Gastric Cancer Clinical Trial
Official title:
Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
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.
Status | Recruiting |
Enrollment | 200 |
Est. completion date | December 31, 2029 |
Est. primary completion date | August 31, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - 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. Exclusion Criteria: - Patients whose CT or pathology images are unclear, making lesion assessment infeasible; - Patients diagnosed with other concurrent tumors. |
Country | Name | City | State |
---|---|---|---|
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 | 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 | |
Italy | San Raffaele University Hospital, Italy | Milan |
Lead Sponsor | Collaborator |
---|---|
Chinese Academy of Sciences | Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Cancer Hospital of Guangxi Medical University, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, First Affiliated Hospital, Sun Yat-Sen University, First Hospital of China Medical University, Fujian Cancer Hospital, Fujian Medical University Union Hospital, Henan Cancer Hospital, Nanfang Hospital, Southern Medical University, Peking Union Medical College Hospital, Peking University Cancer Hospital & Institute, Peking University People's Hospital, Ruijin Hospital, San Raffaele University Hospital, Italy, Sixth Affiliated Hospital, Sun Yat-sen University, The Affiliated Hospital of Qingdao University, The First Affiliated Hospital of Soochow University, The First Affiliated Hospital of Zhengzhou University, Tianjin Medical University Cancer Institute and Hospital, Xiangya Hospital of Central South University, Yunnan Cancer Hospital, Zhenjiang First People's Hospital |
China, Italy,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | |
Primary | 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 | 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. | Three years | |
Secondary | Overall Survival (OS) at 5 years | The duration from the date of patient confirmation to the date of death of the patient. | Five years |
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