DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)

Qun Zhao500 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

500

Conditions

Gastric Cancer (Diagnosis)Postoperative Complications

Eligibility

Sex: ALLMin age: 18 YearsMax age: 85 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II)

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.

Time frame: Postoperative 30 days

Human-AI Collaborative Diagnostic Performance in Gastric Cancer Surgery: Accuracy and Observer Agreement

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.

Time frame: From preoperative assessment through 30 days post-surgery

Secondary Outcomes

Predictive Performance of the DeepComp Model (AUC)

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.

Time frame: Postoperative 30 days

Length of Hospital Stay

Defined as the number of days from surgery to discharge.

Time frame: Up to 30 days