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Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Short- and Long-term Outcomes in Patients with Colorectal Cancer
Authors not listed
First Affiliated Hospital of Chongqing Medical University · 2012
Abstract
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.
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APA
Anonymous. (2012). Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Short- and Long-term Outcomes in Patients with Colorectal Cancer. <em>First Affiliated Hospital of Chongqing Medical University</em>. https://clinicaltrials.gov/study/NCT06614699
Vancouver
Anonymous. Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Short- and Long-term Outcomes in Patients with Colorectal Cancer. First Affiliated Hospital of Chongqing Medical University. 2012.
BibTeX
@misc{anon2012Predic,
title = {Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Short- and Long-term Outcomes in Patients with Colorectal Cancer},
author = {Anonymous},
journal = {First Affiliated Hospital of Chongqing Medical University},
year = {2012},
}
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