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AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT06035250
Recruitment Status : Recruiting
First Posted : September 13, 2023
Last Update Posted : September 28, 2023
Sponsor:
Collaborators:
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
Information provided by (Responsible Party):
Di Dong, Institute of Automation, Chinese Academy of Sciences

Tracking Information
First Submitted Date August 13, 2023
First Posted Date September 13, 2023
Last Update Posted Date September 28, 2023
Actual Study Start Date September 10, 2023
Estimated Primary Completion Date August 31, 2024   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: September 6, 2023)
  • Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model [ Time Frame: 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 [ Time Frame: 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.
Original Primary Outcome Measures Same as current
Change History
Current Secondary Outcome Measures
 (submitted: September 6, 2023)
  • Progression-Free Survival (PFS) at 3 years [ Time Frame: 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 [ Time Frame: Five years ]
    The duration from the date of patient confirmation to the date of death of the patient.
Original Secondary Outcome Measures Same as current
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Official Title Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
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.
Study Type Observational
Study Design Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration Not Provided
Biospecimen Retention:   Samples With DNA
Description:
The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and Eosin) staining for pathology imaging.
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.
Condition
  • Gastric Cancer
  • Image
  • Pathology
Intervention 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.
Study Groups/Cohorts 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.
Intervention: Drug: Neoadjuvant Chemotherapy
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: September 6, 2023)		 200
Original Estimated Enrollment Same as current
Estimated Study Completion Date December 31, 2029
Estimated Primary Completion Date August 31, 2024   (Final data collection date for primary outcome measure)
Eligibility Criteria

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.
Sex/Gender
Sexes Eligible for Study: All
Ages 18 Years and older   (Adult, Older Adult)
Accepts Healthy Volunteers No
Contacts
Contact: Di Dong, Ph.D. +86 13811833760 di.dong@ia.ac.cn
Listed Location Countries China,   Italy
Removed Location Countries  
 
Administrative Information
NCT Number NCT06035250
Other Study ID Numbers CASMI004
Has Data Monitoring Committee Yes
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: Yes
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.
Supporting Materials: Study Protocol
Supporting Materials: Statistical Analysis Plan (SAP)
Supporting Materials: Analytic Code
Time Frame: Data will become available 1 year after study completion and will remain available for a period of 5 years.
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.
URL: http://www.radiomics.net.cn/
Current Responsible Party Di Dong, Institute of Automation, Chinese Academy of Sciences
Original Responsible Party Same as current
Current Study Sponsor Chinese Academy of Sciences
Original Study Sponsor Same as current
Collaborators
  • 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
Investigators
Study Director: Yali Zang, Ph.D. Institute of Automation, Chinese Academy of Sciences
PRS Account Chinese Academy of Sciences
Verification Date September 2023