AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

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.

Study Overview

• Status: Recruiting
• Conditions: Gastric Cancer; Image; Pathology
• Intervention / Treatment: Drug: Neoadjuvant Chemotherapy

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.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Di Dong, Ph.D.; Phone Number: +86 13811833760; Email: di.dong@ia.ac.cn

Study Locations

• China
  • Beijing, China
    • Not yet recruiting
    • Peking University People's Hospital
    • Contact: yi wang
  • Beijing, China
    • Not yet recruiting
    • Peking Union Medical College Hospital
    • Contact: Zhenyu Jin
  • Beijing, China
    • Recruiting
    • Peking University Cancer Hospital & Institute
    • Contact: Lei Tang
  • Beijing, China
    • Not yet recruiting
    • Cancer Institute and Hospital, Chinese Academy of Medical Sciences
    • Contact: Xinming Zhao
  • Changsha, China
    • Not yet recruiting
    • Xiangya Hospital of Central South University
    • Contact: Weihua Liao
  • Fuzhou, China
    • Not yet recruiting
    • Fujian Cancer Hospital
    • Contact: Yangming Li
  • Fuzhou, China
    • Recruiting
    • Fujian Medical University Union Hospital
    • Contact: Changming Huang
  • Guangzhou, China
    • Not yet recruiting
    • Nanfang Hospital of Southern Medical University
    • Contact: Guoxin Li
  • Guangzhou, China
    • Not yet recruiting
    • Affiliated Cancer Hospital & Institute of Guangzhou Medical University
    • Contact: Shuzhong Cui
  • Guangzhou, China
    • Not yet recruiting
    • First Affiliated Hospital, Sun Yat-Sen University
    • Contact: Shenping Yu
  • Guangzhou, China
    • Recruiting
    • Sixth Affiliated Hospital, Sun Yat-sen University
    • Contact: Xiaochun Meng
  • Kunming, China
    • Recruiting
    • Yunnan Cancer Hospital
    • Contact: Zhenhui Li
  • Nanning, China
    • Not yet recruiting
    • Cancer Hospital of Guangxi Medical University
    • Contact: Guanqiao Jin
  • Qingdao, China
    • Not yet recruiting
    • The Affiliated Hospital of Qingdao University
    • Contact: Hexiang Wang
  • Shanghai, China
    • Not yet recruiting
    • Ruijin Hospital
    • Contact: Jun Zhang
  • Shenyang, China
    • Not yet recruiting
    • First Hospital of China Medical University
    • Contact: Zhenning WANG
  • Suzhou, China
    • Not yet recruiting
    • The First Affiliated Hospital of Soochow University
    • Contact: Jie Bao
  • Tianjin, China
    • Not yet recruiting
    • Tianjin Medical University Cancer Institute and Hospital
    • Contact: Zhaoxiang Ye
  • Zhengzhou, China
    • Recruiting
    • Henan Cancer Hospital
    • Contact: Jing Li
  • Zhengzhou, China
    • Recruiting
    • The First Affiliated Hospital of Zhengzhou University
    • Contact: Jianbo Gao
  • Zhenjiang, China
    • Recruiting
    • Zhenjiang First People's Hospital
    • Contact: Xiuhong Shan
• Italy
  • Milan, Italy
    • Recruiting
    • San Raffaele University Hospital, Italy
    • Contact: Francesco De Cobelli

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Gastric Cancer Patients Undergoing 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.

Drug: Neoadjuvant 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
Overall Survival (OS) at 5 years	The duration from the date of patient confirmation to the date of death of the patient.	Five years

Collaborators and Investigators

• Sponsor: Chinese Academy of Sciences
• Collaborators: Xiangya Hospital of Central South University; Peking University People's Hospital; Peking Union Medical College Hospital; Cancer Institute and Hospital, Chinese Academy of Medical Sciences; Fujian Cancer Hospital; First Affiliated Hospital, Sun Yat-Sen University; Henan Cancer Hospital; Peking University Cancer Hospital & Institute; The Affiliated Hospital of Qingdao University; Ruijin Hospital; Tianjin Medical University Cancer Institute and Hospital; The First Affiliated Hospital of Soochow University; The First Affiliated Hospital of Zhengzhou University; Sixth Affiliated Hospital, Sun Yat-sen University; Fujian Medical University Union Hospital; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Zhenjiang First People's Hospital; First Hospital of China Medical University; Yunnan Cancer Hospital; Cancer Hospital of Guangxi Medical University; San Raffaele University Hospital, Italy; Nanfang Hospital, Southern Medical University
• Investigators: Study Director: Yali Zang, Ph.D., Institute of Automation, Chinese Academy of Sciences

Study record dates

Study Major Dates

• Study Start (Actual): September 10, 2023
• Primary Completion (Estimated): August 31, 2024
• Study Completion (Estimated): December 31, 2029

Study Registration Dates

• First Submitted: August 13, 2023
• First Submitted That Met QC Criteria: September 6, 2023
• First Posted (Actual): September 13, 2023

Study Record Updates

• Last Update Posted (Actual): September 28, 2023
• Last Update Submitted That Met QC Criteria: September 26, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: Gastric Cancer; Neoadjuvant Chemotherapy; Radiomics; Treatment Outcome Prediction; Pathomics; Radiopathomics
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Stomach Neoplasms
• Other Study ID Numbers: CASMI004

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
• IPD Sharing Time Frame: Data will become available 1 year after study completion and will remain available for a period of 5 years.
• IPD Sharing Access Criteria: Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No