Activation network improves spatiotemporal modelling of human brain communication processes.

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study.

BACKGROUND: Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. METHODS: A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. RESULTS: Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). CONCLUSIONS: The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

Feasibility of CT attenuation values in distinguishing acute ischemic stroke, old cerebral infarction and leukoaraiosis.

PURPOSE: This study aimed to investigate the feasibility of using computed tomography (CT) attenuation values to differentiate hypodense brain lesions, specifically acute ischemic stroke (AIS) from asymmetric leukoaraiosis (LA) and old cerebral infarction (OCI). MATERIALS AND METHODS: This retrospective study included patients with indeterminate hypodense lesions identified via brain CT scans conducted between June 2019 and June 2021. All lesions were confirmed through head MRI/diffusion-weighted imaging within 48 h after CT. CT attenuation values of hypodense lesions and symmetrical control regions were measured. Additionally, CT attenuation value difference (ΔHU) and ratio (RatioHU) were calculated. One-way analysis of variance (ANOVA) was used to compare age and CT parameters (CT attenuation values, ΔHU and RatioHU) across the groups. Finally, receiver operating characteristic (ROC) analysis was performed to determine the cutoff values for distinguishing hypodense lesions. RESULTS: A total of 167 lesions from 146 patients were examined. The CT attenuation values for AIS(n = 39), LA(n = 53), and OCI(n = 75) were 18.90 ± 6.40 HU, 17.53 ± 4.67 HU, and 11.90 ± 5.92 HU, respectively. The time interval between symptom onset and CT scans for AIS group was 32.21 ± 26.85 h. ANOVA revealed significant differences among the CT parameters of the hypodense lesion groups (all P < 0.001). The AUC of CT values, ΔHU, and RatioHU for distinguishing AIS from OCI were 0.802, 0.896 and 0.878, respectively (all P < 0.001). Meanwhile, the AUC for distinguishing OCI from LA was 0.789, 0.883, and 0.857, respectively (all P < 0.001). Nevertheless, none of the parameters could distinguish AIS from LA. CONCLUSION: CT attenuation parameters can be utilized to differentiate between AIS and OCI or OCI and LA in indeterminate hypodense lesions on CT images. However, distinguishing AIS from LA remains challenging.

Early impairment of right ventricular systolic function in patients with prediabetes and type 2 diabetes mellitus: An analysis of two-dimensional speckle tracking echocardiography.

BACKGROUND: Type 2 diabetes mellitus is a metabolic disease that affects multiple target organs. Current data on right ventricular damage in type 2 diabetes, especially in prediabetes, are limited. Due to the anatomical characteristics of the right ventricle, the assessment of the right ventricle by conventional echocardiography is difficult, whereas the ultrasound two-dimensional speckle tracking echocardiography can provide information on myocardial systolic function by tracking the motion information of myocardial speckles, which can sensitively reflect myocardial mechanical changes. AIMS: To assess the effect of prediabetes and diabetes with preserved left ventricular ejection fraction on right ventricular myocardial systolic function and to identify independent risk factors affecting right ventricular systolic function. METHODS: A total of 49 normoglycaemic (NG) healthy individuals, 43 prediabetics (PDM), and 52 type 2 diabetics (T2DM) were recruited. All study subjects underwent conventional echocardiography and two-dimensional speckle tracking echocardiography (2D-STE). RESULTS: The right ventricular global longitudinal strain (RVGLS) (20.80 ± 1.96% vs. 18.99 ± 3.20% vs. 16.85 ± 4.01%), left ventricular global longitudinal strain (LVGLS), and interventricular septal longitudinal strain (IVS-LS) (17.28 ± 2. 35% vs. 16.14 ± 3.22% vs. 15.53 ± 3.33%) gradually decreased from the controls, through patients with prediabetes, to those with diabetes (p < .001). Right ventricular free wall strain (RVFW-LS) was higher in the control group (25.63 ± 4.58% vs. 22.83 ± 4.83% vs. 20.79 ± 4.92%) than in the other two groups with a statistically significant difference (p < .001), while RVFW-LS was not statistically different between the prediabetic and diabetic groups. Multivariate regression analysis showed that HbA1c (ß = -.626, p < .001), IVS-LS (ß = .417, p < .001), and left ventricular end-diastolic diameter (LVEDd) (ß = .191, p = .011) were independently correlated with RVGLS. CONCLUSIONS: Two-dimensional speckle tracking echocardiography can sensitively detect subtle changes in the early impairment of right ventricular systolic function in patients with abnormal glucose metabolism. Type 2 diabetes is the common mechanism causing impaired myocardial mechanics in the right and left ventricles. The reduced global systolic longitudinal strain of the right ventricle was associated with reduced global septal longitudinal strain and left ventricular remodeling. HbA1c is an independent predictor of the global longitudinal strain of the right ventricle, and controlling blood glucose levels may be expected to improve the extent of myocardial damage.

