Preoperative Omega-6/Omega-3 Fatty Acid Ratio Could Predict Postoperative Outcomes in Patients with Surgically Resected Non-Small-Cell Lung Cancer.

Introduction: The aim of this study was to determine whether preoperative nutritional status and inflammatory status, specifically polyunsaturated acids and the omega 6/3 ratio, would affect postoperative outcomes and complications in patients with lung cancer undergoing lung resection. Methods: This prospective observational study included 68 patients with early-stage non-small-cell lung cancer who were candidates for radical surgery. A complete nutritional assessment was performed. The primary study variable was postoperative complications and mortality in the first 30 days. Descriptive, bivariate, and logistic regression analyses were carried out. Results: A total of 50 men (73.53%) and 18 women (26.47%) underwent surgery, with a median age of 64.2 (±9.74) years. The mean omega 6/3 ratio was 17.39 (±9.45). A complication occurred in 39.7% of the study sample (n = 27), the most common being persistent air leak in 23.53% (n = 16). After performing the bivariate analysis, the only variable that remained significant was the omega 6/3 ratio; we observed that it had a prognostic value for persistent air leak (p = 0.001) independent of age, sex, comorbidity, preoperative respiratory function, and approach or type of surgery. The remaining nutritional and inflammatory markers did not have a statistically significant association (p > 0.05) with postoperative complications. However, this significance was not maintained in the multivariate analysis by a small margin (p = 0.052; 95% CI: 0.77-1.41). Conclusions: Omega 6/3 ratio may be a prognostic factor for air leak, independent of the patient's clinical and pathological characteristics.

Efficacy of hyperthermia pleurodesis: A comparative experimental study on serous membrane of abdominopelvic and thoracic cavities of rats.

BACKGROUND: Pleurodesis is a common technique for treating the accumulation of air or liquid in the pleural space caused by pneumothorax or pleural effusion, it is based on the bounding of pleural layers through induced inflammatory lesions. There are several pleurodesis procedures. OBJECTIVES: To test and describe the inflammatory effect of hyperthermia on the pleural and peritoneal mesothelia of rats, with the aim of testing the effectiveness of this process for inducing pleurodesis. METHODS: 35 Sprague-Dawley (male/female) rats were randomized into four treatment groups: Group A (Talc, 10 individuals); group B (control, 5 individuals); group C (hyperthermic isotonic saline, 10 individuals); and group D (filtrate air at 50°, 10 individuals). Inflammatory effect of hyperthermia was the primary outcome parameter. RESULTS: In the talc group, minimal adhesions between both pleural and peritoneal layers were observed in seven rats. Talc produced peritoneal mesothelium inflammation and fibrosis associated to foreign body giant cells in 80% (8/10) of the sample. Furthermore, clear evidence of a granulomatous foreign-body reaction was detected. No macroscopic and/or microscopic damage was registered in the remaining three groups (control, hyperthermic, and filtrate air). CONCLUSIONS: Talc is an excellent method for producing pleuro-peritoneal inflammatory lesions. On the contrary, hyperthermia apparently does not induce the macroscopic and microscopic damage that is required for efficient pleurodesis. Therefore, hyperthermia should not be used for pleurodesis procedures.

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find 'contour-like' objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ±â€¯8.5% and 97.5 ±â€¯6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases.

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.