Conceptio › Archive › ClinicalTrials.gov
ClinicalTrials.gov public full text Public Domain

Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Colorectal Cancer Patients

First Affiliated Hospital of Chongqing Medical University · 2012
ClinicalTrials.gov · Datasets · License: Public Domain · 2012
Open Source ↗
colorectal-cancersarcopeniasurgery
This study aims to create a clinical prediction model. Abdominal fat and muscle area also play an important role in the prediction of surgical outcomes in colorectal cancer. Studies have shown that excess visceral fat and low skeletal muscle mass (sarcopenia) are associated with poorer postoperative outcomes, including a higher risk of postoperative complications and lower survival. Preoperative imaging techniques such as CT, MRI and ultrasound that provide accurate measurements to assess abdominal fat and muscle area can help surgeons develop individualized surgical and rehabilitation plans, improve surgical success, reduce complications and improve long-term patient prognosis. In this study, the investigators expected to construct a prediction model of abdominal fat and muscle area on the short- and long-term outcomes of colorectal cancer patients by calculating the abdominal fat and muscle area in different levels of abdominal CT images, in order to further adjust and guide the treatment plan.
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 858119
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive