Radiomics reflects the texture and morphological features of tumours by quantitatively analysing the grey values of medical images. We aim to develop a nomogram incorporating radiomics and the Breast Imaging Reporting and Data System (BI-RADS) for predicting breast cancer in BI-RADS ultrasound (US) category 4 or 5 lesions. From January 2017 to August 2018, a total of 315 pathologically proven breast lesions were included. Patients from the study population were divided into a training group (n = 211) and a validation group (n = 104) according to a cut-off date of March 1 st , 2018. Each lesion was assigned a category (4A, 4B, 4C or 5) according to the second edition of the American College of Radiology (ACR) BI-RADS US. A radiomics score was generated from the US image. A nomogram was developed based on the results of multivariate regression analysis from the training group. Discrimination, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed in the validation group. The radiomics score included 9 selected radiomics features. The radiomics score and BI-RADS category were independently associated with breast malignancy. The nomogram incorporating the radiomics score and BI-RADS category showed better discrimination (area under the receiver operating characteristic curve [AUC]: 0.928; 95% confidence interval [CI]: 0.876, 0.980) between malignant and benign lesions than either the radiomics score ( P = 0.029) or BI-RADS category ( P = 0.011). The nomogram demonstrated good calibration and clinical usefulness. In conclusion, the nomogram combining the radiomics score and BI-RADS category is potentially useful for predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

We aimed to study the long-term sinus reversion rate and recovery of left atrial function after modified surgical radiofrequency ablation for permanent atrial fibrillation caused by mitral valve disease. From March 2014 to May 2020, 35 patients who underwent modified surgical radiofrequency ablation during cardiac valve surgery in our hospital were selected as the study group, and 25 normal individuals without cardiac structural changes were selected as the control group. The time of modified surgical radiofrequency ablation and long-term sinus reversion rate were measured, and left atrial anteroposterior, superoinferior, left and right diameters, left atrial ejection fraction, left atrial filling index, and left atrial ejection force were measured before and 6 months after surgery. The mean ablation time was 23.2 min, and the long-term sinus reversion rate was 80.0%. The left atrium diameter decreased and the left atrium ejection fraction increased after the operation ( P < 0.05 ). The left atrium filling index and ejection force were significantly increased in 28 patients with sinus reversion ( P < 0.05 ). The decrease in left atrial diameter and the increase in left atrial ejection fraction were correlated with sinus conversion after surgery ( P < 0.05 ). The modified operation is simple, the curative effect is definite, and the sinus reversion rate is high, which is beneficial to the restoration of left atrial structure, ejection function, and hemodynamic function.

Objective To investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicated by arrhythmias. Methods The clinical data of 158 patients with hypertrophic cardiomyopathy were retrospectively collected from July 2019 to December 2021, and additionally divided into training group 106 cases, validation group 26 cases and test group 26 cases according to the ratio of 4:1:1, and divided into concurrent and non-concurrent groups according to whether they were complicated by arrhythmia or not, respectively. General data of patients (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) were collected, a deep learning model for cardiac ultrasound flow imaging was established, and image data, LVEF, LAVI, E/e', vortex area change rate, circulation intensity change rate, mean blood flow velocity, and mean EL value were extracted. Results The differences in general data (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) between the three groups were not statistically significant, P > 0.05. The differences in age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR between the patients in the concurrent and non-concurrent groups in the training group were not statistically significant, P > 0.05. Conclusions Deep learning-based cardiac ultrasound flow imaging can identify cardiac ultrasound images more accurately and has a high predictive value for arrhythmias complicating hypertrophic cardiomyopathy, and vortex area change rate, circulation intensity change rate, mean flow velocity, mean EL, LAVI, and E/e' are all risk factors for arrhythmias complicating hypertrophic cardiomyopathy.