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NCT Number: NCT06934343

Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data

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.

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Key information

Conditions

Non-small Cell Lung Cancer Bronchial Neoplasms Carcinoma, Bronchogenic Carcinoma, Non-Small-Cell Lung Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Huntsman Cancer Institute at the University of Utah

Salt Lake City, Utah, 84112, United States

Location status: Recruiting

Location contact

Jincheng Shen, Ph.D.

CONTACT

[email protected]

801-213-4007

Jincheng Shen, Ph.D.

PRINCIPAL_INVESTIGATOR

About this study

The proposed research will enhance patient-centered outcomes research (PCOR) and comparative effectiveness research (CER) methodologies by addressing two key challenges: (1) appropriate handling of missing EHR data and (2) rigorous causal inference techniques for sequential treatment strategies. By focusing on treatment strategies tailored to individual patients and incorporating patient-reported outcomes (PROs), this study is fundamentally patient-centered. Furthermore, the research is guided by practicing physicians, a patient advocate, and a former patient caregiver, ensuring that it remains aligned with the needs and priorities of those directly affected by aNSCLC.

This study will develop novel reinforcement learning algorithms by integrating multiply robust matching-based approaches. This study will tailor each component of DTR to optimize treatment sequences for aNSCLC patients, leveraging two large-scale, high-quality nationwide real-world electronic health record (EHR) databases: the Flatiron aNSCLC database and the CancerLinQ lung cancer database. These databases provide comprehensive clinicodemographic and longitudinal patient data.

Additionally, incorporating PRO data from two National Cancer Institute (NCI)-designated Comprehensive Cancer Centers -Huntsman Cancer Institute (HCI) and Moffitt Cancer Center (MCC) - will enable this trial to capture the patient perspective when personalizing aNSCLC care recommendations. Key outcomes will include overall survival, quality-adjusted life years (QALYs), time to second progression or death (PFS2), and time to worsening of selected PROs, all framed as time-to-event outcomes.

These methodological innovations will establish a reproducible pipeline for translating real-world evidence from large-scale EHR data into personalized DTR recommendations for aNSCLC patients and other complex disease populations.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Overall Survival (OS)

    Time frame: From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 10 years.

    Mortality is the primary concern in aNSCLC care. This study will track survival from the initiation of first-line therapy to death or the last follow-up, whichever occurs first.

    OS will be censored at the last recorded date in the electronic health records.

Secondary outcomes

  1. Quality-Adjusted Life Years (QALYs)

    Time frame: From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 2 years.

    Assessed within two years after treatment initiation.

  2. Time to second progression or death (PFS2)

    Time frame: From the initiation of first-line therapy to second disease progression, death, or the last follow-up, whichever occurs first, up to 10 years.

    Time from treatment initiation to second disease progression or death, whichever occurs first.

  3. National Cancer Institute (NCI) Patient-Reported Outcomes Measurement Information System-Cancer (PROMIS-Ca)

    Time frame: From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.

    PROs were collected using the NCI PROMIS-Ca Bank at the Huntsman Cancer Institute. Six PROMIS-Ca scales were utilized: the 17-item Fatigue scale (Fatigue-17), the 7-item PROMIS Fatigue scale (Fatigue-7), the 10-item Pain Interference scale (PainInt-10), the 10-item Physical Function scale (PhysFunc-10), the 9-item Anxiety scale (Anxiety-9), and the 10-item Depression scale (Depression-10).

    Each item was rated on a 5-point ordinal scale, with all PROMIS-Ca scales scored so that higher scores indicate greater levels of the respective concept (e.g., a higher Fatigue-7 score reflects greater fatigue, while a higher PhysFunc-10 score denotes better physical functioning).

    Raw scores were calculated as the prorated sum of item responses when at least 50% of the items on a scale were completed. T-scores were derived using a reference population with a mean of 50 and a standard deviation of 10. This outcome measure will report each T-score (range: 0-100).

  4. Edmonton Symptom Assessment System (ESAS) Outcomes

    Time frame: From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.

    The Edmonton Symptom Assessment System (ESAS) was used to evaluate 9 common symptoms: pain, tiredness, drowsiness, nausea, lack of appetite, shortness of breath, depression, anxiety, and overall well-being. Symptoms were rated on an 11-point scale ranging from 0 (no symptom burden) to 10 (worst symptom burden). Moffitt employs a modified version of the ESAS that includes two additional symptoms: constipation and sleep disturbance.

    ESAS will be analyzed using a time-to-worsening framework, with a worsening benchmark of ≥2 points for ESAS-both representing approximately twice the minimally important difference.

Sponsors and collaborators

Lead sponsor

University of Utah

Other

Collaborators

  • Patient-Centered Outcomes Research Institute

Registry information

Important dates

Study start
2024
Primary completion
2027
Study completion
2027
First posted
Apr 18, 2025
Registry last updated
May 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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