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

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. Can multimodal clinical data be used to construct an accurate model for predicting pathological complete response (pCR) following neoadjuvant therapy? 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?

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Key information

Conditions

Esophagus Cancer Digestive System Diseases Digestive System Neoplasms Esophageal Diseases Esophageal Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Head and Neck Neoplasms Neoplasms Neoplasms by Site

Sex eligibility

All sexes

Study type

Observational

Primary location

The Second Xiangya Hospital of Central South University

Changsha, Hunan, 410011, China

Location status: Recruiting

Location contact

Ping Jiang

CONTACT

[email protected]

+8673185292476

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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.

Treatment and study plan

Primary outcomes

pCR

Time frame: From enrollment to the end of surgery

Pathologic Complete Response

Secondary outcomes

Non-Favorable Responses

Time frame: From enrollment to the end of surgery

stable disease/progressive disease

Study contacts

Contact information is provided by the study sponsor or research team.

Chen Chen

CONTACT

[email protected]

+8673185295188

Sponsors and collaborators

Lead sponsor

Central South University

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jul 14, 2025
Registry last updated: Jul 7, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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