Evaluation of split renal dysfunction using radiomics based on magnetic resonance diffusion-weighted imaging.

BACKGROUND: Accurate and noninvasive assessment of split renal dysfunction is crucial, while there is lack of corresponding method clinically. PURPOSE: To investigate the feasibility of using diffusion-weighted imaging (DWI)-based radiomics models to evaluate split renal dysfunction. METHODS: We enrolled patients with impaired and normal renal function undergoing renal DWI examination. Glomerular filtration rate (GFR, mL/min) was measured using 99mTc-DTPA scintigraphy, which is reference standard of GFR measurement. The kidneys were classified into normal (GFR ≥40), mildly impaired (20≤ GFR < 40), moderately impaired (10≤ GFR < 20), and severely impaired (GFR < 10) renal function groups. Optimized subsets of radiomics features were selected from renal DWI images and radiomics scores (Rad-score) calculated to discriminate groups with different renal function. The radiomics model (Rad-score based) was developed in a training cohort and validated in a test cohort. Evaluations were conducted on the discrimination, calibration, and clinical application of the method. RESULTS: The final analysis included 330 kidneys. Logistic regression was used to develop three radiomics models, model A, B, and C, which were used to distinguish normal from impaired, mild from moderate, and moderate from severe renal function, respectively. The area under the curve of the three models were 0.822, 0.704, and 0.887 in the training cohort and 0.843, 0.717, and 0.897 in the test cohort, respectively, indicating efficient discrimination performance. CONCLUSIONS: DWI-based radiomics models have potential for evaluating split renal dysfunction and discriminating between normal and impaired renal function groups and their subgroups.

Intratumoral and Peritumoral Edema Radiomics Based on Fat-Suppressed T2- Weighted Imaging for Preoperative Prediction of Triple-Negative Breast Cancer.

AIM: Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). METHODS: This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model. RESULTS: The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 82.5 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis. CONCLUSION: The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

Association between coronary artery disease and clinical outcome in cancer patients: A propensity score matching analysis.

Objective: The aim of this study was to evaluate the prognostic value of coronary artery disease (CAD) detected by coronary computed tomography angiography (CTA) to predict the risk of all-cause mortality in cancer patients in a propensity score matching (PSM) analysis. Methods: A total of 331 patients who previously had cancer and underwent coronary CTA from January 2015 to December 2019 were included. Multivariate Cox proportional hazards regression analysis and propensity-score matching analysis were performed. The primary endpoint was all-cause of mortality. Results: In total, 125 with CAD and 206 with no CAD during a median follow-up of 3.3 years were included in this study. After PSM, age (HR, 1.040; 95%CI, 1.001-1.081; p = 0.014) and CAD (HR, 2.164; 95%CI, 1.057-4.430; p = 0.035) remained significant factors for all-cause mortality. Conclusion: CAD evaluated by coronary CTA was found to be at higher risk for all-cause mortality in cancer patients. Due to the retrospective design and lack of information on some medical history and treatments, especially immune checkpoint inhibitors, a large-scale prospective study is needed to further determine the prognostic value of coronary CTA in cancer patients.

The Correlation between Region-specific Epicardial Adipose Tissue and Myocardial Ischemia Defined by CT-FFR in Type 2 Diabetes Mellitus Patients.

