Actively Recruiting
Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
Led by Sixth Affiliated Hospital, Sun Yat-sen University · Updated on 2024-06-11
500
Participants Needed
1
Research Sites
725 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
This study aims to develop a multimodal model combining radiomic and pathomic features to predict pathological complete response (pCR) in advanced gastric cancer patients undergoing neoadjuvant chemotherapy (NAC). The researchers intended to collected pre-intervention CT images and pathological slides from patients, extract radiomic and pathomic features, and build a prediction model using machine learning algorithms. The model will be validated using a separate cohort of patients. This research intend to build a radiomic-pathomic model that can outperform models based on either radiomic or pathomic features alone, aiming to improve the prediction of pCR in gastric cancer.
CONDITIONS
Official Title
Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received neoadjuvant chemotherapy and radical gastrectomy
- Patients who underwent abdominal multidetector computed tomography (CT) inspection, gastroscope, and tumor tissue biopsy before any intervention started
- Lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1
You will not qualify if you...
- Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection
- Patients without distinguishable tumor cells on the pathological slides due to inadequate sampling
- Patients with insufficient data
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
The Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China, 510655
Actively Recruiting
Research Team
Y
Yonghe Chen, MD
CONTACT
J
Junsheng Peng, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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