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Deep Learning Model Integrating Clinical and Radiomic Features: Risk Stratification for Rapid Progression in EGFR-Mutant Lung Cancer Patients Undergoing TKI Therapy

Deep Learning Model Integrating Clinical and Radiomic Features: Risk Stratification for Rapid Progression in EGFR-Mutant Lung Cancer Patients Undergoing TKI Therapy

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500113335
Enrollment
Unknown
Registered
2025-11-27
Start date
2025-12-01
Completion date
Unknown
Last updated
2025-12-01

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

Conditions

Lung cancer

Interventions

Case series:None

Sponsors

Affiliated Hospital of Guangdong Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 85 Years

Inclusion criteria

Inclusion criteria: 1. Age >= 18 years, to ensure the patient has full civil capacity; 2. Pathologically confirmed diagnosis of lung adenocarcinoma; 3. Availability of complete clinical data, including basic information, symptom presentation, imaging examinations, pathological diagnosis reports, driver gene status, diagnostic staging, treatment plans, treatment efficacy evaluations, and traceable and usable follow-up data; 4. Patient agreement to participate in the study, with signed informed consent.

Exclusion criteria

Exclusion criteria: 1. Presence of other significant comorbidities that severely impact survival or treatment evaluation and cannot be clearly distinguished from the treatment and prognosis of lung cancer; 2. Patients with severe mental illness or cognitive impairment who are unable to cooperate with treatment and follow-up; 3. Pregnant or lactating women, to avoid potential effects on the fetus or infant; 4. Participation in other clinical trials that may interfere with the assessment of treatment efficacy; 5. Patients with severe hepatic or renal dysfunction who cannot tolerate conventional treatment; 6. Patients with severe hematological diseases, such as severe anemia or coagulation disorders, which may affect treatment efficacy and safety; 7. Critical missing clinical data, such as unknown TNM staging, unknown treatment details (e.g., surgery, radiotherapy status), or unknown survival time, rendering the data unsuitable for effective analysis.

Design outcomes

Primary

MeasureTime frame
AUC-ROC;Decision curve analysis, DCA;Progress Free Survival;

Countries

China

Contacts

Public ContactYanli Mo

Affiliated Hospital of Guangdong Medical University

1092548315@qq.com+86 759 238 7458

Outcome results

None listed

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