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Machine Learning for Identifying Characteristic Genes of Adenocarcinoma of the Esophagogastric Junction

Machine Learning for Identifying Characteristic Genes of Adenocarcinoma of the Esophagogastric Junction

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500109271
Enrollment
Unknown
Registered
2025-09-16
Start date
2025-09-30
Completion date
Unknown
Last updated
2025-09-22

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

Conditions

Esophagogastric Junction Adenocarcinoma

Interventions

adenocarcinoma of the esophagogastric junction:no intervention

Sponsors

West China Hospital, Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients with postoperative pathological confirmation of adenocarcinoma of the esophagogastric junction; 2. Patients who underwent surgical resection; 3. Patients who received no neoadjuvant chemotherapy, radiotherapy, or targeted therapy before surgery.

Exclusion criteria

Exclusion criteria: 1. Multiple primary cancers; 2. Pathological type classified as other types.

Design outcomes

Primary

MeasureTime frame
Expression levels of Characteristic Genes ;—

Countries

China

Contacts

Public ContactSong Xiaohai

West China Hospital, Sichuan University

1282825332@qq.com+86 184 8362 2465

Outcome results

None listed

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