BACKGROUND: Epicardial Adipose Tissue (EAT) accumulation is closely associated with the presence and severity of coronary artery disease (CAD), myocardial ischemia, plaque vulnerability, and major adverse cardiovascular events. OBJECTIVE: The aim of this study was to investigate the correlation between myocardial ischemia defined by computed tomography-derived fractional flow reserve (CT-FFR) and region-specific EAT in patients with type 2 diabetes mellitus (T2DM). METHODS: Between January 2022 and May 2023, 200 T2DM patients were randomly selected from the Department of Endocrinology in The Central Hospital of Wuhan. These patients were divided into two groups based on myocardial ischemia defined by CT-FFR: myocardial ischemia group (152 cases) and control group (48 cases). Both groups of patients used a post-treatment workstation to measure the thickness of region-specific EAT. Receiver operating characteristic (ROC) curve analysis and binary logistic regression were used to evaluate the correlation between various parameters and myocardial ischemia. RESULTS: Patients in the myocardial ischemia group had significantly higher values of age, male gender, systolic blood pressure, total cholesterol, triglycerides, LDL, HDL, fasting blood glucose, fasting insulin, HOMA-IR, EAT thickness in right ventricular wall, left atrioventricular groove, and superior and inferior interventricular groove. ROC curve analysis results showed that EAT thickness in the left atrioventricular groove had the largest area under the ROC curve for diagnosing myocardial ischemia (0.837 [95% CI 0.766-0.865]; P < 0.001). Binary logistic regression analysis showed that EAT thickness in the left atrioventricular groove was an independent risk factor for myocardial ischemia in patients with T2DM (P < 0.05). CONCLUSION: The EAT thickness in the left atrioventricular groove is an independent risk factor for myocardial ischemia in patients with T2DM. Adipose tissue in the left atrioventricular groove region plays a major role in EAT-mediated CAD.

Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.

BACKGROUND: Limited studies have investigated the accuracy of therapeutic decision-making using machine learning-based coronary computed tomography angiography (ML-CCTA) compared with CCTA. PURPOSE: To investigate the performance of ML-CCTA for therapeutic decision compared with CCTA. MATERIAL AND METHODS: The study population consisted of 322 consecutive patients with stable coronary artery disease. The SYNTAX score was calculated with an online calculator based on ML-CCTA results. Therapeutic decision-making was determined by ML-CCTA results and the ML-CCTA-based SYNTAX score. The therapeutic strategy and the appropriate revascularization procedure were selected using ML-CCTA, CCTA, and invasive coronary angiography (ICA) independently. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, accuracy of ML-CCTA and CCTA for selecting revascularization candidates were 87.01%, 96.43%, 95.71%, 89.01%, 91.93%, and 85.71%, 87.50%, 86.27%, 86.98%, 86.65%, respectively, using ICA as the standard reference. The area under the receiver operating characteristic curve (AUC) of ML-CCTA for selecting revascularization candidates was significantly higher than CCTA (0.917 vs. 0.866, P = 0.016). Subgroup analysis showed the AUC of ML-CCTA for selecting percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) candidates was significantly higher than CCTA (0.883 vs. 0.777, P < 0.001, 0.912 vs. 0.826, P = 0.003, respectively). CONCLUSION: ML-CCTA could distinguish between patients who need revascularization and those who do not. In addition, ML-CCTA showed a slightly superior to CCTA in making an appropriate decision for patients and selecting a suitable revascularization strategy.

Prognostic value of coronary CTA-based classifications for predicting major events without obstructive coronary artery disease.

We aim to explore the classifications based on coronary computed tomography angiography (CTA) for predicting the risk of major adverse cardiovascular events (MACE) in patients with suspected non-obstructive coronary artery disease (CAD) and compare with traditional non-obstructive CAD (NOCAD) classification, Duke prognostic NOCAD index, Non-obstructive coronary artery disease reporting and data system (NOCAD-RADS). 4378 consecutive non-obstructive CAD patients were assessed by coronary CTA for traditional NOCAD classification, Duke prognostic NOCAD index, NOCAD-RADS and a new classification (stenosis proximal involvement, SPI) from two medical centrals. We defined proximal involvement as any plaque was present in the main or proximal segments of coronary artery (left main, left anterior descending artery, left circumflex artery, or right coronary artery). The main outcome was MACE. During a median follow-up of 3.7 years, a total of 310 patients experienced MACE event. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with traditional NOCAD, Duke NOCAD index, NOCAD-RADS and SPI classifications (all P < 0.001). In multivariate Cox regressions, the risk for the events increased from HR 1.20 (95% CI 0.78-1.83, P = 0.408) for SPI 1 to 1.35 (95% CI 1.05-1.73, P = 0.019) for SPI 2, using SPI 0 as the reference group. Coronary CTA based SPI classification provided important prognostic information for all cause-mortality risk and MACE prediction in patients with non-obstructive CAD, which was non-inferior than traditional NOCAD, Duke NOCAD Index and NOCAD-RADS classifications. The plaque location information by coronary CTA may provide additional risk prediction in patients with non-obstructive CAD.

Non-obstructive CAD and Risk of All-cause Mortality in Middle-aged and Older Patients: a Nine-Year Follow-up and Multicentre Study.

AIM: We aimed to examine all-cause mortality risk in relation to the extent of non-obstructive coronary artery disease (CAD) by coronary computed tomography angiography (CTA) in Chinese middle-aged and older patients in a multicenter study with nine-year follow-up. METHODS: This was a retrospective, observational, multicentre study. The study population consisted of 3,240 consecutive middle-aged and older patients (age ≥ 40 years) with suspected CAD who underwent coronary CTA between June 2011 and December 2013 at three hospitals in Wuhan, China. Patients were grouped according to CAD extent for the final analysis: no CAD, 1-vessel non-obstructive CAD, 2-vessels non-obstructive CAD, and 3-vessels non-obstructive CAD. The primary endpoint was all-cause mortality. Kaplan-Meier method and Cox proportional hazards regression models were used for analysis. RESULTS: A total of 2,522 patients were included in the present analysis. Of these, 188 (7.5%) deaths occurred during the median 9.0 years (interquartile range 8.6-9.4) of study follow-up. The annualized all-cause mortality rate was 0.54 (95% CI: 0.44-0.68), 0.91 (95% CI: 0.68-1.21), 1.44 (95% CI: 1.01-1.93), and 2.00 (95% CI: 1.46-2.69) for the no CAD, 1-vessel non-obstructive CAD, 2-vessels non-obstructive CAD, and 3-vessels non-obstructive CAD group, respectively. Kaplan-Meier survival curves showed a significant increase in the cumulative events associated with the extent of non-obstructive CAD (P < 0.001). In multivariate Cox regression, after adjustment for age and sex, the presence of 3-vessels non-obstructive CAD was a significant predictor of all-cause mortality (HR 1.60, 95% CI: 1.04-2.45, P = 0.032). CONCLUSION: In this cohort of Chinese middle-aged and older patients undergoing coronary CTA, the presence and extent of non-obstructive CAD, compared to no CAD, were associated with a significantly greater nine-year risk of all-cause mortality. The present findings suggest the clinical importance of the stage of non-obstructive CAD and warrant investigation of the optimal risk stratification to improve outcomes among these patients.

Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA.

Objectives: The aim of the present study was to investigate the prognostic value of the novel coronary artery disease reporting and data system (CAD-RADS) 2.0 compared with CAD-RADS 1.0 in patients with suspectedcoronary artery disease (CAD) evaluated by convolutional neural networks (CNN) based coronary computed tomography angiography (CCTA). Methods: A total of 1796 consecutive inpatients with suspected CAD were evaluated by CCTA for CAD-RADS 1.0 and CAD-RADS 2.0 classifications. Kaplan-Meier and multivariate Cox models were used to estimate major adverse cardiovascular events (MACE) inclusive of all-cause mortality or myocardial infarction (MI). The C-statistic was used to assess the discriminatory ability of the two classifications. Results: In total, 94 (5.2%) MACE occurred over the median follow-up of 45.25 months (interquartile range 43.53-46.63 months). The annualized MACE rate was 0.014 (95% CI: 0.011-0.017). Kaplan-Meier survival curves indicated that the CAD-RADS classification, segment involvement score (SIS) grade, and Computed Tomography Fractional Flow Reserve (CT-FFR) classification were all significantly associated with the increase in the cumulative MACE (all P < 0.001). CAD-RADS classification, SIS grade, and CT-FFR classification were significantly associated with endpoint in univariate and multivariate Cox analysis. CAD-RADS 2.0 showed a further incremental increase in the prognostic value in predicting MACE (c-statistic 0.702, 95% CI: 0.641-0.763, P = 0.047), compared with CAD-RADS 1.0. Conclusions: The novel CAD-RADS 2.0 evaluated by CNN-based CCTA showed higher prognostic value of MACE than CAD-RADS 1.0 in patients with suspected CAD.

