to confirm the performance of the SVM model. An increase ReHo values were observed in the lingual gyrus, middle occipital gyrus, postcentral gyrus, and precentral gyrus, whereas a decrease in ReHo was found in vermis compared with the control group. The SVM model showed good performance in classification prediction of adolescent depression, with an area under curve (AUC) of 0.778 [95% confidence interval (CI), 0.661-0.797]. The changes in the spontaneous neural activity of these regions may play an important role in the neuropathological mechanism of adolescent depression and may provide promising markers for clinical evaluation.

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study.

OBJECTIVE: This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets. RESULTS: The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689-0.858), 0.767 (95% CI 0.583-0.857), and 0.79 (95% CI 0.63-0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as - 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%. CONCLUSIONS: The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer. KEY POINTS: • The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers.

Altered Spontaneous Neural Activity in Peripartum Depression: A Resting-State Functional Magnetic Resonance Imaging Study.

Abnormalities related to peripartum depression (PPD) have been detected in several brain regions through tasking-state functional magnetic resonance imaging (fMRI). In this study, we used the two markers of resting-state fMRI (rs-fMRI) to investigate changes in spontaneous neural activity of PPD and their correlation with depression severity. A total of 16 individuals with PPD were compared with 16 age- and education-matched healthy controls (HCs) by using rs-fMRI. Two-sample t-test was used to compare the fractional amplitudes of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) values between groups. Pearson correlation analysis was used to determine the correlation between the fALFF and ReHo of the abnormal brain region and the Hamilton Depression Scale (HAMD) and Edinburgh Postnatal Depression Scale scores. The spontaneous neural activity of the PPD group significantly increased mainly in the left middle frontal gyrus, left precuneus, left inferior parietal lobule, and left dorsolateral prefrontal cortex (DLPFC) and decreased mainly in the bilateral precentral gyrus and right inferior occipital gyrus compared with those of the HCs. The fALFF value of the left DLPFC was negatively correlated with the HAMD score in PPD. This rs-fMRI study suggests that changes in the spontaneous neural activity of these regions are related to emotional responses. PPD cases with low fALFF values in the left DLPFC have severe depression.

Adnexal torsion in the third trimester of pregnancy: a case report and diagnostic value of MR imaging.

BACKGROUND: The torsion of normal adnexa is rare during pregnancy, especially in the third trimester. Nonspecific symptoms and signs as well as the limitations of ultrasound (US) make the diagnosis difficult, resulting in the loss of adnexa and fetal compromise. The magnetic resonance imaging (MRI) features of the torsion of normal adnexa are not classically described during pregnancy and only reported in a few cases. We find some different MRI features of the torsion of normal adnexa in late pregnancy and its diagnosis and treatment values are discussed in our report. CASE PRESENTATION: A 27-year-old woman at 31 + 5 weeks' gestation presented to the emergency department with a three-day history of the left lower abdominal pain. US discovered a mass of 87 × 61 mm in the left abdomen, but did not show whether the mass originated from the left ovary or the uterus. MRI showed the left ovary was increased in size to 82 × 42 × 85 mm with peripheral follicles. On fat-suppressed T2-weighted images, the signal intensity of the lesion was significantly decreased compared with the right ovary. The adjacent fallopian tube was found to be thickened. The radiologists diagnosed ovary infarction secondary to adnexal torsion. With the provisional diagnosis of adnexal torsion, the patient was taken to surgery. The left adnexal torsion was found during surgery. There was extensive hemorrhage and necrosis, so a left salpingo-oophorectomy was performed. The histopathology confirmed an extensively hemorrhagic fallopian tube and ovary with partial necrosis. CONCLUSION: We believe MRI is helpful where US is indeterminate in diagnosis of the torsion of normal adnexa in advanced pregnancy. We found that aside from hyperintensity on fat-saturated T1-weighted images, the low signal intensity on T2-weighted images can also reflect adnexal hemorrhage in conjunction with the torsion of normal adnexa.

Assessment of the Relationship Between the Coronary Venous and Arterial Systems Using 256-Slice Computed Tomography.

OBJECTIVES: To investigate the coronary venous system (CVS) and its spatial relationship with coronary arteries by using 256-slice computed tomography (CT). METHODS: One hundred one patients underwent coronary CT angiography by using a 256-slice CT. In each patient, the CVS and its spatial relationship with coronary arteries were analyzed. We measured the diameters and angulations of the coronary sinus (CS), great cardiac vein, anterior interventricular vein (AIV), left marginal vein, posterior vein of the left ventricle (PVLV), and posterior interventricular vein (PIV), and the distances, respectively, from the CS ostium and from the crossing point to the ostium of corresponding tributaries. RESULTS: The following 5 pairs of veins and arteries had a higher frequency of intersecting compared with others: the CS/great cardiac vein and the left circumflex coronary artery (97.1%), the AIV and the diagonal or ramus branch (92.1%), the PIV and the posterior branch of left ventricle artery (88.1%), the left marginal vein and the circumflex or circumflex marginal (73.9%), and the PVLV and the circumflex or circumflex marginal (31.6%). The other 2 pairs had a higher frequency of running parallel to each other: the AIV and the left anterior descending artery (76.2%) and the PIV and the posterior descending artery (54.4%). Most tributaries were lateral to their corresponding arteries at the crossing point except for the AIV. For the PVLV and PIV, the distances from the crossing point to the ostium of corresponding veins when the veins were lateral to the arteries were smaller than those when the veins were medial to the arteries (P < 0.05). CONCLUSIONS: The CVS and its anatomical relationship with the coronary arterial system can be examined with details by using a 256-slice CT, which has important clinical implications.