Unsupervised machine learning reveals epicardial adipose tissue subtypes with distinct atrial fibrosis profiles in patients with persistent atrial fibrillation: A prospective 2-center cohort study

CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue

PURPOSE

We aimed to evaluate the reproducibility of computed tomography (CT) radiomic features (RFs) about Epicardial Adipose Tissue (EAT). The features derived from coronary photon-counting computed tomography (PCCT) angiography datasets using the PureCalcium (VNCPC) and conventional virtual non-contrast (VNCConv) algorithm were compared with true non-contrast (TNC) series.

METHODS

RFs of EAT from 52 patients who underwent PCCT were quantified using VNCPC, VNCConv, and TNC series. The agreement of EAT volume (EATV) and EAT density (EATD) was evaluated using Pearson's correlation coefficient and Bland-Altman analysis. A total of 1530 RFs were included. They are divided into 17 feature categories, each containing 90 RFs. The intraclass correlation coefficients (ICCs) and concordance correlation coefficients (CCCs) were calculated to assess the reproducibility of RFs. The cutoff value considered indicative of reproducible features was > 0.75.

RESULTS

the VNCPC and VNCConv tended to underestimate EATVs and overestimate EATDs. Both EATV and EATD of VNCPC series showed higher correlation and agreement with TNC than VNCConv series. All types of RFs from VNCPC series showed greater reproducibility than VNCConv series. Across all image filters, the Square filter exhibited the highest level of reproducibility (ICC = 67/90, 74.4%; CCC = 67/90, 74.4%). GLDM_GrayLevelNonUniformity feature had the highest reproducibility in the original image (ICC = 0.957, CCC = 0.958), exhibiting a high degree of reproducibility across all image filters.

CONCLUSION

The accuracy evaluation of EATV and EATD and the reproducibility of RFs from VNCPC series make it an excellent substitute for TNC series exceeding VNCConv series.

Use of a two-handed model to improve comprehension of ventricular outflow tract anatomy

BACKGROUND

Mastering cardiac anatomy is a formidable obstacle in the learning process for cardiac electrophysiology trainees. The complex three-dimensional characteristics and contiguous relationship of the ventricular outflow tract are particularly difficult to visualize with the limited study methods available. The hands can recreate a morphology similar to the ventricular outflow tract; this study explored whether a two-handed model of the heart helps electrophysiology trainees improve their understanding of ventricular outflow tract anatomy.

METHODS

After an initial assessment, trainees were randomly placed into variable and control groups. Subsequently, all trainees learned the outflow tract anatomy using routine methods, with the variable group receiving additional instruction using the two-handed model. One day and one week after the course conclusion, knowledge of the ventricular outflow tract anatomy was assessed for the participants in both groups.

RESULTS

Thirty-eight trainees participated (19 in each group). The median scores obtained for the first, second, and third tests were 38 (24,55), 80 (70,86), and 75 (70,81) points, respectively. In the second test, trainees in the variable group had a mean score 6.8 points higher than those in the control group (p = 0.103); in the last test, the mean score was 9.7 points higher in the variable group than in the control group (p = 0.003).

CONCLUSIONS

It is convenient to use hands to create a model representing the ventricular outflow tract. Trainees using this model had a better understanding and retention of the ventricular outflow tract anatomy compared to those of the control group.