Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy

Status: Recruiting
Location: See location...
Intervention Type: Diagnostic test
Study Type: Observational
SUMMARY

This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
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• Pathologically confirmed esophageal squamous cell carcinoma (ESCC).

• Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.

• Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.

• Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.

Locations
Other Locations
China
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
RECRUITING
Wuhan
Contact Information
Primary
Yangkai Li, MD, PhD
doclyk@163.com
+8613995516396
Backup
Lin Zhou, MSc
zhoul0928@163.com
Time Frame
Start Date: 2025-03-01
Estimated Completion Date: 2026-12-01
Participants
Target number of participants: 300
Treatments
ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery
Patients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery.
Related Therapeutic Areas
Sponsors
Leads: Tongji Hospital

This content was sourced from clinicaltrials.gov