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CT-based deep learning predicts long-term survival in locally advanced thoracic esophageal squamous cell carcinoma

CT-based deep learning predicts long-term survival in locally advanced thoracic esophageal squamous cell carcinoma

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100049537
Enrollment
Unknown
Registered
2021-08-02
Start date
2021-08-02
Completion date
Unknown
Last updated
2022-04-19

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

Conditions

Esophageal cancer

Interventions

Gold Standard:Clinical outcome
Index test:comprehensive&#32
prognostic&#32
model&#32
of&#32
esophageal&#32
cancer

Sponsors

Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1. Age 18-70 years old, male or female; 2. Histopathologically proven II-IVa thoracic esophageal squamous cell carcinoma (UICC 2002 staging); 3. No treatment (surgery, radiotherapy and chemotherapy) was performed before selection.

Exclusion criteria

Exclusion criteria: 1. Patients with distant metastasis before treatment; 2. Leaving the hospital or giving up treatment and transferring to another hospital for surgery; 3. Those with incomplete clinical data; 4. The image data is incomplete and the image quality is poor.

Design outcomes

Primary

MeasureTime frame
overall survival;locoregional progression free survival;local recurrence free survival;—

Countries

China

Contacts

Public ContactLi Baosheng

Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences

bsli@sdfmu.edu.cn+86 17653115505

Outcome results

None listed

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