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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07063901

Enrollment

200

Registered

2025-07-14

Start date

2025-06-01

Completion date

2026-12-31

Last updated

2026-07-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Esophagus Cancer

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. 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?

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

1. Patients with histologically confirmed esophageal cancer based on biopsy results; 2. Patients recommended for neoadjuvant chemoimmunotherapy following multidisciplinary team (MDT) discussion or evaluation by thoracic surgery specialists; 3. Patients who received neoadjuvant chemoimmunotherapy; 4. Patients with complete imaging data before and after neoadjuvant treatment.

Exclusion criteria

1. Patients deemed eligible for surgery by the thoracic surgery team but who refused surgical treatment; 2. Patients with missing or poor-quality CT images; 3. Patients with concurrent malignancies other than esophageal cancer; 4. Patients with incomplete clinical data.

Design outcomes

Primary

Measure	Time frame	Description
pCR	From enrollment to the end of surgery	Pathologic Complete Response

Secondary

Measure	Time frame	Description
Non-Favorable Responses	From enrollment to the end of surgery	stable disease/progressive disease

Countries

China

Contacts

CONTACTChen Chen

chenchen1981412@csu.edu.cn+8673185295188

Outcome results