Predictive value of bilirubin and serum γ-glutamyltranspeptidase levels in type-2 diabetes mellitus patients with acute coronary syndrome.

Background: Cardiovascular disease is a major complication of diabetes mellitus (DM). Type-2 DM (T2DM) is associated with an increased risk of cardiovascular events and mortality, while serum biomarkers may facilitate the prediction of these outcomes. Early differential diagnosis of T2DM complicated with acute coronary syndrome (ACS) plays an important role in controlling disease progression and improving safety.

Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram.

Objective: The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors.

Methods: The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia ( = 58) and normal myocardial blood supply ( = 52) groups.

Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia.

Purpose: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis.

Methods: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

Objective: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC).

Methods: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans.

Adaptive prospective ECG-triggered sequence coronary angiography in dual-source CT without heart rate control: Image quality and diagnostic performance.

The aim of this study was to evaluate the accuracy of using second generation dual-source CT (DSCT) to obtain high quality images and diagnostic performance and to reduce the radiation dose in adaptive prospective electrocardiography (ECG)-triggered sequence (CorAdSeq) CT coronary angiography (CTCA) without heart rate control. No prescan β-blockers were administered. Un-enhanced CT and CTCA with adaptive prospective CorAdSeq scanning without heart rate control were performed in 683 consecutive patients divided into two body mass index (BMI) groups: BMI <25 kg/m(2) (group A, n=412) and BMI ≥25 kg/m(2) (group B, n=271).