AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
1 other identifier
observational
200
2 countries
22
Brief Summary
This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Sep 2023
Longer than P75 for all trials
22 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 13, 2023
CompletedStudy Start
First participant enrolled
September 10, 2023
CompletedFirst Posted
Study publicly available on registry
September 13, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 31, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2029
ExpectedSeptember 28, 2023
September 1, 2023
12 months
August 13, 2023
September 26, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model
The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.
two months
Accuracy of TRG prediction by the AI model
Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.
two months
Secondary Outcomes (2)
Progression-Free Survival (PFS) at 3 years
Three years
Overall Survival (OS) at 5 years
Five years
Study Arms (1)
Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy
This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen.
Interventions
Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.
Eligibility Criteria
The study population comprises gastric cancer patients from various hospitals. Participants are individuals diagnosed with advanced gastric cancer and are currently undergoing neoadjuvant chemotherapy treatments. Selection is based on criteria such as age, specific diagnosis, past treatment history, and the clarity of their medical images and pathology images.
You may qualify if:
- Age 18 years or older;
- Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
- Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
- Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
- CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
- Patients possess comprehensive preoperative clinical information and post-operative TRG grading.
You may not qualify if:
- Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
- Patients diagnosed with other concurrent tumors.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Chinese Academy of Scienceslead
- Peking University Cancer Hospital & Institutecollaborator
- Cancer Institute and Hospital, Chinese Academy of Medical Sciencescollaborator
- Yunnan Cancer Hospitalcollaborator
- Henan Cancer Hospitalcollaborator
- Zhenjiang First People's Hospitalcollaborator
- First Hospital of China Medical Universitycollaborator
- Cancer Hospital of Guangxi Medical Universitycollaborator
- Peking University People's Hospitalcollaborator
- Tianjin Medical University Cancer Institute and Hospitalcollaborator
- The First Affiliated Hospital of Zhengzhou Universitycollaborator
- Nanfang Hospital, Southern Medical Universitycollaborator
- The Affiliated Hospital of Qingdao Universitycollaborator
- Ruijin Hospitalcollaborator
- Sixth Affiliated Hospital, Sun Yat-sen Universitycollaborator
- Peking Union Medical College Hospitalcollaborator
- Xiangya Hospital of Central South Universitycollaborator
- Affiliated Cancer Hospital & Institute of Guangzhou Medical Universitycollaborator
- The First Affiliated Hospital of Soochow Universitycollaborator
- First Affiliated Hospital, Sun Yat-Sen Universitycollaborator
- Fujian Medical University Union Hospitalcollaborator
- Fujian Cancer Hospitalcollaborator
- San Raffaele University Hospital, Italycollaborator
Study Sites (22)
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Beijing, China
Peking Union Medical College Hospital
Beijing, China
Peking University Cancer Hospital & Institute
Beijing, China
Peking University People's Hospital
Beijing, China
Xiangya Hospital of Central South University
Changsha, China
Fujian Cancer Hospital
Fuzhou, China
Fujian Medical University Union Hospital
Fuzhou, China
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Guangzhou, China
First Affiliated Hospital, Sun Yat-Sen University
Guangzhou, China
Nanfang Hospital of Southern Medical University
Guangzhou, China
Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, China
Yunnan Cancer Hospital
Kunming, China
Cancer Hospital of Guangxi Medical University
Nanning, China
The Affiliated Hospital of Qingdao University
Qingdao, China
Ruijin Hospital
Shanghai, China
First Hospital of China Medical University
Shenyang, China
The First Affiliated Hospital of Soochow University
Suzhou, China
Tianjin Medical University Cancer Institute and Hospital
Tianjin, China
Henan Cancer Hospital
Zhengzhou, China
The First Affiliated Hospital of Zhengzhou University
Zhengzhou, China
Zhenjiang First People's Hospital
Zhenjiang, China
San Raffaele University Hospital, Italy
Milan, Italy
Biospecimen
The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and Eosin) staining for pathology imaging.
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Yali Zang, Ph.D.
Institute of Automation, Chinese Academy of Sciences
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER GOV
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
August 13, 2023
First Posted
September 13, 2023
Study Start
September 10, 2023
Primary Completion
August 31, 2024
Study Completion (Estimated)
December 31, 2029
Last Updated
September 28, 2023
Record last verified: 2023-09
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, ANALYTIC CODE
- Time Frame
- Data will become available 1 year after study completion and will remain available for a period of 5 years.
- Access Criteria
- Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.
Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.