Relationship of blood heavy metals and osteoporosis among the middle-aged and elderly adults: A secondary analysis from NHANES 2013 to 2014 and 2017 to 2018.

Objective: This study aimed to assess the relationship between blood heavy metals and a higher prevalence of osteoporosis in middle-aged and elderly US adults using the National Health and Nutritional Examination Surveys (NHANES). Methods: The secondary data analysis was performed using the data of NHANES 2013-2014 and 2017-2018. We used the information, including physical examination, laboratory tests, questionnaires, and interviews, provided by participants in NHANES. Logistic regression and weighted quantile sum (WQS) regression models were used to explore the relationships between levels of blood heavy metals and a higher prevalence of osteoporosis. Results: A total of 1,777 middle-aged and elderly participants were analyzed in this study, comprising 115 participants with osteoporosis and 1,662 without osteoporosis. Adjusted model 1 showed a significant positive relationship between cadmium (Cd) levels and a higher prevalence of osteoporosis (quartile 2, OR = 7.62; 95% CI, 2.01-29.03; p = 0.003; quartile 3, OR = 12.38; 95% CI, 3.88-39.60; p < 0.001; and quartile 4, OR = 15.64; 95% CI, 3.22-76.08; p = 0.001). The fourth quartile of selenium (Se) level (OR = 0.34; 95% CI, 0.14-0.39; p < 0.001) led to a lower prevalence of osteoporosis and exerted a protective effect on model 1. Other models produced similar results to those of model 1. A subgroup analysis showed that Cd levels were positively related to a higher prevalence of osteoporosis in all three models in women, while this relationship was not found in men. The fourth quartile of the Se level was related to a lower prevalence of osteoporosis in both male and female analyses. A significant positive relationship was found between the blood Cd level and a higher prevalence of osteoporosis in the non-smoking subgroup. Blood Se level showed a protective effect on the fourth quartile in both the smoking and non-smoking subgroups. Conclusion: Blood Cd level aggravated the prevalence of osteoporosis, while blood Se level could be a protective factor in osteoporosis among the US middle-aged and older populations.

Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks.

RATIONALE AND OBJECTIVES: The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients. MATERIALS AND METHODS: Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort. RESULTS: Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001). CONCLUSION: The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

Graft Patency Assessment with CCTA Using a Comprehensive Singlebranch Bridging Score.

AIMS: This study aims to assess the prognostic value of graft patency with coronary computed tomography angiography (CCTA) using a comprehensive single-branch targeted atherosclerotic risk score (CSBS) in patients before coronary artery bypass grafting (CABG). METHODS: This retrospective study contains a total of 88 patients who underwent clinical CCTA before off-pump CABG surgery between 2015 and 2018. Graft failure was defined as patients with missing multi-slice CCTA or coronary angiography. The predictive value of CSBS (ranging from 0-70 and divided into 2 groups: < 20 and ≥20) was analyzed using Kaplan-Meier analysis and Cox regression models. RESULTS: Patients' mean age was 61.2 ± 10.5 years, with a mean follow-up of 20.4 ± 15.2 months. A total of 203 grafts (21.5% arterial grafts) were analyzed and 30 of the vessels were occluded (14.8%). There was no significant difference in graft occlusion among the three targeted vessel groups. The Cox proportional hazard analysis showed that CSBS < 20 was a significant predictor of graft failure. CONCLUSION: Lower comprehensive single-branch targeted atherosclerotic risk score evaluated by CCTA is an independent prognostic factor for graft failure in patients before CABG surgery.

Dual-energy Computed Tomography for the Diagnosis of Acute Gouty Arthritis.