Efficacy and confounding factors of CT attenuation value differences in distinguishing acute and old vertebral compression fractures: a retrospective study.

PURPOSE: To evaluate the influence of various factors on CT attenuation values (HUs) of acute and old fracture vertebra, and to determine the efficacy of HU differences (△HUs) in the differentiation of the two type of fractures. MATERIALS AND METHODS: A total of 113 acute and 71 old fracture vertebrae confirmed by MRI were included. Four HUs measured at the mid-sagittal, upper 1/3 axial, mid-axial, and lower 1/3 axial planes of each vertebra were obtained. The △HUs between fracture vertebra and its control counterpart was calculated. Receiver operating characteristic (ROC) curve analysis was used and the areas under the ROC curve (AUC) were calculated to evaluate the efficacy of HUs and △HUs. To evaluate the effect of height reduction, region, age and gender on HUs and △HUs, one-way analysis of variance, Pearson correlation analysis and t-test were used. RESULTS: The HUs and △HUs at the upper 1/3 axial plane achieved the highest AUCs of 0.801 and 0.839, respectively. The HUs decreased gradually from Thoracic to Lumbar in control group of acute fracture. While no significant differences were found in the HUs among the 3 localizations in both fracture groups (all P > 0.05). The HUs were negatively correlated with age in all groups. The HUs of male were significantly higher than female patients in all groups (all P < 0.05). While △HU was not significantly different between males and females (all P > 0.05). CONCLUSION: The vertebral HUs at the upper 1/3 axial plane are more likely to identify acute fractures. △HUs were beneficial in eliminating interfering factors.

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures.

PURPOSE: To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). MATERIALS AND METHODS: 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. RESULTS: For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. CONCLUSION: Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.

BACKGROUND: Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency. METHODS: Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system. RESULTS: Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60 s to 189.12 s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively. CONCLUSIONS: With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.

Plasma-derived extracellular vesicles transfer microRNA-130a-3p to alleviate myocardial ischemia/reperfusion injury by targeting ATG16L1.

Extracellular vesicles (EVs) are implicated in myocardial ischemia/reperfusion (I/R) injury as modulators by shuttling diverse cargoes, including microRNAs (miRNAs). The current study was initiated to unravel the potential involvement of plasma-derived EVs carrying miR-130a-3p on myocardial I/R injury. Rats were induced with moderate endoplasmic reticulum stress, followed by isolation of plasma-derived EVs. Then, an I/R rat model and hypoxia/reoxygenation (H/R) cardiomyoblast model were established to simulate a myocardial I/R injury environment where miR-130a-3p was found to be abundantly expressed. miR-130a-3p was confirmed to target and negatively regulate autophagy-related 16-like 1 (ATG16L1) in cardiomyoblasts. Based on a co-culture system, miR-130a-3p delivered by EVs derived from plasma protected H/R-exposed cardiomyoblasts against H/R-induced excessive cardiomyoblast autophagy, inflammation, and damage, improving cardiac dysfunction as well as myocardial I/R-induced cardiac dysfunction and tissue injury. The mechanism underlying the functional role of EVs-loaded miR-130a-3p was found to be dependent on its targeting relation with ATG16L1. The protective action of EV-carried miR-130a-3p was further re-produced in a rat model serving as in vivo validation as evidenced by improved cardiac function, tissue injury, myocardial fibrosis, and myocardial infarction. Collectively, miR-130a-3p shuttled by plasma-derived EVs was demonstrated to alleviate excessive cardiomyoblast autophagy and improve myocardial I/R injury.

Utilization of T1-Mapping for the pelvic and thigh muscles in Duchenne Muscular Dystrophy: a quantitative biomarker for disease involvement and correlation with clinical assessments.

