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.

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.

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.

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.

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. 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).

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics.

RATIONALE AND OBJECTIVES: To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature. MATERIALS AND METHODS: A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis . RESULTS: Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful. CONCLUSION: This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Distinguishing Cardiac Amyloidosis and Hypertrophic Cardiomyopathy by Thickness and Myocardial Deformation of the Right Ventricle.

OBJECTIVES: To compare right ventricular thickness (RVT) and deformation of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) patients. METHODS: Sixty CA (mean age 58 ± 10 years; 33 males (55%)) and sixty HCM patients (mean age 55 ± 14 years; 27 males (45%)) were retrospectively enrolled. RVT, global radical peak strain (GRPS), global longitudinal peak strain (GLPS), and global circumferential peak stain (GCPS) were analyzed. To determine the cutoff values of the RVT and RV strain parameters for distinguishing CA from HCM, the areas under the receiver operating characteristic curve (AUCs) were analyzed. RESULTS: RVT of CA patients was significantly thicker than that of HCM patients (7.8 ± 2.1 vs 5.9 ± 1.3, p < 0.001). Moreover, significantly decreased RV-GRPS (12.1 ± 6.9 vs 23.5 ± 12.1, p < 0.001), RV-GCPS (-3.4 ± 2.2 vs -5.6 ± 3.5, p < 0.001), and RV-GLPS (-4.6 ± 2.3 vs -11.1 ± 4.9, p < 0.001) were observed in CA patients compared with HCM patients. RVT and RV strain demonstrate comparable diagnostic accuracy in differentiating CA from HCM. In particular, RV-GLPS combined with RVT showed the best performance for discriminating CA from HCM (AUC = 0.92, 95% CI: 0.85 to 0.96, p = 0.0001). CONCLUSIONS: Right ventricular myocardial thickness and deformation of CA patients was more severe than HCM patients. RV-GLPS combined with RVT presents an excellent diagnostic performance in distinguishing CA and HCM.

A special type aortico-left ventricular tunnel with bicuspid aortic valve.

Aortico-left ventricular tunnel (ALVT) combined with bicuspid aortic valve (BAV) is an extremely rare congenital heart malformation. This article presents an adult case of a special type ALVT combined with BAV which was diagnosed by echocardiography and computed tomographic arteriography (CTA) then successfully performed surgery and cured.

3.0 T magnetic resonance myocardial perfusion imaging for semi-quantitative evaluation of coronary microvascular dysfunction in hypertrophic cardiomyopathy.

This study aimed to assess coronary microvascular dysfunction (CMD) differences in hypertrophic cardiomyopathy (HCM) patients using cardiac magnetic resonance (CMR) first-pass perfusion and late gadolinium enhancement imaging. Forty-seven patients with HCM and twenty-one healthy volunteers underwent CMR at rest. Imaging protocols included short axis cine, first-pass myocardial perfusion, and late gadolinium enhancement (LGE). Left ventricular end-diastolic wall thickness (EDTH), LGE, time to peak (Tpeak), maximal up-slope (Slopemax), and peak signal intensity (SIpeak) were assessed for each myocardial segment. The HCM myocardial segments were grouped by the degree of LGE and hypertrophy. Tpeak, SIpeak, Slopemax and EDTH in multiple groups were assessed and compared by ANOVA test/Kruskal-Wallis test. The Spearman correlation test was used to determine the relationships between EDTH, LGE and perfusion parameters (Tpeak, Slopemax and SIpeak). Compared to control group segments, Tpeak increased while Slopemax and SIpeak decreased in non-LGE/non-hypertrophic segments and LGE/hypertrophic segments in the HCM group, while Tpeak increased more significantly in LGE/hypertrophic segments (all p < 0.05). Tpeak statistically increased with increasing degrees of myocardial LGE (p < 0.01). Differences in Tpeak, SIpeak and EDTH were observed between segments with and without hypertrophy (p < 0.05). EDTH and LGE were positively correlated with Tpeak (r = 0.279, p = 0.031 and r = 0.237, p < 0.001). 3.0 T magnetic resonance myocardial perfusion imaging identifies abnormal perfusion in non-LGE and non-hypertrophic segments of HCM patients, and it may be helpful in the early diagnosis of coronary microvascular dysfunction in HCM. This abnormal perfusion is associated with the severity of myocardial fibrosis and the degree of hypertrophy.