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Development of an individualized artificial intelligence algorithm to predict treatment response to chemotherapy plus immunotherapy in driver gene-negative non-small cell lung cancer patients

Development of an Artificial Intelligence-Based Personalized Prediction Model for Chemotherapy-Combined Immunotherapy Response in NSCLC Patients with Driver Gene Positive

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500109454
Enrollment
Unknown
Registered
2025-09-18
Start date
2025-09-08
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

non-small cell lung cancer

Interventions

Case series:NA

Sponsors

The Fifth Affiliated Hospital of Guangzhou Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1. Patients who provided written informed consent for participation in this observational study; 2. Age between 18 and 75 years (inclusive); 3. Histologically or cytologically confirmed non-small cell lung cancer (NSCLC) according to standard diagnostic criteria; 4. Patients treated at Guangzhou Medical University Fifth Affiliated Hospital between 2019 and 2024; 5. Driver gene-negative tumors (confirmed absence of actionable mutations in EGFR, ALK, ROS1, and other relevant oncogenic drivers); 6. Advanced-stage disease (stage IIIB-IV according to the 8th edition of the AJCC TNM staging system); 7. Eastern Cooperative Oncology Group (ECOG) performance status 0-2; 8. Receipt of combination therapy with immune checkpoint inhibitors and chemotherapy, with completion of at least one treatment cycle; 9. Presence of measurable disease according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1.

Exclusion criteria

Exclusion criteria: 1. History of other malignancies within the past 5 years (except adequately treated basal cell or squamous cell carcinoma of the skin, or carcinoma in situ); 2. Active autoimmune diseases requiring systemic treatment or history of systemic immunosuppressive therapy; 3. Severe organ dysfunction defined as: Severe cardiac insufficiency (New York Heart Association class III-IV); Severe hepatic impairment (Child-Pugh class C); Severe renal insufficiency (estimated glomerular filtration rate 30% missing essential variables); 8. Expected poor compliance or inability to complete follow-up assessments; 9. Prior treatment with immune checkpoint inhibitors; 10. Concurrent participation in other clinical studies that might interfere with study endpoints; 11. Any condition that, in the investigator's judgment, would make participation inappropriate or unsafe.

Design outcomes

Primary

MeasureTime frame
Progression-Free Survival;Treatment Effect Prediction (AUC);

Secondary

MeasureTime frame
Overall Survival;Treatment-Related Adverse Event (AE) Incidence;Adverse Reaction Prediction (AUC);

Countries

China

Contacts

Public ContactGuifen Yu

The Fifth Affiliated Hospital of Guangzhou Medical University

526136010@qq.com+86 134 1814 0245

Outcome results

None listed

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