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.

Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features.

BACKGROUND: Distinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma. METHODS: Approximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA). RESULTS: Ultimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone. CONCLUSION: The nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Three-dimensional echocardiography and strain cardiac imaging in patients with prediabetes and type 2 diabetes mellitus.

Background: Several studies using two-dimensional speckle tracking echocardiography (2D-STE) have confirmed the presence of left ventricular (LV) systolic dysfunction in patients with diabetes mellitus (DM), but there is a paucity of studies on whether three-dimensional (3D)-STE is superior to 2D-STE. The aim of this study was to evaluate the clinical value of 3D-STE in assessing subclinical LV systolic dysfunction in prediabetic and diabetic patients with preserved LV ejection fraction (LVEF) and to investigate the independent risk factors for this medical disorder. Methods: This study included 40 diabetic patients, 35 prediabetic patients, and 33 healthy volunteers. All participants underwent LV peak systolic strain analysis using 3D- and 2D-STE, and the receiver operating characteristic (ROC) curve was constructed to determine the clinical diagnostic value of strain parameters for evaluating subclinical LV dysfunction in patients with prediabetes and type 2 DM (T2DM). Regression models were established to analyze independent risk factors for subclinical LV systolic dysfunction in patients with prediabetes and diabetes. Results: The results of the 3D-STE-based analysis showed that the global longitudinal strain (GLS) of the control, prediabetic, and diabetic groups were (18.64%±2.43%, 15.21%±1.49%, and 13.49%±2.36%, respectively), global circumferential strain (GCS) was (18.09%±2.37%, 14.62%±1.75%, and 12.95%±2.20%, respectively), global area strain (GAS) was (31.30%±3.88%, 27.51%±3.31%, and 24.80%±3.86%, respectively), and global radial strain (GRS) was (49.18%±5.91%, 39.17%±4.55%, and 35.72%±7.19%, respectively). All 3D-STE global strain parameters gradually decreased from the controls, through the prediabetic group to the diabetic group, and there was statistical significance between the three groups (P<0.001). The area under the curve (AUC) of the 3D-STE global strain parameters (GLS, GCS, GAS, and GRS) were 0.898, 0.831, 0.863, and 0.868, respectively. The AUC of the 2D-STE global strain parameters (GLS and GCS) were 0.867 and 0.636, respectively. Multivariate regression analysis identified increased glycosylated hemoglobin A1c (HbA1c) and body mass index (BMI) as independent risk factors for subclinical LV systolic dysfunction. Conclusions: Prediabetic and diabetic patients with preserved LVEF are at risk of subclinical LV systolic dysfunction. 3D-STE is a reliable imaging technique for evaluating early damage to LV myocardial mechanics. Early control of blood glucose (Glu) levels and weight can effectively prevent heart failure in the prediabetic and diabetic populations.

Application of medical imaging in ovarian cancer: a bibliometric analysis from 2000 to 2022.

Background: Ovarian cancer (OC) is the most lethal tumor within the female reproductive system. Medical imaging plays a significant role in diagnosis and monitoring OC. This study aims to use bibliometric analysis to explore the current research hotspots and collaborative networks in the application of medical imaging in OC from 2000 to 2022. Methods: A systematica search for medical imaging in OC was conducted on the Web of Science Core Collection on August 9, 2023. All reviews and articles published from January 2000 to December 2022 were downloaded, and an analysis of countries, institutions, journals, keywords, and collaborative networks was perfomed using CiteSpace and VOSviewer. Results: A total of 5,958 publications were obtained, demonstrating a clear upward trend in annual publications over the study peroid. The USA led in productivity with 1,373 publications, and Harvard University emerged as the most prominent institution with 202 publications. Timmerman D was the most prolific contributor with 100 publications, and Gynecological Oncology led in the number of publications with 296. The top three keywords were "ovarian cancer" (1,256), "ultrasound" (725), and "diagnosis" (712). In addition, "pelvic masses" had the highest burst strength (25.5), followed by "magnetic resonance imaging (MRI)" (21.47). Recent emergent keywords such as "apoptosis", "nanoparticles", "features", "accuracy", and "human epididymal protein 4 (HE 4)" reflect research trends in this field and may become research hotspots in the future. Conclusion: This study provides a comprehensive summary of the key contributions of OC imaging to field's development over the past 23 years. Presently, primary areas of OC imaging research include MRI, targeted therapy of OC, novel biomarker (HE 4), and artificial intelligence. These areas are expected to influence future research endeavors in this field.

The predictive value of coronary microvascular dysfunction for left ventricular reverse remodelling in dilated cardiomyopathy.

