Gastric Cancer, Image, Pathology
Conditions
Keywords
Gastric Cancer, Neoadjuvant Chemotherapy, Radiomics, Treatment Outcome Prediction, Pathomics, Radiopathomics
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.
Interventions
DRUGNeoadjuvant Chemotherapy
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.
Sponsors
Peking University Cancer Hospital & Institute
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Yunnan Cancer Hospital
Henan Cancer Hospital
Zhenjiang First People's Hospital
First Hospital of China Medical University
Cancer Hospital of Guangxi Medical University
Peking University People's Hospital
Tianjin Medical University Cancer Institute and Hospital
The First Affiliated Hospital of Zhengzhou University
Nanfang Hospital, Southern Medical University
The Affiliated Hospital of Qingdao University
Ruijin Hospital
Sixth Affiliated Hospital, Sun Yat-sen University
Peking Union Medical College Hospital
Xiangya Hospital of Central South University
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
The First Affiliated Hospital of Soochow University
First Affiliated Hospital, Sun Yat-Sen University
Fujian Medical University Union Hospital
Fujian Cancer Hospital
San Raffaele University Hospital, Italy
Chinese Academy of Sciences
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| Overall Survival (OS) at 5 years | Five years | The duration from the date of patient confirmation to the date of death of the patient. |
Countries
China, Italy
Contacts
Primary ContactDi Dong, Ph.D.
Outcome results
None listed