Menu
Recruiting NCT06035250

AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

— Observational Gastric Cancer Image Pathology

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Neoadjuvant Chemotherapy.
Who it may be relevant to
Registry conditions: Gastric Cancer, Image, Pathology. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China, Italy
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

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

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model [Time frame: two months]
  • Accuracy of TRG prediction by the AI model [Time frame: two months]
Secondary outcome measures (2)
  • Progression-Free Survival (PFS) at 3 years [Time frame: Three years]
  • Overall Survival (OS) at 5 years [Time frame: Five years]

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 21 centers
  • Cancer Institute and Hospital, Chinese Academy of Medical Sciences — Beijing
  • Peking Union Medical College Hospital — Beijing
  • Peking University Cancer Hospital & Institute — Beijing
  • Peking University People's Hospital — Beijing
  • Xiangya Hospital of Central South University — Changsha
  • Fujian Cancer Hospital — Fuzhou
  • Fujian Medical University Union Hospital — Fuzhou
  • Affiliated Cancer Hospital & Institute of Guangzhou Medical University — Guangzhou
  • … and 13 more centers
Italy · 1 center
  • San Raffaele University Hospital, Italy — Milan

Identifiers

NCT: NCT06035250 · CASMI004

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