AIMS: To investigate the diagnostic value of Dual-energy Computed Tomography (DECT) in Acute Gouty Arthritis (AGA) or patients presenting suspected gouty arthritis. METHODS: This retrospective study was performed in a single centre from May 2017 to August 2018. Two hundred and twenty-six patients with an initial diagnosis of AGA in the preceding 15 days were included. All patients were referred for a DECT scan of the affected joints. The diagnosis criteria of gout with the American College of Rheumatology Classification Standard were regarded as the reference standard. RESULTS: After filtration, two hundred patients were included in the present study. The sensitivity, specificity, positive predictive value, and negative predictive value of DECT in the diagnosis of all AGA were 83.83%, 60.61%, 91.5%, and 42.55%, respectively. When AGA was subdivided according to the joint site, the sensitivity, specificity, positive predictive value, and negative predictive value were 80.68%, 61.11%, 91.03%, and 39.29% in feet, 93.55%, 40%, 93.55%, and 40% in knees and 87.5%, 71.43%, 91.3%, and 62.5% in ankles, respectively. CONCLUSION: DECT had a high sensitivity for the diagnosis of AGA. However, the specificity was limited, particularly for the diagnosis of acute gouty knee arthritis. Prospective multicenter studies of large samples will enhance the application of DECT among AGA patients in the future.

Evaluation of Aortic Distensibility in Patients with Nonalcoholic Fatty Liver Using CT.

BACKGROUND: Nonalcoholic fatty liver disease has attracted considerable attention with continuously increasing morbidity. OBJECTIVE: To evaluate the aortic distensibility in patients with non-diabetic and hypertension-type Nonalcoholic Fatty Liver Disease (NAFLD) through Dual-Source Computed Tomography (DSCT). METHODS: 120 patients with NAFLD (experimental group) and 30 healthy subjects (control group) were consecutively enrolled in the study. In the two groups, aortic distensibility was calculated as D = ΔA/(A0 ×Δp). Record fasting insulin, fasting blood glucose, fasting lipid status, age, heart rate, waist circumference, systolic blood pressure, and diastolic blood pressure. Calculate homeostasis model assessment of insulin resistance (HOMA-IR) and Body Mass Index (BMI). A comparative analysis between the two groups was carried out, followed by a correlation analysis between D value and risk factors. RESULTS: D value and liver attenuation of the patients in the NAFLD group were significantly reduced relative to those in the control group (2.24±0.63×10-3 mmHg-1 vs. 3.19±0.86×10-3 mmHg-1, P<0.001 and 41±6HU vs. 53±5HU, P<0.001, respectively) and their fasting blood glucose, fasting insulin, triglyceride, low-density lipoprotein, aspartate aminotransferase, alanine transaminase, HOMA- IR, and BMI were higher than those in the control group. Liver attenuation, HOMA-IR, age, and BMI were significantly correlated with D value in the NAFLD group. The stepwise multiple linear regression analysis indicates that liver attenuation and HOMA-IR were the significant risk factors for D value (ß coefficient =0.43, P =0.001, and ß coefficient =-0.33, P =0.02, respectively). CONCLUSION: Patients with NAFLD suffer from a reduction in aortic distensibility, and insulin resistance may play a significant role in the early atherosclerosis stage.

Prognostic value of CAD-RADS classification by coronary CTA in patients with suspected CAD.

BACKGROUND: The study sought to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) classification with traditional coronary artery disease (CAD) classifications and Duke Prognostic CAD Index for predicting the risk of all-cause mortality in patients with suspected CAD. METHODS: 9625 consecutive suspected CAD patients were assessed by coronary CTA for CAD-RADS classification, traditional CAD classifications and Duke Prognostic CAD Index. Kaplan-Meier and multivariable Cox models were used to estimate all-cause mortality. Discriminatory ability of classifications was assessed using time dependent receiver-operating characteristic (ROC) curves and The Hosmer-Lemeshow goodness-of-fit test was employed to evaluate calibration. RESULTS: A total of 540 patients died from all causes with a median follow-up of 4.3 ± 2.1 years. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with CAD-RADS, three traditional CAD classifications and Duke Prognostic CAD Index. In multivariate Cox regressions, the risk for the all-cause death increased from HR 0.861 (95% CI 0.420-1.764) for CAD-RADS 1 to HR 2.761 (95% CI 1.961-3.887) for CAD-RADS 4B&5, using CAD-RADS 0 as the reference group. The relative HRs for all-cause death increased proportionally with the grades of the three traditional CAD classifications and Duke Prognostic CAD Index. The area under the time dependent ROC curve for prediction of all-cause death was 0.7917, 0.7805, 0.7991for CAD-RADS in 1 year, 3 year, 5 year, respectively, which was non-inferior to the traditional CAD classifications and Duke Prognostic CAD Index. CONCLUSIONS: The CAD-RADS classification provided important prognostic information for patients with suspected CAD with noninvasive evaluation, which was non-inferior than Duke Prognostic CAD Index and traditional stenosis-based grading schemes in prognostic value of all-cause mortality. Traditional and simplest CAD classification should be preferable, given the more number of groups and complexity of CAD-RADS and Duke prognostic index, without using more time consuming classification.

Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study.

BACKGROUND: Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA). METHODS: Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: computer depth learning group (n=43) and experimental group (n=42). For the training set (13,144 sheets) and the test set (313 sheets), the auxiliary diagnosis method was obtained and applied to the experimental group. RESULTS: Among the participants, a good sensitivity of 90.9% and an average false positive of 2.0 were obtained by using the deep learning detection method proposed in this paper, and the detection rate was positively correlated with arterial grade. CONCLUSIONS: The computer-aided diagnostic method proposed in this paper can effectively improve the detection rate of PE, especially for the detection of intra-arterial embolism above grade 3. However, because of the high misdetection rate, more in-depth learning datasets are needed for the detection of embolism below grade 3.

Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan, China.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. METHODS: This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID-19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. RESULTS: Patients with severe/critical symptoms were older (p < 0.001) and more often male (p = 0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (> 11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811-0.902). CONCLUSIONS: Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (> 11) was associated with severe illness.

The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography.

This study sought to evaluate the association of breast arterial calcification (BAC) on breast screening mammography with the Coronary Artery Disease-Reporting and Data System (CAD-RADS) based on Deep Learning-coronary computed tomography angiography (CCTA). This prospective single institution study included asymptomatic women over 40 who underwent CCTA and breast cancer screening mammography between July 2018 and April 2019. CAD-RADS was scored based on Deep Learning (DL). Mammograms were assessed visually for the presence of BAC. A total of 213 patients were included in the analysis. In comparison to the low CAD-RADS (CAD-RADS < 3) group, the high CAD-RADS (CAD-RADS ≥ 3) group, more often had a history of hypertension (P = 0.036), diabetes (P = 0.017), and chronic kidney disease (P = 0.006). They also had a significantly higher level of LDL-C (P = 0.024), while HDL-C was lower than in the low CAD-RADS group (P = 0.003). BAC was also significantly higher in the high CAD-RADS group (P = 0.002). In multivariate analysis, the presence of BAC [odd ratio (OR) 10.22, 95% CI 2.86-36.49, P < 0.001] maintained a significant associations with CAD-RADS after adjustment by meaningful variable. The same tendency was also found after adjustment by all covariates. There was a significant correlation between the severities of CAD detected by DL based CCTA and BAC in women undergoing breast screening mammography. BAC may be used as an additional diagnostic tool to predict the severity of CAD in this population.

Evaluation of LVDD by CCTA with Dual-source CT in Type 2 Diabetes Mellitus Patients.

BACKGROUND: Left ventricular diastolic dysfunction (LVDD) is a common abnormality among patients in T2DM. AIMS: We aimed to evaluate the feasibility of coronary computed tomography angiography (CCTA) for the assessment of LVDD in type 2 diabetes mellitus (T2DM) patients. METHODS: 80 consecutive T2DM patients who were referred for a clinically dual-source CCTA examination to evaluate suspected coronary artery disease and also underwent 2D echocardiography within 7 days of CCTA inclusion and exclusion criteria, were performed. Correlation between CCTA and echocardiography was tested through linear regression and Bland-Altman analysis. RESULTS: In total, 60 T2DM patients were included for the analysis. Pearson correlation showed good correlation for E (r = 0.28; P = 0.028), E/A (r = 0.69; P < 0.01); E (r = -0.06; P = 0.776), E/A (r = 0.54; P = 0.003) and E (r = 0.64; P < 0.01), E/A (r = 0.83; P < 0.01) in three groups, respectively. Overall, diagnostic accuracy for assessment in CCTA of diastolic dysfunction was 79.76% (95% CI: 68%-91%), 71.43% (95% CI: 58%-85%) and 87.50 (95% CI: 79%-96%) in three groups. CONCLUSION: The presented study proved that CCTA showed good correlations in the estimation of LV filling pressures compared with echocardiography in T2DM patients. Accordingly, retrospectively ECG-gated CCTA may provide valuable information on the evaluation of LVDD in T2DM patients.

