AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Gastric Cancer Clinical Trial

Official title: Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Status Recruiting

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Gastric Cancer
• Image
• Pathology
• Stomach Neoplasms

• NCT number: NCT06035250
• Study type: Observational
• Source: Chinese Academy of Sciences
• Contact: Di Dong, Ph.D.
• Phone: +86 13811833760
• Email: di.dong@ia.ac.cn
• Status: Recruiting
• Start date: September 10, 2023
• Completion date: December 31, 2029