BACKGROUND: Little is known about the disease distribution and severity detected by T1-mapping in Duchenne muscular dystrophy (DMD). Furthermore, the correlation between skeletal muscle T1-values and clinical assessments is less studied. Hence, the purposes of our study are to investigate quantitative T1-mapping in detecting the degree of disease involvement by detailed analyzing the hip and thigh muscle, future exploring the predicting value of T1-mapping for the clinical status of DMD. METHODS: Ninety-two DMD patients were included. Grading fat infiltration and measuring the T1-values of 19 pelvic and thigh muscles (right side) in axial T1-weighted images (T1WI) and T1-maps, respectively, the disease distribution and severity were evaluated and compared. Clinical assessments included age, height, weight, BMI, wheelchair use, timed functional tests, NorthStar ambulatory assessment (NSAA) score, serum creatine kinase (CK) level. Correlation analysis were performed between the muscle T1-value and clinical assessments. Multiple linear regression analysis was conducted for the independent association of T1-value and motor function. RESULTS: The gluteus maximus had the lowest T1-value, and the gracilis had the highest T1-value. T1-value decreased as the grade of fat infiltration increased scored by T1WI (P < 0.001). The decreasing of T1-values was correlated with the increase of age, height, weight, wheelchair use, and timed functional tests (P < 0.05). T1-value correlated with NSAA (r = 0.232-0.721, P < 0.05) and CK (r = 0.208-0.491, P < 0.05) positively. T1-value of gluteus maximus, tensor fascia, vastus lateralis, vastus intermedius, vastus medialis, and adductor magnus was independently associated with the clinical motor function tests (P < 0.05). Interclass correlation coefficient (ICC) analysis and Bland-Altman plots showed excellent inter-rater reliability of T1-value region of interest (ROI) measurements. CONCLUSION: T1-mapping can be used as a quantitative biomarker for disease involvement, further assessing the disease severity and predicting motor function in DMD.

Quantitative Evaluation of Myocardial Strain After Myocardial Infarction with Cardiovascular Magnetic Resonance Tissue-Tracking Imaging.

To investigate the value of cardiovascular magnetic resonance tissue-tracking (CMR-TT) imaging in the differentiation of subendocardial and transmural myocardial infarction (MI) and determine whether strain parameters are enable to detect adverse left ventricular (LV) remodeling.Global peak circumferential, longitudinal, and radial strains (GPCS, GPLS, GPRS) and segmental peak circumferential, longitudinal, and radial strains (PCS, PLS, PRS) in accordance with the 16-segment model were all derived. All positive segments were divided into two groups according to transmural degree. All patients were dichotomized in accordance with the existence of LV remodeling, which was defined as infarct size (IS) > 24%.Patients with MI showed significant lower GPRS, GPCS, and GPLS than the control group (16.41% ± 8.92%, -8.77%± 3.51%, -7.54% ± 2.43% versus 32.41% ± 12.99%, -14.92% ± 3.32%, -11.50% ± 2.51%). Lower PRS [3.25% (-5.57, 7.835) versus 19.94% (12.50, 30.75), P < 0.001] and PCS (-3.81 ± 4.60% versus -8.97± 4.43%, P < 0.001) can be found in transmural infarcted segments compared to subendocardial infarcted segments. PLS between transmural and subendocardial infarcted segments (-4.03% ± 4.88% versus -4.34% ± 4.98%), without however statistical significance (P = 0.523). The optimal cutoff value for PRS in the discriminate diagnosis of MI was 8.97% with a sensitivity of 81.8% and specificity of 98.0%. The optimal cutoff value for PCS was -7.56% with a sensitivity of 83.6% and specificity of 72.1%. Receiver operating characteristic (ROC) analysis revealed an optimal cutoff GPRS of 15.45%, and GPCS of -6.72% yielded high diagnostic accuracy in the identification of remodeling, which was higher than left ventricular ejection fraction (LVEF).CMR-TT can differentiate between subendocardial and transmural infarction and detect LV remodeling, and the diagnostic value was superior to conventional functional parameters.

Value of Cardiac Magnetic Resonance Fractal Analysis Combined With Myocardial Strain in Discriminating Isolated Left Ventricular Noncompaction and Dilated Cardiomyopathy.

