Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study.
Li Yuan, Lin Yang, Shichuan Zhang...
https://pubmed.ncbi.nlm.nih.gov/36825238Actively Recruiting
Age: 18Years - 75Years
All Genders
ID06642857
Predicting Treatment Response to Immunotherapy Combined with Chemotherapy in Advanced Gastric/Gastroesophageal Junction Cancer Based on Multi-omics Information During Tumor Evolution
Led by Xiangdong Cheng · Updated on 2024-10-15
150
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are studying advanced gastric and gastroesophageal junction cancer in patients who are undergoing immunotherapy combined with chemotherapy. This project focuses on collecting and analyzing detailed multi-omics data to find key features linked to how well immunotherapy works. The study aims to identify potential molecular markers and dominant microbiota that could predict treatment effects and help improve therapy decisions for these advanced cancers. Participants will receive chemotherapy combined with immunotherapy as part of their treatment. Samples such as peripheral blood, tongue coating, saliva, and feces will be collected along with clinical data from these patients. This observational study will monitor patients over time to develop and validate a model that predicts response to the combined therapy. During the study, researchers will assess tumor response and overall survival over periods up to 60 months. Progression-free survival will also be evaluated. Patients will undergo regular evaluations to track treatment effects and disease progression. The study will provide insights to help predict which patients may benefit most from immunotherapy combined with chemotherapy, supporting optimized treatment planning.
CONDITIONS
Brief Title
Multi-omics Based Prediction of Treatment Response to Immunotherapy Combined with Chemotherapy in Advanced Gastric/Gastroesophageal Junction Cancer.
Who Can Participate
Age: 18Years - 75Years
All Genders
Eligibility Criteria
You may qualify if you...
- Patients with gastric or gastroesophageal junction adenocarcinoma confirmed by pathology and with advanced or metastatic disease that cannot be surgically removed
- HER2 negative
- Have not received any prior anti-tumor treatment
- Planned treatment includes chemotherapy combined with immunotherapy
- Aged between 18 and 75 years, any gender
- Expected survival time of at least 3 months
You will not qualify if you...
- Presence of malignant tumors other than gastric cancer or tumors that have spread to the stomach from other sites
- Previous treatments such as surgery, radiotherapy, chemotherapy, targeted therapy, or immunotherapy
- Severe infections
- History of mental illness preventing cooperation with the research
- Severe heart, liver, kidney, or other serious diseases
- Pregnant or breastfeeding
- HER2 positive
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
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Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Monitoring
Duration - Up to 60 months
Participants with advanced gastric cancer receiving chemotherapy combined with immunotherapy have blood, tongue coating, saliva, and feces samples collected along with clinical data to observe treatment response.
Periodic sample collections and clinical assessments during treatment and follow-up
Trial Site Locations
Total: 1 location
1
Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)
Hangzhou, Zhejiang, China, 310000
Actively Recruiting
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Research Team
X
Xiangdong Cheng Cheng, PhD
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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Published Research Related To This Trial
Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis.
Meng-Yi Li, Ding-Ju Zhu, Wen Xu...
https://pubmed.ncbi.nlm.nih.gov/34840700Cancer statistics, 2023.
Rebecca L Siegel, Kimberly D Miller, Nikita Sandeep Wagle...
https://pubmed.ncbi.nlm.nih.gov/36633525