Aims: To evaluate the degree of coronary microvascular dysfunction (CMD) in dilated cardiomyopathy (DCM) patients by cardiac magnetic resonance (CMR) first-pass perfusion parameters and to examine the correlation between myocardial perfusion and left ventricle reverse remodelling (LVRR). Methods: In this study, 94 DCM patients and 35 healthy controls matched for age and sex were included. Myocardial perfusion parameters, including upslope, time to maximum signal intensity (Timemax), maximum signal intensity (SImax), baseline signal intensity (SIbaseline), and the difference between maximum and baseline signal intensity (SImax-baseline) were measured. Additionally, left ventricular (LV) structure, function parameters, and late gadolinium enhancement (LGE) were also recorded. The parameters were compared between healthy controls and DCM patients. Univariable and multivariable logistic regression analyses were used to determine the predictors of LVRR. Results: With a median follow-up period of 12 months [interquartile range (IQR), 8-13], 41 DCM patients (44%) achieved LVRR. Compared with healthy controls, DCM patients presented CMD with reduced upslope, SIbaseline, and increased Timemax (all p < 0.01). Timemax, SImax, and SImax-baseline were further decreased in LVRR than non-LVRR group (Timemax: 60.35 [IQR, 51.46-74.71] vs. 72.41 [IQR, 59.68-97.70], p = 0.017; SImax: 723.52 [IQR, 209.76-909.27] vs. 810.92 [IQR, 581.30-996.89], p = 0.049; SImax-baseline: 462.99 [IQR, 152.25-580.43] vs. 551.13 [IQR, 402.57-675.36], p = 0.038). In the analysis of multivariate logistic regression, Timemax [odds ratio (OR) 0.98; 95% confidence interval (CI) 0.95-1.00; p = 0.032)], heart rate (OR 1.04; 95% CI 1.01-1.08; p = 0.029), LV remodelling index (OR 1.73; 95% CI 1.06-3.00; p = 0.038) and LGE extent (OR 0.85; 95% CI 0.73-0.96; p = 0.021) were independent predictors of LVRR. Conclusions: CMD could be found in DCM patients and was more impaired in patients with non-LVRR than LVRR patients. Timemax at baseline was an independent predictor of LVRR in DCM.

Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022.

Background: Prostate cancer (PCa) is the most common tumor of the male genitourinary system. With the development of imaging technology, the role of magnetic resonance imaging (MRI) in the management of PCa is increasing. The present study summarizes research on the application of MRI in the field of PCa using bibliometric analysis and predicts future research hotspots. Methods: Articles regarding the application of MRI in PCa between January 1, 1984 and June 30, 2022 were selected from the Web of Science Core Collection (WoSCC) on November 6, 2022. Microsoft Excel 2016 and the Bibliometrix Biblioshiny R-package software were used for data analysis and bibliometric indicator extraction. CiteSpace (version 6.1.R3) was used to visualize literature feature clustering, including co-occurrence analysis of countries, institutions, authors, references, and burst keywords analysis. Results: A total of 10,230 articles were included in the study. Turkbey was the most prolific author. The USA was the most productive country and had strong partnerships with other countries. The most productive institution was Memorial Sloan Kettering Cancer Center. Journal of Magnetic Resonance Imaging and Radiology were the most productive and highest impact factor (IF) journals in the field, respectively. Timeline views showed that "#1 multiparametric magnetic resonance imaging", "#4 pi-rads", and "#8 psma" were currently the latest research hotspots. Keywords burst analysis showed that "machine learning", "psa density", "multi parametric mri", "deep learning", and "artificial intelligence" were the most frequently used keywords in the past 3 years. Conclusions: MRI has a wide range of applications in PCa. The USA is the leading country in this field, with a concentration of highly productive and high-level institutions. Meanwhile, it can be projected that "deep learning", "radiomics", and "artificial intelligence" will be research hotspots in the future.

The application of radiology for dilated cardiomyopathy diagnosis, treatment, and prognosis prediction: a bibliometric analysis.

