Gastric Cancer (Diagnosis), Postoperative Complications
Conditions
Brief summary
Gastric cancer is a leading cause of cancer-related mortality, and radical surgery remains the primary treatment. However, postoperative complications are common and can significantly impact patient recovery and quality of life. Currently, doctors lack precise tools to accurately predict which patients are at high risk for developing severe complications before surgery. This study aims to validate a novel artificial intelligence (AI) model called "DeepComp." The DeepComp model integrates clinical data with advanced radiomic features derived from routine preoperative CT scans. Specifically, it analyzes both the tumor characteristics and the patient's body composition (including skeletal muscle and fat distribution) to assess physiological reserve. In this prospective, multicenter observational study, researchers will enroll patients scheduled for gastric cancer surgery across five medical centers. The DeepComp model will be used to predict the risk of moderate-to-severe postoperative complications (Clavien-Dindo grade II or higher). These predictions will then be compared with the actual clinical outcomes observed 30 days after surgery. The goal is to determine the accuracy and reliability of the DeepComp model in a real-world clinical setting, potentially providing a powerful tool for personalized surgical risk assessment.
Interventions
None listed
Sponsors
Qun Zhao
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
Age ≥ 18 years. Histologically confirmed gastric adenocarcinoma. Scheduled for elective radical gastrectomy (open, laparoscopic, or robotic) with curative intent. Standard preoperative contrast-enhanced abdominal CT scans (venous phase) performed within 14 days prior to surgery. Willingness to sign informed consent.
Exclusion criteria
Emergency surgery due to perforation, obstruction, or massive bleeding. Intraoperative findings of distant metastasis (Stage IV) or unresectable disease preventing R0 resection. Concurrent or previous malignant tumors within the last 5 years (except gastric cancer). Pregnancy or lactation. Severe metallic artifacts on CT images preventing radiomic analysis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II) | Postoperative 30 days | Postoperative complications will be graded according to the Clavien-Dindo classification system. Major complications are defined as Grade II or higher, which require pharmacological treatment, surgical/endoscopic/radiological intervention, or life-threatening complications (including death). The occurrence of these events will be recorded and compared with the model's preoperative predictions. |
| Human-AI Collaborative Diagnostic Performance in Gastric Cancer Surgery: Accuracy and Observer Agreement | From preoperative assessment through 30 days post-surgery | In a subset of 120 randomly selected gastric cancer surgery patients, ten surgeons of varying experience levels (Junior \<5 years, n=4; Intermediate 5-10 years, n=3; Senior ≥10 years, n=3) will first independently assess postoperative complication risk using blinded preoperative data. Subsequently, they will receive predictions from the DeepComp AI model and update their assessments. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Performance of the DeepComp Model (AUC) | Postoperative 30 days | The discrimination performance of the DeepComp model in predicting major postoperative complications will be evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC). Sensitivity, specificity, positive predictive value, and negative predictive value will also be calculated. |
| Length of Hospital Stay | Up to 30 days | Defined as the number of days from surgery to discharge. |
Countries
China
Contacts
CONTACTPing'an Ding, PhD
CONTACTQun Zhao, PhD
PRINCIPAL_INVESTIGATORQun Zhao
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Outcome results
None listed