AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
- Conditions
- Gastric CancerImagePathology
- Interventions
- Drug: Neoadjuvant Chemotherapy
- Registration Number
- NCT06035250
- Lead Sponsor
- Chinese Academy of Sciences
- 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.
- Detailed Description
This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants.
Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset.
Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.
Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.
Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.
TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.
Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 200
- 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.
- Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
- Patients diagnosed with other concurrent tumors.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy 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.
- Primary Outcome Measures
Name Time Method Accuracy of TRG prediction by the AI model two months 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.
Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model two months 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.
- Secondary Outcome Measures
Name Time Method Overall Survival (OS) at 5 years Five years The duration from the date of patient confirmation to the date of death of the patient.
Progression-Free Survival (PFS) at 3 years Three years The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.
Trial Locations
- Locations (22)
First Affiliated Hospital, Sun Yat-Sen University
🇨🇳Guangzhou, China
First Hospital of China Medical University
🇨🇳Shenyang, China
San Raffaele University Hospital, Italy
🇮🇹Milan, Italy
Nanfang Hospital of Southern Medical University
🇨🇳Guangzhou, China
The First Affiliated Hospital of Soochow University
🇨🇳Suzhou, China
Tianjin Medical University Cancer Institute and Hospital
🇨🇳Tianjin, China
Zhenjiang First People's Hospital
🇨🇳Zhenjiang, China
Yunnan Cancer Hospital
🇨🇳Kunming, China
The Affiliated Hospital of Qingdao University
🇨🇳Qingdao, China
Ruijin Hospital
🇨🇳Shanghai, China
The First Affiliated Hospital of Zhengzhou University
🇨🇳Zhengzhou, China
Fujian Medical University Union Hospital
🇨🇳Fuzhou, China
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
🇨🇳Guangzhou, China
Henan Cancer Hospital
🇨🇳Zhengzhou, China
Peking University People's Hospital
🇨🇳Beijing, China
Xiangya Hospital of Central South University
🇨🇳Changsha, China
Sixth Affiliated Hospital, Sun Yat-sen University
🇨🇳Guangzhou, China
Cancer Hospital of Guangxi Medical University
🇨🇳Nanning, China
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
Fujian Cancer Hospital
🇨🇳Fuzhou, China