Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia.

OBJECTIVES: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia. METHODS: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification. RESULTS: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000. CONCLUSION: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia. KEY POINTS: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning.

OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. MATERIALS AND METHODS: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. RESULTS: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. CONCLUSION: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

Feasibility of the radial artery as a vascular access route in performing primary percutaneous coronary intervention.

We aimed to evaluate the feasibility of transradial primary percutaneous coronary intervention (PCI) in patients with ST elevation myocardial infarction (STEMI) by comparing the procedural results and complications with those of transfemoral intervention. From April 1997 to October 2004, we enrolled 352 consecutive cases of STEMI who underwent primary PCI. The femoral route was used in 132 cases (TFI group) and the radial route was used in 220 cases (TRI group). Cases with Killips class IV, a negative Allen test or a non-palpable radial artery were excluded from our study. Baseline clinical and angiographic profiles were comparable in both groups. Vascular access time was 3.8 +/- 3.5 min in the TFI group and 3.6 +/- 3.1 min in the TRI group, and cath room to reperfusion time was 25 +/- 11 min in the TRI group and 26 +/- 13 min in the TRI group. The procedural success rate was 89% in the TFI group and 88% in the TRI group. Crossover occurred in 9 cases (4%) due to approaching vessel tortuosity in the TRI group. Major access site complications occurred in 7 cases (5%) in the TFI group, and there were no complications in the TRI group (p < 0.001). Although radial occlusion occurred in 5 cases of the TRI group, there was no evidence of hand ischemia. The total hospital stay was significantly shorter in TRI group than in TFI group. In conclusion, use of the radial artery might be a potential vascular access route in performing primary PCI in selected cases.

Trans-1,3-diphenyl-2,3-epoxypropan-1-one, a chalcone derivative, induces apoptosis via ROS-mediated down-regulation of Bcl-xL in human leukemia HL-60 cells.

The anticancer effects of trans-1,3-diphenyl-2,3-epoxypropan-1-one (DPEP), a chalcone derivative, were investigated in human leukemia HL-60 cells. Treatment of HL-60 cells with various concentration of DPEP resulted in a sequence of events characteristic of apoptosis, including loss of cell viability, morphological changes, and increased sub-G1 DNA content. We demonstrated that DPEP elevates reactive oxygen species (ROS) levels in HL-60 cells, and that the ROS scavenger N-acetylcysteine (NAC) could block DPEP-induced ROS generation and apoptosis. Western blot analysis revealed that DPEP inhibits Bcl-xL expression, leading to caspase-3 activation and poly-ADP-ribose polymerase (PARP) cleavage, thereby inducing apoptosis. However, NAC pre-treatment significantly inhibited the activation of caspase-3 and PARP cleavage and reduced Bcl-xL levels. These findings provide the first evidence that DPEP may inhibit the growth of HL-60 cells and induce apoptosis through a ROS-mediated Bcl-xL pathway.