Icariin promotes osteogenic differentiation of BMSCs by upregulating BMAL1 expression via BMP signaling.

Increasing research has demonstrated that expression of brain and muscle ARNT­like 1 (BMAL1) and other circadian clock genes can be regulated by drugs and toxicants. We previously demonstrated that icariin, extracted from Herba Epimedii, sromotes osteogenic differentiation. However, the mechanism underlying the association between icariin and BMAL1 in osteogenic differentiation of bone marrow­derived mesenchymal stem cells (BMSCs) remains unclear. The present study was designed with an aim to clarify the association between icariin and BMAL1 in osteogenic differentiation of BMSCs. The Cell Counting Kit­8 assay was used to evaluate cell proliferation. The expression of bone morphogenetic protein 2 (BMP2), RUNX family transcription factor 2 (RUNX2), alkaline phosphatase (ALP), osteocalcin (OC) and BMAL1 in BMSCs was evaluated by reverse transcription­quantitative PCR and western blotting. ALP and Alizarin red S (ARS) staining were also performed. Icariin promoted BMSC proliferation, and upregulated expression of osteogenic genes and BMAL1. In addition, expression of the osteogenic genes BMP2, RUNX2, ALP and OC were upregulated by BMAL1 overexpression. Furthermore, we confirmed that BMAL1 deficiency suppressed osteogenic differentiation in BMSCs. Finally, ARS staining of BMAL1­/­ BMSCs revealed that BMAL1 was an essential intermediary in matrix mineralization during osteogenic differentiation. In conclusion, these results demonstrated that icariin promoted osteogenic differentiation through BMAL1­BMP2 signaling in BMSCs. The present study thus described a novel target of icariin that has potential applications in the treatment of osteogenic disorders.

Icariin regulates the osteoblast differentiation and cell proliferation of MC3T3-E1 cells through microRNA-153 by targeting Runt-related transcription factor 2.

Osteoporosis has become one of the most serious public health problems. Icariin, miR-153 and Runt-related transcription factor 2 (Runx2) have been demonstrated to regulate cell proliferation and differentiation in multiple cells. The aim of the present experiments was to investigate the potential mechanism underlying osteoblast differentiation and cell proliferation of MC3T3-E1 cells treated with icariin. Cell Counting kit-8, alkaline phosphatase (ALP) activity and alizarin red S assays, as well as reverse transcription-quantitative polymerase chain reaction and western blot analysis, were performed to examine whether icariin promoted osteoblast differentiation and cell proliferation in MC3T3-E1 cells. Subsequently, miR-153 target and pathway prediction, and functional analysis were assessed. The results demonstrated that icariin promoted proliferation, mineral content and ALP activity in MC3T3-E1 cells. In addition, miR-153 and Runx2 expression levels were increased following treatment with icariin. Luciferase assay revealed that miR-153 significantly upregulate the luciferase activity of wild-type (Wt) Runx2 3'-untranslated region. Furthermore, the group treated with a combination of miR-153 mimics and icariin exhibited a significantly higher expression of Runx2 in comparison with the miR-153 mimic-treated alone group. Finally, icariin reversed the potential effect of miR-153 inhibitor in MC3T3-E1 cells. In conclusion, icariin exerted a strong osteoblast differentiation effect in MC3T3-E1 cells through the miR-153/Runx2 pathway. The current study provided evidence suggesting that icariin should be considered as an effective candidate for the management of osteoporosis.