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A Multimodal Model Integrating Radiomics and Deep Learning for Predicting Esophageal Fistula Following Chemoradiotherapy in Esophageal Cancer

A Multimodal Model Integrating Radiomics and Deep Learning for Predicting Esophageal Fistula Following Chemoradiotherapy in Esophageal Cancer - Multimodal Model for Esophageal Fistula Prediction

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600120218
Enrollment
Unknown
Registered
2026-03-11
Start date
2026-03-11
Completion date
Unknown
Last updated
2026-03-16

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

Conditions

Esophageal fistula after chemoradiotherapy for esophageal squamous cell carcinoma (ESCC)

Interventions

Observation group:None

Sponsors

The First Affiliated Hospital of Henan Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1.Histologically confirmed esophageal squamous cell carcinoma (ESCC); 2.Received definitive chemoradiotherapy with available pre-treatment contrast-enhanced CT images; 3.No prior esophageal surgery.

Exclusion criteria

Exclusion criteria: 1.Incomplete or poor-quality CT images (e.g., severe motion artifacts); 2.Diagnosed with esophageal fistula before treatment; 3.Incomplete pathological or follow-up data; 4.Presence of synchronous malignant tumors; 5.Previous radiotherapy or chemotherapy for esophageal cancer.

Design outcomes

Primary

MeasureTime frame
Model Prediction Performance (such as AUC, accuracy, sensitivity, specificity, etc.);—

Countries

China

Contacts

Public ContactYue Junyan

The First Affiliated Hospital of Henan Medical University

Yuejunyan@126.com+86 159 3650 4625

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Mar 20, 2026