AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorChinese Academy of Sciences
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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;
Disqualifiers
Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
Patients diagnosed with other concurrent tumors.
Trial population
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.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Neoadjuvant Chemotherapy
DrugParticipants 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.
Treatment groups
200 Participants
are divided into 1 treatment groupGroup A: Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
two months2
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.
Time frame
two monthsSecondary outcomes
1
Progression-Free Survival (PFS) at 3 years
The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.
Time frame
Three years2
Overall Survival (OS) at 5 years
The duration from the date of patient confirmation to the date of death of the patient.
Time frame
Five yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
Chinese Academy of Sciences
Lead sponsor
Institute of Automation, Chinese Academy of Sciences
Sponsor institution
Peking University Cancer Hospital & Institute
Collaborator
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Collaborator
Yunnan Cancer Hospital
Collaborator
Henan Cancer Hospital
Collaborator
Zhenjiang First People's Hospital
Collaborator
First Hospital of China Medical University
Collaborator
Cancer Hospital of Guangxi Medical University
Collaborator
Peking University People's Hospital
Collaborator
Tianjin Medical University Cancer Institute and Hospital
Collaborator
The First Affiliated Hospital of Zhengzhou University
Collaborator
Nanfang Hospital, Southern Medical University
Collaborator
The Affiliated Hospital of Qingdao University
Collaborator
Ruijin Hospital
Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University
Collaborator
Peking Union Medical College Hospital
Collaborator
Xiangya Hospital of Central South University
Collaborator
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Collaborator
The First Affiliated Hospital of Soochow University
Collaborator
First Affiliated Hospital, Sun Yat-Sen University
Collaborator
Fujian Medical University Union Hospital
Collaborator
Fujian Cancer Hospital
Collaborator
San Raffaele University Hospital, Italy
Collaborator
Source ClinicalTrials.gov