Brief Summary:
Predicting response to therapy and disease progression in stage IV NSCLC patients treated
with pembrolizumab monotherapy, chemotherapy-pembrolizumab combination therapy or
chemotherapy alone in the first-line setting.
Inclusion Criteria:
- Adult ≥18 years old
- Patient diagnosed with Stage IV NSCLC (de novo or earlier stage progression to stage
IV)
- Absence of oncogene activating mutations eligible patients to targeted therapy (EGFR,
ALK)
- Cohort A: Received first line treatment with pembrolizumab monotherapy
- Cohort B: Received first line treatment with chemotherapy and pembrolizumab
combination therapy
- Cohort C: Received first line treatment with chemotherapy doublet
Exclusion Criteria:
- Prior anti-cancer therapy for actual stage IV NSCLC
- Critical data missing (e.g., PD-L1 status, baseline millimetric imaging, first
evaluation millimetric imaging)
- Patients participating in other clinical trials that modify the standard of care
Primary outcome:
1. Treatment response at first evaluation (Time Frame - 6-12 weeks after treatment start):
Predict treatment response at first evaluation using baseline data
Secondary outcome:
1. Progression-Free Survival (Time Frame - Through study completion, expected 6-14 months contingent on cohort):
Predict Progression-Free Survival (PFS) using data at baseline and first evaluation
2. Overall Survival (Time Frame - Through study completion, expected 8-20 months contingent on cohort):
Predict Overall Survival (OS) using data at baseline and first evaluation
3. Duration of Response (Time Frame - Through study completion, expected 6-14 months contingent on cohort):
Predict Duration of Response (DoR) using data at baseline and first evaluation
4. Time-To-Progression (Time Frame - Through study completion, expected 6-14 months contingent on cohort):
Predict Time-To-Progression (TTP) using data at baseline and first evaluation
- Pembrolizumab monotherapy
- Chemotherapy and pembrolizumab combination therapy
- Chemotherapy doublet
- Predictive models (data collection):
Machine learning predictive models
Quelle: ClinicalTrials.gov
