Clinical trial NCT06035250: AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Official title: Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Gastric Cancer
Image
Pathology
Keywords: Gastric Cancer
Neoadjuvant Chemotherapy
Radiomics
Treatment Outcome Prediction
Pathomics
Radiopathomics
Lead sponsor: Chinese Academy of Sciences
Sponsor class: OTHER_GOV
Collaborators: name: Peking University Cancer Hospital & Institute
name: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
name: Yunnan Cancer Hospital
name: Henan Cancer Hospital
name: Zhenjiang First People's Hospital
name: First Hospital of China Medical University
name: Cancer Hospital of Guangxi Medical University
name: Peking University People's Hospital
name: Tianjin Medical University Cancer Institute and Hospital
name: The First Affiliated Hospital of Zhengzhou University
name: Nanfang Hospital, Southern Medical University
name: The Affiliated Hospital of Qingdao University
name: Ruijin Hospital
name: Sixth Affiliated Hospital, Sun Yat-sen University
name: Peking Union Medical College Hospital
name: Xiangya Hospital of Central South University
name: Affiliated Cancer Hospital & Institute of Guangzhou Medical University
name: The First Affiliated Hospital of Soochow University
name: First Affiliated Hospital, Sun Yat-Sen University
name: Fujian Medical University Union Hospital
name: Fujian Cancer Hospital
name: San Raffaele University Hospital, Italy
First posted: 2023-09-13
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Primary outcomes:
measure: Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model
timeFrame: two months
measure: Accuracy of TRG prediction by the AI model
timeFrame: two months
Secondary outcomes:
measure: Progression-Free Survival (PFS) at 3 years
timeFrame: Three years
measure: Overall Survival (OS) at 5 years
timeFrame: Five years
Other outcomes:
Locations:
city: Beijing
country: China
facility: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
city: Beijing
country: China
facility: Peking Union Medical College Hospital
city: Beijing
country: China
facility: Peking University Cancer Hospital & Institute
city: Beijing
country: China
facility: Peking University People's Hospital
city: Changsha
country: China
facility: Xiangya Hospital of Central South University
city: Fuzhou
country: China
facility: Fujian Cancer Hospital
city: Fuzhou
country: China
facility: Fujian Medical University Union Hospital
city: Guangzhou
country: China
facility: Affiliated Cancer Hospital & Institute of Guangzhou Medical University
city: Guangzhou
country: China
facility: First Affiliated Hospital, Sun Yat-Sen University
city: Guangzhou
country: China
facility: Nanfang Hospital of Southern Medical University
city: Guangzhou
country: China
facility: Sixth Affiliated Hospital, Sun Yat-sen University
city: Kunming
country: China
facility: Yunnan Cancer Hospital
city: Nanning
country: China
facility: Cancer Hospital of Guangxi Medical University
city: Qingdao
country: China
facility: The Affiliated Hospital of Qingdao University
city: Shanghai
country: China
facility: Ruijin Hospital
city: Shenyang
country: China
facility: First Hospital of China Medical University
city: Suzhou
country: China
facility: The First Affiliated Hospital of Soochow University
city: Tianjin
country: China
facility: Tianjin Medical University Cancer Institute and Hospital
city: Zhengzhou
country: China
facility: Henan Cancer Hospital
city: Zhengzhou
country: China
facility: The First Affiliated Hospital of Zhengzhou University
city: Zhenjiang
country: China
facility: Zhenjiang First People's Hospital
city: Milan
country: Italy
facility: San Raffaele University Hospital, Italy
Study officials:
affiliation: Institute of Automation, Chinese Academy of Sciences
name: Yali Zang, Ph.D.
role: STUDY_DIRECTOR
References: