DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-85
SponsorQun Zhao
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
None
Trial population
Adult patients diagnosed with potentially resectable gastric cancer who are admitted to the participating centers for surgical treatment.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
500 Participants
are grouped into 1 trial groupGroup A: Gastric Cancer Surgery Cohort
Trial outcomes
Primary outcomes
1
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 days2
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-surgerySecondary outcomes
1
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 days2
Length of Hospital Stay
Defined as the number of days from surgery to discharge.
Time frame
Up to 30 daysSponsors and contacts
Click on the lead sponsor to view all of their trials.
Qun Zhao
Lead sponsor
Hebei Medical University
Sponsor institution
Source ClinicalTrials.gov