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Research on the Application of Explainable Deep Learning Models Based on CT Images to Assist in the Precision Immunotherapy of Esophageal Squamous Cell Carcinoma

Research on the Application of Explainable Deep Learning Models Based on CT Images to Assist in the Precision Immunotherapy of Esophageal Squamous Cell Carcinoma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500098347
Enrollment
Unknown
Registered
2025-03-06
Start date
2025-03-06
Completion date
Unknown
Last updated
2025-03-10

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

Conditions

esophageal squamous cell carcinoma

Interventions

Neoadjuvant immunotherapy group:Neoadjuvant immunotherapy

Sponsors

Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences)
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Patients aged 18-80 years; 2. Esophageal squamous cell carcinoma confirmed by histopathology; 3. Esophageal squamous cell carcinoma was clinically diagnosed; 4. Receiving neoadjuvant therapy plus surgery; 5. Surgical treatment without neoadjuvant therapy.

Exclusion criteria

Exclusion criteria: 1. A history of other malignant tumors; 2. Treatment is not completed; 3. Incomplete medical records.

Design outcomes

Primary

MeasureTime frame
Overall survival, progression-free survival, pathological complete response rate;Net reclassification improvement, NRI;Decision curve analysis, DCA;—

Countries

China

Contacts

Public Contactchenyi xie

Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences)

xiechenyi@gdph.org.cn+86 20 83827812

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026