Ignite Creation Date:
2025-12-18 @ 8:28 AM
Ignite Modification Date:
2025-12-18 @ 8:28 AM
Study NCT ID:
NCT06606912
Status:
None
Last Update Posted:
2024-09-23 00:00:00
First Post:
2024-09-16 00:00:00
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Gastric Cancer Patients
Sponsor:
None
Organization:
Study Overview
Official Title:
Predictive Effect of Abdominal Fat and Muscle Area Calculated Based on Abdominal CT on Short- and Long-term Outcomes in Patients with Gastric Cancer
Status:
None
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This is a retrospective observational study, which is expected to include patients diagnosed with gastric cancer and undergoing radical gastric 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\'s Hospital, and to discuss the predictive effects of abdominal fat and muscle area on the short-term and long-term outcomes of gastric cancer patients after surgery.
1\. Case collection Patients diagnosed with gastric cancer and undergoing radical gastric cancer surgery were screened according to the inclusion criteria. All patients had signed informed consent.
Inclusion criteria:
1. Diagnosed with gastric 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.).
1\. Case collection Patients diagnosed with gastric cancer and undergoing radical gastric cancer surgery were screened according to the inclusion criteria. All patients had signed informed consent.
Inclusion criteria:
1. Diagnosed with gastric 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.).
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: