Impact of Surgery after Endoscopically Resected High-Risk T1 Colorectal Cancer: Results of an Emulated Target Trial.

Felix Corre,Jeremie Albouys，Mathieu Pioche,Stanislas Chaussade

GASTROINTESTINAL ENDOSCOPY（2024）

Cited 1|Views34

Abstract

BACKGROUND AND AIMS:We aimed to compare the long-term outcomes of patients with high-risk T1 colorectal cancer (CRC) resected endoscopically who received either additional surgery or surveillance.METHODS:We used data from routine care to emulate a target trial aimed at comparing 2 strategies after endoscopic resection of high-risk T1 CRC: surgery with lymph node dissection (treatment group) versus surveillance alone (control group). All patients from 14 tertiary centers who underwent an endoscopic resection for high-risk T1 CRC between March 2012 and August 2019 were included. The primary outcome was a composite outcome of cancer recurrence or death at 48 months.RESULTS:Of 197 patients included in the analysis, 107 were categorized in the treatment group and 90 were categorized in the control group. From baseline to 48 months, 4 of 107 patients (3.7%) died in the treatment group and 6 of 90 patients (6.7%) died in the control group. Four of 107 patients (3.7%) in the treatment group experienced a cancer recurrence and 4 of 90 patients (4.4%) in the control group experienced a cancer recurrence. After balancing the baseline covariates by inverse probability of treatment weighting, we found no significant difference in the rate of death and cancer recurrence between patients in the 2 groups (weighted hazard ratio, .95; 95% confidence interval, .52-1.75).CONCLUSIONS:Our study suggests that patients with high-risk T1 CRC initially treated with endoscopic resection may not benefit from additional surgery.

Translated text

Key words

T1 colorectal cancer,superficial colorectal cancer,endoscopic mucosal resection,endoscopic submucosal dissection,additional surgery

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper