Using machine learning to personalize treatment for advanced lung cancer

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This research leverages machine learning and causal inference techniques applied to real-world data to personalize treatment strategies for patients with advanced non-small cell lung cancer (aNSCLC). The study aims to refine treatment selection among existing therapeutic options based on individual patient characteristics, rather than influencing regulatory decisions. By utilizing large-scale electronic health record databases and incorporating patient-reported outcomes, the research seeks to enhance patient-centered outcomes and inform future clinical trial designs. The methodology will also establish a framework for identifying optimal treatment regimes for other complex diseases.

Why it matters

Who can join: the full rules

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.

Where it's running

• Huntsman Cancer Institute at the University of Utah
  Salt Lake City, Utah, United States Recruiting

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.