AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Sponsor

Chinese Academy of Sciences (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT06035250

Collaborator

Peking University Cancer Hospital & Institute (Other), Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Other), Yunnan Cancer Hospital (Other), Henan Cancer Hospital (Other), Zhenjiang First People's Hospital (Other), First Hospital of China Medical University (Other), Cancer Hospital of Guangxi Medical University (Other), Peking University People's Hospital (Other), Tianjin Medical University Cancer Institute and Hospital (Other), The First Affiliated Hospital of Zhengzhou University (Other), Nanfang Hospital, Southern Medical University (Other), The Affiliated Hospital of Qingdao University (Other), Ruijin Hospital (Other), Sixth Affiliated Hospital, Sun Yat-sen University (Other), Peking Union Medical College Hospital (Other), Xiangya Hospital of Central South University (Other), Affiliated Cancer Hospital & Institute of Guangzhou Medical University (Other), The First Affiliated Hospital of Soochow University (Other), First Affiliated Hospital, Sun Yat-Sen University (Other), Fujian Medical University Union Hospital (Other), Fujian Cancer Hospital (Other), San Raffaele University Hospital, Italy (Other)

200

Enrollment

Locations

75.7

Anticipated Duration (Months)

9.1

Patients Per Site

0.1

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Gastric Cancer Image Pathology	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 Design

Study Type:

Observational

Anticipated Enrollment :

200 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Anticipated Study Start Date :

Sep 10, 2023

Anticipated Primary Completion Date :

Aug 31, 2024

Anticipated Study Completion Date :

Dec 31, 2029

Arms and Interventions

Arm	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.

Arm

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.

Outcome Measures

Primary Outcome Measures

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 Outcome Measures

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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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.

Contacts and Locations

Locations

	Site	City	Country
1	Cancer Institute and Hospital, Chinese Academy of Medical Sciences	Beijing	China
2	Peking Union Medical College Hospital	Beijing	China
3	Peking University Cancer Hospital & Institute	Beijing	China
4	Peking University People's Hospital	Beijing	China
5	Xiangya Hospital of Central South University	Changsha	China
6	Fujian Cancer Hospital	Fuzhou	China
7	Fujian Medical University Union Hospital	Fuzhou	China
8	Affiliated Cancer Hospital & Institute of Guangzhou Medical University	Guangzhou	China
9	First Affiliated Hospital, Sun Yat-Sen University	Guangzhou	China
10	Nanfang Hospital of Southern Medical University	Guangzhou	China
11	Sixth Affiliated Hospital, Sun Yat-sen University	Guangzhou	China
12	Yunnan Cancer Hospital	Kunming	China
13	Cancer Hospital of Guangxi Medical University	Nanning	China
14	The Affiliated Hospital of Qingdao University	Qingdao	China
15	Ruijin Hospital	Shanghai	China
16	First Hospital of China Medical University	Shenyang	China
17	The First Affiliated Hospital of Soochow University	Suzhou	China
18	Tianjin Medical University Cancer Institute and Hospital	Tianjin	China
19	Henan Cancer Hospital	Zhengzhou	China
20	The First Affiliated Hospital of Zhengzhou University	Zhengzhou	China
21	Zhenjiang First People's Hospital	Zhenjiang	China
22	San Raffaele University Hospital, Italy	Milan	Italy