Search

Background and objective The incidence of post-surgical complications (PSCs) after curative resection of non-metastatic colorectal cancer (CRC) is very widespread and evident. Some studies suggest that PSCs often predict poor long-term oncological outcomes. However, the available data on the topic is often controversial. The aim of this systematic review and meta-analysis was to study the effect of postoperative complications (POCs) on long-term oncological outcomes following curative resection of non-metastatic (stage I-III) CRC. Methods A comprehensive search of MEDLINE® and Excerpta Medica dataBASE (EMBASE) databases was performed via the Ovid platform, by using controlled vocabulary as well as natural language terms for POCs, outcomes, and CRC. Two authors independently screened the studies and extracted data. Conflicts were resolved by discussion among authors and also independently with the help of a third author. Meta-analysis was performed using a random-effects model (REM) to calculate pooled estimates for overall survival (OS), disease-free survival (DFS), and overall recurrence. Results Overall, 3,836 studies were initially screened, and 16 studies involving 37,192 patients were ultimately selected for final inclusion in the analysis. Meta-analysis of these studies showed that PSCs following non-metastatic CRC surgery predicted worse OS rates [hazard ratio (HR): 1.36; 95% CI: 1.15-1.61; p

This paper presents an approach for age estimation based on faces through classifying facial images into predefined age-groups. However, a task such as the one at hand faces several difficulties because of the different aspects of every single person. Factors like exposure, weather, gender and lifestyle all come into play. While some trends are similar for faces from a similar age group, it is problematic to distinguish the aging aspects for every age group. This paper’s concentration is in four chosen age groups where the estimation takes place. We employed a fast and effective machine learning method: deep learning so that it could solve the age categorization issue. Principal component analysis (PCA) was used for extracting features and reducing face image. Age estimation was applied to three different aging datasets from Morph and experimental results are reported to validate its efficiency and robustness. Eventually, it is evident from the results that the current approach has achieved high classification results compared with support vector machine (SVM) and k-nearest neighbors (K-NN).