BACKGROUND: Excessive trabeculation is present in isolated left ventricular noncompaction (LVNC) and dilated cardiomyopathy (DCM), which sometimes makes the differentiation between these two difficult. Fractal dimension (FD) is a unitless measure value of how completely the object fills space, which can assess the extent of myocardial trabeculae quantitatively. PURPOSE: To compare the trabeculae features and myocardial strain derived from cardiac MR between LVNC and DCM. STUDY TYPE: Respective case-control series. POPULATION: In all, 35 LVNC patients and 30 DCM patients were enrolled, and 20 healthy volunteers were selected as a control group. FIELD STRENGTH/SEQUENCE: 5 T with 8-channel phased-array cardiac receiver coil including steady-state free precession cine imaging. ASSESSMENT: The degree of left ventricular trabeculation was evaluated by a semiautomatic tool based on fractal analysis. Myocardial deformation was assessed by feature tracking. STATISTICAL TESTS: Independent samples Student's t-test, Mann-Whitney U-test, receiver operating characteristics (ROC) curves, and Spearman's rank coefficient were conducted. RESULTS: Max apical FD and mean global FD were higher in the LVNC group than in the DCM group (1.433 ± 0.074 vs. 1.341 ± 0.062, P < 0.001; 1.323 ± 0.036 vs. 1.267 ± 0.041, P < 0.001, respectively). For diagnosing LVNC, max apical FD was 1.392 (area under the curve [AUC] = 0.881, 95% confidence interval [CI]: 0.804-0.957), and the cutoff value of mean global FD was 1.283 (AUC = 0.895, 95% CI: 0.828-0.961). The global peak longitudinal strain value of the left ventricle (GPLS) showed significant differences between the LVNC group and DCM group [-6.49 (-11.41, -4.90) vs. -4.61 (-5.87, -3.61), P = 0.006]. The diagnostic accuracy for LVNC is highest when using FDs in coordination with GPLS (AUC = 0.93, 95% CI: 0.86-0.98, P < 0.001). DATA CONCLUSION: Fractal analysis provides a quantitative measurement of myocardial trabeculation. The combination of fractal analysis with myocardial strain provides a novel biomarker in distinguishing LVNC from DCM. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:153-163.

Two Patients with Coincident Noncompacted Myocardium and Hypertrophic Cardiomyopathy.

To our knowledge, left ventricular noncompaction (LVNC) and hypertrophic cardiomyopathy (HCM) commonly occur as separate disorders in different patients; however, LVNC associated with HCM, which is called hypertrophic LVNC, is relatively rare.1) Here we report two sporadic cases of hypertrophic LVNC which were diagnosed by echocardiography and cardiac magnetic resonance (CMR).

An Atypical Right Atrial Myxoma With Spontaneous Rupture.

Typically, cardiac maxomas arise from the interatrial septum at the border of the fossa ovalis in the left atrium, whereas atypical right atrial myxoma, especially with spontaneous rupture, is extremely rare. Here we report the diagnostic evaluation and successful surgical resection of an atypical myxoma with spontaneous rupture arising from the posterior wall of the right atrium in a 34-year-old male.

Feasibility of using 8 mL of iodinated contrast media in cerebral computed tomographic angiography with a dual-layer spectral detector.

Background: Virtual monoenergetic images (VMIs) at a low energy level can improve image quality when the amount of iodinated contrast media (CM) is reduced. The purpose was to evaluate the feasibility of using an extremely low CM volume and injection rate in cerebral computed tomography angiography (CTA) on a dual-layer spectral detector computed tomography (CT). Methods: Patients who were clinically suspected of intracranial aneurysm or cerebrovascular diseases were included in our study (from June to November 2022). In this prospective study, 80 patients were randomly enrolled into group A (8 mL of CM with a 1-mL/s flow rate) or group B (40 mL of CM with 4-mL/s flow rate). The VMIs at 40-70 keV in group A and polychromatic conventional images in the 2 groups were reconstructed. CT attenuation, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated via the t-test or Mann-Whitney test (2 groups), while analysis of variance or Kruskal-Wallis test (multiple groups). Subjective image quality was assessed on a 5-point scale. Results: In group A, the subjective image quality score, CT attenuation, and CNR of the internal carotid artery (ICA) and middle cerebral artery (MCA) were the highest on VMIs at 40 keV. The image noise on VMIs at 40 keV was 5.08±0.84 Hounsfield units. The subjective image quality score, CT value of the ICA, MCA, and cerebral parenchyma on VMIs at 40 keV in group A were similar to those in group B (all P values >0.05). Compared to those in group B, the VMIs at 40 keV in group A demonstrated a significantly higher mean SNR and CNR of the ICA (mean SNR: 46.22±20.18 vs. 34.32±12.40, P=0.002; CNR: 55.47±13.43 vs. 46.18±12.30, P=0.002) and MCA [SNR: 13.66 (9.78, 20.29) vs. 9.99 (7.53, 14.00), P=0.003; CNR: 47.00±12.71 vs. 39.45±10.47, P=0.005]. Conclusions: Cerebral CTA on VMIs at 40 keV with 8 mL of CM and a 1-mL/s injection rate can provide diagnostic image quality.

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Bibliometric analysis of the association between periodontal disease and cardiovascular disease.

