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.

This is a retrospective observational study, which is expected to include patients diagnosed with colorectal cancer and undergoing radical colorectal cancer surgery in the Department of Gastrointestinal Surgery of the First Affiliated Hospital of Chongqing Medical University, the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Chongqing Medical University, the Department of Gastrointestinal Surgery of the Affiliated Yongchuan Hospital of Chongqing Medical University, and the Department of Gastrointestinal Surgery of the Qijiang People Hospital, and to discuss the predictive effects of abdominal fat and muscle area on the short-term and long-term outcomes of colorectal cancer patients after surgery.

1. Case collection Patients diagnosed with colorectal cancer and undergoing radical colorectal cancer surgery were screened according to the inclusion criteria. All patients had signed informed consent. Inclusion criteria: (1) Diagnosed with colorectal cancer by pathology or cytology; (2) Age >18 years; (3) Not having received chemotherapy, radiotherapy, targeted therapy or immunotherapy; (4) Patients with postoperative pathological stages other than stage IV, or without metastases in liver, lung or other distant organs confirmed by CT, MRI or B-ultrasound imaging and without surgical treatment; (5) Pre-operative CT examination data were kept in our hospital.
2. Data collection Collect patients' preoperative baseline information such as gender, age, body mass index (BMI), complications, tumor stage, etc. Collect patients' preoperative CT scans (make sure to include images from lumbar 1 to lumbar 5). Collect patients' surgical conditions such as operation time and intraoperative bleeding. Collect patients' postoperative complications during hospitalization;
3. Prognostic follow-up Closely follow up the death or cancer recurrence of patients after surgery.
4. Outcome indicators (1) Primary outcome indicators: overall survival (OS), disease-free survival (DFS), postoperative complications. (2) Secondary outcome indicators: postoperative recovery time, hospitalization time, postoperative weight change.
5. Image processing 3D Slicer was used to outline the range of subcutaneous fat, visceral fat, and muscle at the level of waist 1 to waist 5 on enhanced CT (5.0mm) images. Export the segmentation result as gpj.format, and select the Area Reading Calculator to calculate the area of fat and muscle at each level. Calculate fat area and muscle area at each level, calculate overall abdominal fat area and muscle area, and use the ratio of fat area to muscle area (e.g., visceral fat/skeletal muscle) as a predictor to generate the desired quantitative analyses.
6. Statistical Analysis (1) Basic characteristics and imaging indices were described as mean and standard deviation or median and standard deviation. (2) Univariate analysis including Kaplan-Meier survival analysis and Log-rank test were used to evaluate the effects of fat and muscle area on postoperative survival and complications. (3) Multivariate analysis including Cox regression models or Logistic regression models were used to evaluate the independent predictive role of abdominal fat and muscle area on surgical outcomes, controlling for potential confounders (e.g., age, gender, BMI, tumor stage, etc.).

Inclusion Criteria:

• 1, Diagnosis of colorectal cancer confirmed by pathology or cytology;
• 2, aged >18 years;
• 3, not received chemotherapy, radiotherapy, targeted therapy or immunotherapy;
• 4, post-operative pathological stages other than stage IV, or no liver, lung or other organs as confirmed by CT, MRI, B-ultrasound imaging.
• 5, patients who have pre-operative CT examination data kept in our hospital.

Exclusion Criteria:

• 1, Poor quality of preoperative CT images;
• 2, refusal to participate in this study.

All adult patients diagnosed with colorectal cancer and undergoing radical surgery for colorectal cancer at the study centre.

IPD is not allowed sharing in this three clinic centers.