Total neoadjuvant therapy in rectal cancer: a network meta-analysis of randomized trials.

Purpose: To assess the efficacy of total neoadjuvant therapy (TNT) for rectal carcinoma in comparison with conventional chemoradiotherapy (CRT).

Methods: A systematic review was performed according to the PRISMA guidelines. A Bayesian network meta-analysis was done using NetMetaXL and WinBUGS.

Intraoperative fluorescence angiography as an independent factor of anastomotic leakage and a nomogram for predicting leak for colorectal anastomoses.

Purpose: Colorectal anastomotic leakage (AL) is a life-threatening complication, which increases morbidity, hospital stay and cost of treatment. The aim of this study is to identify risk factors, including intraoperative indocyanine green fluorescence angiography (ICG FA), associated with the leak of stapled colorectal anastomosis.

Methods: Four hundred twenty-nine consecutive patients underwent surgery between 2017 and 2019 for benign (n=10, 2.

Intraoperative use of fluorescence with indocyanine green reduces anastomotic leak rates in rectal cancer surgery: an individual participant data analysis.

Background: Fluorescence imaging by means of Indocyanine green (ICG) has been applied to intraoperatively determine the perfusion of the anastomosis. The purpose of this Individual Participant Database meta-analysis was to assess the effectiveness in decreasing the incidence of anastomotic leak (AL) after rectal cancer surgery.

Methods: We searched PubMed, Embase, Cochrane Library and ClinicalTrial.

Genome-scale analysis of DNA methylation in colorectal cancer using Infinium HumanMethylation450 BeadChips.

Illumina's Infinium HumanMethylation450 BeadChip arrays were used to examine genome-wide DNA methylation profiles in 22 sample pairs from colorectal cancer (CRC) and adjacent tissues and 19 colon tissue samples from cancer-free donors. We show that the methylation profiles of tumors and healthy tissue samples can be clearly distinguished from one another and that the main source of methylation variability is associated with disease status. We used different statistical approaches to evaluate the methylation data.