Background: Radiology plays a highly crucial role in the diagnosis, treatment, and prognosis prediction of dilated cardiomyopathy (DCM). Related research has increased rapidly over the past few years, but systematic analyses are lacking. This study thus aimed to provide a reference for further research by analyzing the knowledge field, development trends, and research hotspots of radiology in DCM using bibliometric methods. Methods: Articles on the radiology of DCM published between 2002 and 2021 in the Web of Science Core Collection database (WoSCCd) were searched and analyzed. Data were retrieved and analyzed using CiteSpace V, VOSviewer, and Scimago Graphic software, and included the name, research institution, and nationality of authors; journals of publication; and the number of citations. Results: A total of 4,257 articles were identified on radiology of DCM from WoSCCd. The number of articles published in this field has grown steadily from 2002 to 2021 and is expected to reach 392 annually by 2024. According to subfields, the number of papers published in cardiac magnetic resonance field increased steadily. The authors from the United States published the most (1,364 articles, 32.04%) articles. The author with the most articles published was Bax JJ (54 articles, 1.27%) from Leiden University Medical Center. The most cited article was titled "2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure", with 138 citations. Citation-based clustering showed that arrhythmogenic cardiomyopathy, T1 mapping, and endomyocardial biopsy are the current hots pots for research in DCM radiology. The most frequently occurring keyword was "dilated cardiomyopathy". The keyword-based clusters mainly included "late gadolinium enhancement", "congestive heart failure", "cardiovascular magnetic resonance", "sudden cardiac death", "ventricular arrhythmia", and "cardiac resynchronization therapy". Conclusions: The United States and Northern Europe are the most influential countries in research on DCM radiology, with many leading distinguished research institutions. The current research hots pots are myocardial fibrosis, risk stratification of ventricular arrhythmia, the prognosis of cardiac resynchronization therapy (CRT) treatment, and subtype classification of DCM.

The evolution and current situation in the application of dual-energy computed tomography: a bibliometric study.

Background: Dual-energy computed tomography (DECT) has received extensive attention in clinical practice; however, a quantitative assessment of published literature in this domain is presently lacking. This study thus aimed to characterize the application conditions, developmental trends, and research hot spots of DECT using bibliometric analysis. Methods: All literature on DECT was retrieved from the Web of Science Core Collection (WoSCC) on January 22, 2023. The co-occurrence, cooperation network, and co-citation of countries, institutions, references, authors, journals, and keywords were analyzed using CiteSpace, VOSviewer, and R-bibliometrix software. Results: In total, 4,720 original articles and reviews were included. The number of publications related to DECT has rapidly increased since 2006. The USA (n=1,662) and Mayo Clinic (n=178) were found to be the most productive country and institution, respectively. The most cited article was published by Johnson TRC et al., while the article published by McCollough CH et al. in 2015 had the most co-citations. Schoepf UJ ranked first with most articles among 16,838 authors. The journal with the most published articles was European Radiology, with 411 publications. The timeline analysis indicated that material decomposition was the most recent topic, followed by gout, radiomics, proton therapy, and bone marrow edema. Conclusions: An increasing number of researchers are committed to researching DECT, with the USA making the most significant contributions in this area. Prior studies have primarily concentrated on cardiovascular diseases, and contemporary hot spots include expansion into to other fields, such as iodine quantification, deep learning, and bone marrow edema.

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.

The potential predictive value of cardiac mechanics for left ventricular reverse remodelling in dilated cardiomyopathy.

AIMS: Left ventricular reverse remodelling (LVRR) is an important objective of optimal medical management for dilated cardiomyopathy (DCM) patients, as it is associated with favourable long-term outcomes. Cardiac magnetic resonance (CMR) can comprehensively assess cardiac structure and function. We aimed to assess the CMR parameters at baseline and investigate independent variables to predict LVRR in DCM patients. METHODS AND RESULTS: Nighty-eight initially diagnosed DCM patients who underwent CMR and echocardiography examinations at baseline were included. CMR parameters and feature tracking (FT) based left ventricular (LV) global strain (nStrain) and nStrain indexed to LV cardiac mass index (rStrain) were measured. The predictors of LVRR were determined by multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of CMR parameters and were compared by the DeLong test. At a median follow-up time of 9 [interquartile range, 7-12] months, 35 DCM patients (36%) achieved LVRR. The patients with LVRR had lower LV volume, mass, LGE extent and stroke volume index (LVSVi) and higher left ventricular remodelling index (LVRI), nStrains, rStrains, and peak systolic strain rate (PSSR) in the longitudinal direction and rStrains in the circumferential direction at baseline (all P < 0.05). In the multivariate logistic regression analyses, LVRI [per SD, odds ratio (OR) 1.79; 95% confidence interval (CI) 1.08-2.98; P = 0.024] and the ratio of global longitudinal peak strain (rGLPS) (per SD, OR 1.88; 95% CI 1.18-3.01; P = 0.008) were independent predictors of LVRR. The combination of LVSVi, LVRI, and rGLPS had a greater area under the curve (AUC) than the combination of LVSVi and LVRI (0.75 vs. 0.68), but not significantly (P = 0.09). CONCLUSIONS: Patients with LVRR had a lower LV volume index, lower LVSV index, lower LGE extent, higher LVRI, and preserved myocardial deformation in the longitudinal direction at baseline. LVRI and rGLPS at baseline were independent determinants of LVRR.