Aims: This study aimed to evaluate the morphologic characteristics of double kissing (DK) mini-culotte and mini-culotte stenting through imaging of bench testing.Methods and results: DK mini-culotte and mini-culotte stenting were performed in two silicone bifurmini-culotte group. The results of factorial analysis showed that maximum distance of malapposed struts F Conclusions: Compared with mini-culotte stenting, DK mini-culotte stenting was associated with shorter metal carina length, shorter maximum distance of malapposed struts, and smaller SBO area stenosis. Thus, DK mini-culotte stenting may obtain better morphologic characteristics.

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Background De novo metastasis of breast cancer is a complex clinical issue to be identified. This study was the first to construct artificial neural networks (ANN) and logistic regression (LR) models with comparison to find out important factors associated with occurrence of de novo metastasis in invasive breast cancer. Methods A total of 40,899 patients diagnosed with de novo metastatic breast cancer in 2010 from Surveillance, Epidemiology and End Results (SEER) Cancer database were enrolled. ANN models and LR models were constructed based on thirteen relevant factors by 10-fold cross-validation approach respectively. Evaluation indexes as well as processing time were compared. Results Overall area under ROC curve (AUC) value of ANN models was significantly higher than that of LR models (0.917±0.01 vs. 0.844±0.011, P<0.001). In ANN models, number of positive ipsilateral axillary lymph nodes, tumor size, lymph node ratio (LNR) and regional lymph nodes status were important associated factors. While under the same experiment environment, ANN models obviously took much more processing time than LR models did (14,400 vs. 15 minutes for 10-fold cross-validation). Conclusions ANN models outperformed traditional LR models in identifying de novo metastasis of breast cancer. On the other hand, the much longer processing time of ANN models should also be considered.