Clinical trial · Observational
Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This research will leverage machine learning (ML) and causal inference techniques applied to real-world data (RWD) to generate evidence that personalizes treatment strategies for patients with advanced non-small cell lung cancer (aNSCLC). Rather than influencing regulatory decisions or clinical guidelines, the goal of this trial is to refine treatment selection among existing therapeutic options, ensuring that care is tailored to individual patient characteristics. Additionally, by generating real-world evidence, these findings will inform the design and implementation of future clinical trials. Importantly, the methodological advancements will establish a pipeline that extends beyond aNSCLC, facilitating the identification of optimal dynamic treatment regimes (DTRs) for other complex diseases.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Non-small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (4)
- label
- Flatiron database
- description
- The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
- label
- CancerLinQ database
- description
- The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
- label
- Huntsman Cancer Institute (HCI) Cohort
- description
- The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
- label
- Moffitt Cancer Center (MCC) Cohort
- description
- The current study will utilize data from national EHR databases (Flatiron and CancerLinQ) and existing cohort data (HCI and MCC). Only de-identified data will be used, and no patients will be contacted during the study.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria Subjects must meet all of the following eligibility criteria: * Diagnosed with advanced NSCLC between January 1, 2011, and June 30, 2024. * Follow-up available until December 31, 2024, with a minimum potential follow-up period of at least six months. Exclusion Criteria Subjects meeting any of the following criteria at baseline will be excluded: * Fewer than one day of follow-up post-initiation of first-line (1L) therapy. * Presence of a targetable mutation, including ALK, BRAF, EGFR, KRAS, or ROS1. * PD-L1 expression \<50% at baseline (restricted to patients with PD-L1 ≥50%). * First-line treatment limited to immunotherapy or chemoimmunotherapy (excluding other treatment regimens). * Patients receiving second-line (2L) treatment, including those enrolled in a clinical study.
References
Publications (0)
Data not yet available