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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06035250
• Other study ID #: CASMI004
• Status: Recruiting
• Start date: September 10, 2023
• Est. completion date: December 31, 2029

Study information

• Verified date: September 2023
• Source: Chinese Academy of Sciences
• Contact: Di Dong, Ph.D.
• Phone: +86 13811833760
• Email: di.dong[@]ia.ac.cn
• Is FDA regulated: No
• Study type: Observational

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.

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.

Recruitment information / eligibility

• 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.

Related Conditions & MeSH terms

• Gastric Cancer
• Image
• Pathology
• Stomach Neoplasms

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.

Locations

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

Sponsors (23)

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

Countries where clinical trial is conducted

China,  Italy, 

Outcome

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