Development and Application of AI-Based Therapeutic Strategies for Esophageal Cancer Integrating Multimodal Imaging and Digital Pathology

Henan Cancer Hospital

Status: Not yet enrolling

Conditions: Esophageal Cancer; Neoadjuvant Therapy

Study type: Observational

Funder types: Other

Identifiers

NCT07203690

2025-066

Details and patient eligibility

About

The purpose of this clinical study is to conduct a multi-center, big data study to create a neural network decision model for predicting treatment efficacy and prognosis based on multi-modal, multi-temporal imaging features combined with tumor microenvironment scores. It will also use various model interpretation techniques to clarify the role and mechanism of key biomarkers or strongly associated biomarker groups in treatment efficacy and prognosis. Ultimately, it aims to achieve the research and application of AI treatment strategies combining multi-modal imaging and digital pathology to guide clinicians in the personalized treatment strategies for patients with esophageal squamous cell carcinoma.

Enrollment: 7,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Aged 18-70 years;
2. Histologically confirmed esophageal carcinoma by biopsy;
3. No prior antitumor therapy received.

Exclusion criteria

1. Contraindications to MRI examination;
2. Poor compliance with antitumor therapy;
3. Unwillingness to participate in the study;
4. Image quality inadequate for diagnostic requirements.

Trial design

7,000 participants in 1 patient group

Multimodal AI Esophageal Carcinoma Cohort Protocol

Description:

Efficacy and Prognosis of Different Treatment Modalities for Esophageal Squamous Cell Carcinoma