Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer

NCT07063901 | Recruiting

• 1 other identifier: LYF20250118
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

This observational study aims to investigate a clinical cohort of patients with locally advanced esophageal cancer undergoing neoadjuvant chemoimmunotherapy. By integrating multimodal clinical data-including demographic characteristics, medical history, imaging studies, pathological findings, and laboratory tests-and employing deep learning algorithms, the study seeks to develop predictive models for the early and accurate assessment of treatment response prior to surgery. Specifically, this study focuses on addressing the following key scientific questions:

1. 1.Can multimodal clinical data be used to construct an accurate model for predicting pathological complete response (pCR) following neoadjuvant therapy?
2. 2.Can deep learning models enable early identification of patients with suboptimal response to neoadjuvant therapy, defined as stable disease (SD) or progressive disease (PD), before surgery?

Conditions

Esophagus Cancer

Outcome Measures

Primary Outcomes (1)

• pCR

  Pathologic Complete Response

  From enrollment to the end of surgery

Secondary Outcomes (1)

• Non-Favorable Responses

  From enrollment to the end of surgery

Study Arms (4)

group 1: pCR

group 1: Pathologic Complete Response

group 2: Non-pCR

group 2: Non-Pathological Complete Response

group 3: Non-Favorable Responses

group 3: SD/PD

group 4: Favorable Response

group 4: Non-SD/PD

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with esophageal cancer who received neoadjuvant therapy at the Second Xiangya Hospital of Central South University

You may qualify if:

• Patients with histologically confirmed esophageal cancer based on biopsy results;
• Patients recommended for neoadjuvant chemoimmunotherapy following multidisciplinary team (MDT) discussion or evaluation by thoracic surgery specialists;
• Patients who received neoadjuvant chemoimmunotherapy;
• Patients with complete imaging data before and after neoadjuvant treatment.

You may not qualify if:

• Patients deemed eligible for surgery by the thoracic surgery team but who refused surgical treatment;
• Patients with missing or poor-quality CT images;
• Patients with concurrent malignancies other than esophageal cancer;
• Patients with incomplete clinical data.

Sponsors & Collaborators

• Central South University: lead

Study Sites (1)

The Second Xiangya Hospital of Central South University

Changsha, Hunan, 410011, China

RECRUITING

MeSH Terms

Conditions

Esophageal Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Head and Neck Neoplasms; Digestive System Diseases; Esophageal Diseases; Gastrointestinal Diseases

Central Study Contacts

Chen Chen

CONTACT

+8673185295188; chenchen1981412@csu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: July 3, 2025
• First Posted: July 14, 2025
• Study Start: June 1, 2025
• Primary Completion: March 31, 2026
• Study Completion (Estimated): May 31, 2026
• Last Updated: March 24, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)