Purpose: Conduct a bibliometric analysis to review the knowledge structure and research trends regarding the association between periodontal disease and cardiovascular disease (CVD). Methods: The Web of Science Core collection database was searched for retrieving publications related to periodontitis and CVD between January 1, 2003 and December 31, 2022. The VOSviewer, CiteSpace, and R software package "bibliometrix" were employed for the bibliometric analysis. Results: In total, 3447 articles were collected from 98 countries over the past 20 years, with the United States (1,003), Japan (377), and China (321) contributing the most publications. The literature in this field exhibited exponential growth. The University of Helsinki (n = 125, 1.37 %) holds the distinction of being the research institution with the highest number of publications, with a predominant representation from institutions in the United States. Notably, the Journal of Periodontology emerges as the most popular journal in the field, whereas the Journal of Clinical Periodontology takes the lead in terms of citations. These publications originated from 15,236 authors, with Pussinen (n = 40) having the highest number of published papers and Tonetti (n = 976) garnering the most citations. The visualization analysis of keywords identified "oral microbiome," "inflammation," and "porphyromonas gingivalis" as emerging research hotspots in exploring the relationship between periodontitis and CVDs. Conclusion: Through a comprehensive bibliometric analysis, this study posits that periodontitis may heighten the risk of cardiovascular events, offering valuable academic references for scholars investigating the link between periodontitis and CVDs.

The use of cardiac magnetic resonance in hypertrophic cardiomyopathy over the past 10 years [2013-2023]: a CiteSpace-based bibliometric analysis.

Background: Hypertrophic cardiomyopathy (HCM) is a common genetic cardiac disorder characterized by the hypertrophy of a segment of the myocardium. Cardiac magnetic resonance (CMR) has been widely used in the assessment of HCM. However, no bibliometric assessment has been conducted on the progress of research in this field. This study thus aimed to examine the current state of research into the application of CMR in HCM and the hotspots and trends that have emerged in this field over the past decade. Methods: A systematic search was conducted on the Web of Science regarding CMR in the assessment of HCM. The databases were searched from 2013 to June 2023. CiteSpace is an application that can be used to characterize the underlying knowledge of the scientific literature in a given field. We used it to analyze the relationship between publication year and country, institution, journal, author, bibliography, and keywords in the field of CMR for the assessment of HCM. Results: A total of 1,427 articles were included in the analysis. In the assessment of HCM, the findings from the past decade have consistently demonstrated a progressive rise in the quantity of articles pertaining to CMR. The country with the largest number of publications was the United States [310], and the institution with the greatest number of publications was the University College London [45]. The analysis of keywords revealed the diagnosis and management of HCM with CMR to be the current research focus and emerging trend within this academic field. Conclusions: This study used a novel approach to visually analyze the use of CMR in HCM assessment. The current research trajectory in CMR consists of the diagnosis and management of patients with HCM. Although most studies confirmed the indispensability of CMR in the assessment of HCM, larger-scale cohorts are still needed to more comprehensively evaluate the role of CMR in the differential diagnosis, pre- and post-treatment assessment, and long-term management of patients with HCM.

The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis.

BACKGROUND: Thyroid nodules are common lesions in benign and malignant thyroid diseases. More and more studies have been conducted on the feasibility of artificial intelligence (AI) in the detection, diagnosis, and evaluation of thyroid nodules. The aim of this study was to use bibliometric methods to analyze and predict the hot spots and frontiers of AI in thyroid nodules. METHODS: Articles on the application of artificial intelligence in thyroid nodules were retrieved from the Web of Science core collection database. A website (https://bibliometric.com/), VOSviewer and CiteSpace software were used for bibliometric analyses. The collaboration maps of countries and institutions were analyzed. The cluster and timeline view based on cocitation references and keywords citation bursts visualization map were generated. RESULTS: The study included 601 papers about AI in thyroid nodules. China contributed to more than half (52.41%) of these publications. The cluster view and timeline view of co-citation references were assembled into 9 clusters, "AI", "deep learning", "papillary thyroid carcinoma", "radiomics", "ultrasound image", "biomarkers", "medical image segmentation", "central lymph node metastasis (CLNM)", and "self-organizing auto-encoder". The "AI", "radiomics", "medical image segmentation", "deep learning," and "CLNM", emerging in the last 10 years and continuing until recent years, were included. CONCLUSION: An increasing number of scholars were devoted to this field. The potential future research hotspots include risk factor assessment and CLNM prediction of thyroid carcinoma based on radiomics and deep learning, automatic segmentation based on medical images (especially ultrasound images).

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs). MATERIALS AND METHODS: The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA). RESULTS: Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model. CONCLUSION: Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs.

CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study.

OBJECTIVES: The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. METHODS: A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value. RESULTS: 3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy. CONCLUSION: The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.