Purpose: To evaluate the safety, efficacy, and long-term outcome of bronchial artery embolization (BAE) in the treatment of non-massive hemoptysis and the prognostic factors associated with recurrent bleeding. Materials and methods: From March 2005 to September 2014, BAE was performed in 233 patients with non-massive hemoptysis. All patients had a history of persistent or recurrent hemoptysis despite conservative medical treatment. We assessed the technical and clinical success, recurrence, prognostic factors related to recurrent bleeding, recurrence-free survival rate, additional treatment, and major complications in all the patients. Results: Technical success was achieved in 224 patients (96.1%), and clinical success was obtained in 219 (94.0%) of the 233 patients. In addition, 64 patients (27.5%) presented hemoptysis recurrence with median time of 197 days after embolization. Tuberculosis sequelae and presence of aberrant bronchial artery or non-bronchial systemic collaterals were significantly related to recurrent bleeding (p < 0.05). The use of Histoacryl-based embolic materials significantly reduced the recurrent bleeding rate (p < 0.05). Patient who had a tuberculosis sequelae showed a significantly lower recurrence-free survival rate (p = 0.013). Presence of aberrant bronchial artery or non-bronchial systemic collaterals showed a statistically significant correlation with recurrence-free survival rate (p = 0.021). No patients had major complications during follow-up. Conclusions: BAE is a safe and effective treatment to manage non-massive hemoptysis. The procedure may offer a better long-term control of recurrent hemoptysis and quality of life than conservative therapy alone.

Irritable bowel syndrome is a chronic disorder affecting the gastrointestinal tract, negatively impacting patients’ quality of life. Here, we aimed to evaluate the effects of Lacticaseibacillus rhamnosus IDCC 3201 (RH 3201) on irritable bowel syndrome with constipation (IBS-C). In this randomised, double-blind, placebo-controlled trial, a total of 30 subjects with IBS-C were randomly assigned (1:1) to receive 8 weeks of probiotics administration or placebo. Concerning bowel activities, both irritant bowel movements and discomfort caused by constipation showed significant improvement with RH 3201 at 8 weeks. Symptoms including severity of abdominal bloating, frequency of abdominal bloating, and satisfaction of bowel habits based on the irritable bowel syndrome-severity scoring system also ameliorated in the probiotic group. Analysis of the fecal microbiome revealed that the abundance of Bacteroides cellulosilyticus and Akkermansia muciniphila was higher during the period of RH 3201 administration compared to the placebo. Untargeted metabolome analysis further suggested a correlation between specific metabolites, such as N-acetylornithine, xanthine, and 3-phenylpropionic acid, and the improvement of clinical symptoms. These results indicate that RH 3201 was effective in ameliorating IBS-C, potentially by enriching beneficial microbes and associated metabolites in the gut environment.

In this study, we aimed to investigate quantitative differences in performance in terms of comparing the automated classification of deep vein thrombosis (DVT) using two categories of artificial intelligence algorithms: deep learning based on convolutional neural networks (CNNs) and conventional machine learning. We retrospectively enrolled 659 participants (DVT patients, 282; normal controls, 377) who were evaluated using contrast-enhanced lower extremity computed tomography (CT) venography. Conventional machine learning consists of logistic regression (LR), support vector machines (SVM), random forests (RF), and extreme gradient boosts (XGB). Deep learning based on CNN included the VGG16, VGG19, Resnet50, and Resnet152 models. According to the mean generated AUC values, we found that the CNN-based VGG16 model showed a 0.007 higher performance (0.982 ± 0.014) as compared with the XGB model (0.975 ± 0.010), which showed the highest performance among the conventional machine learning models. In the conventional machine learning-based classifications, we found that the radiomic features presenting a statistically significant effect were median values and skewness. We found that the VGG16 model within the deep learning algorithm distinguished deep vein thrombosis on CT images most accurately, with slightly higher AUC values as compared with the other AI algorithms used in this study. Our results guide research directions and medical practice.

The detection and segmentation of thrombi are essential for monitoring the disease progression of abdominal aortic aneurysms (AAAs) and for patient care and management. As they have inherent capabilities to learn complex features, deep convolutional neural networks (CNNs) have been recently introduced to improve thrombus detection and segmentation. However, investigations into the use of CNN methods is in the early stages and most of the existing methods are heavily concerned with the segmentation of thrombi, which only works after they have been detected. In this work, we propose a fully automated method for the whole process of the detection and segmentation of thrombi, which is based on a well-established mask region-based convolutional neural network (Mask R-CNN) framework that we improve with optimized loss functions. The combined use of complete intersection over union (CIoU) and smooth L1 loss was designed for accurate thrombus detection and then thrombus segmentation was improved with a modified focal loss. We evaluated our method against 60 clinically approved patient studies (i.e., computed tomography angiography (CTA) image volume data) by conducting 4-fold cross-validation. The results of comparisons to multiple other state-of-the-art methods suggested the superior performance of our method, which achieved the highest F1 score for thrombus detection (0.9197) and outperformed most metrics for thrombus segmentation.

Kim JW, et al. Korean J Radiol. 2020 Jul;21:e118. https://doi.org/10.3348/kjr.2020.0067

Byun SS, et al. Korean J Radiol. 2008 Jan-Feb;9(1):59-66. http://dx.doi.org/10.3348/kjr.2008.9.1.59