The diagnostic value of dynamic volume computed tomography angiography in children with anomalous origin of the left coronary artery from the pulmonary artery.

There have been almost no reports on the technique of dynamic volume computed tomography angiography (DVCTA) in children with anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA). Twelve children with ALCAPA, aged 5 months to 15 years, were enrolled in this retrospective study to explore the clinical value of DVCTA in the diagnosis of ALCAPA in children. All patients underwent low-dose prospective ECG-gated 320-slice DVCTA and transthoracic echocardiography. Two radiologists evaluated the image quality of the DVCTA and recorded the radiation dose at the same time. The accuracy of DVCTA in the diagnosis of ALCAPA was 100%, with the left coronary artery (LCA) opening in the left wall of the pulmonary artery in 4 cases (33.3%), the right wall in 2 cases (16.7%), and the posterior wall in 6 cases (50.0%). All children completed 320-slice DVCTA at a single timepoint; all of the images were diagnosable, and the subjective score was 3.3±0.6, with good consistency between the evaluations performed by the two radiologists (k=0.79). From the echocardiographs of these cases, 4 cases (33.3%) of ALCAPA were diagnosed correctly, 4 cases (33.3%) were misdiagnosed as LCA-pulmonary artery fistula, and 4 cases (33.3%) were missed, including a small LCA that was not displayed in 2 cases. The average CT radiation dose was 0.83±0.57 mSv. Low-dose DVCTA clearly showed the origin, course, and collateral vessels of ALCAPA and could be used reliably for noninvasive diagnosis of ALCAPA in children.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.