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Multimodal data prediction of EGFR mutation targeted therapy efficacy in lung cancer patients based on artificial intelligence

Multimodal data prediction of EGFR mutation targeted therapy efficacy in lung cancer patients based on artificial intelligence

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500110410
Enrollment
Unknown
Registered
2025-10-13
Start date
2025-10-25
Completion date
Unknown
Last updated
2025-10-20

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

Conditions

Lung cancer

Interventions

Observation group:None

Sponsors

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1.Ages 18-80, accepting radical lung cancer surgery (R0 resection); 2.Postoperative pathological staging is IB-IIIA, and the pathology is adenocarcinoma. 3.EGFR gene test positive, EGFR 19del/L858R mutation. 4.Post-operative adjuvant therapy with EGFR-TK1 targeting. 5.There are complete and clear preoperative imaging data, genetic testing reports, and pathological reports.

Exclusion criteria

Exclusion criteria: 1.EGFR negative patients; 2.Incomplete surgical resection (R1, R2); 3.Postoperative EGFR-TKI targeted therapy was not received. 4.Incomplete information before or after surgery; 5.Patients who died within 30 days post-surgery; 6. Patients with recurrence or advanced stages.

Design outcomes

Primary

MeasureTime frame
Disease-free survival after surgery;—

Countries

China

Contacts

Public ContactXiaorong Dong

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

xhzzdxr@126.com+86 13886252286

Outcome results

None listed

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