Non-small Cell Lung CancerNCT06934343
Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data
An observational study of Non-small Cell Lung Cancer, run by University of Utah at 1 site in United States.
Trial summary
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). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Non-small Cell Lung Cancer
- How many people
- 144,400 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2024to 31 Aug 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The primary research focus is on non-small cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases and remains a leading cause of cancer mortality in the United States. This study will use de-identified EHR data from two large nationwide databases - Flatiron and CancerLinQ - as well as de-identified data from Huntsman Cancer Institute (HCI) and Moffitt Cancer Center (MCC) to gain insights into personalized aNSCLC care.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Huntsman Cancer Institute at the University of Utah
Salt Lake City, Utah, 84112
Jincheng Shen, Ph.D., 801-213-4007, jincheng.shen@hsc.utah.edu
Jincheng Shen, Ph.D.
About this study
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.
Read the detailed description
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.
- Conditions
- Non-small Cell Lung Cancer
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Flatiron database | 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. | |
| CancerLinQ database | 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. | |
| Huntsman Cancer Institute (HCI) Cohort | 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. | |
| Moffitt Cancer Center (MCC) Cohort | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Survival (OS)
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.
Measured: From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 10 years.
Secondary outcomes (4)
Quality-Adjusted Life Years (QALYs)
Assessed within two years after treatment initiation.
Measured: From the initiation of first-line therapy to death or the last follow-up, whichever occurs first, up to 2 years.
Time to second progression or death (PFS2)
Time from treatment initiation to second disease progression or death, whichever occurs first.
Measured: From the initiation of first-line therapy to second disease progression, death, or the last follow-up, whichever occurs first, up to 10 years.
National Cancer Institute (NCI) Patient-Reported Outcomes Measurement Information System-Cancer (PROMIS-Ca)
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).
Measured: From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.
Edmonton Symptom Assessment System (ESAS) Outcomes
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.
Measured: From the initiation of first-line therapy to worsening or the last follow-up, whichever occurs first, up to 10 years.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Apr 2025
- Study start1 Sep 2024
- Primary completion31 Aug 2027 (estimated)
- Study completion31 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-small Cell Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 144,400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Utah
- Sponsor type
- University, hospital or other
- Collaborators
- Patient-Centered Outcomes Research Institute
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06934343
- Sponsor's protocol ID
- HCI183476
- Other IDs
- ME-2023C2-33957
- Status last verified
- Apr 2026
- First submitted
- 28 Feb 2025
- Last updated
- 4 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Approaches to Personalized Therapy for Advanced Non-small Cell Lung Cancer With Real-World Data
- ClinicalTrials.gov ID
- NCT06934343
- Acronym / sponsor ID
- HCI183476
- Conditions
- Non-small Cell Lung Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 144,400 participants (estimated)
- Lead sponsor
- University of UtahUniversity, hospital or other
- Collaborators
- Patient-Centered Outcomes Research Institute
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 31 Aug 2027 (estimated)
- Study completion
- 31 Aug 2027 (estimated)
- First posted
- 18 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Flatiron database | – | –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. |
| CancerLinQ database | – | –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. |
| Huntsman Cancer Institute (HCI) Cohort | – | –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. |
| Moffitt Cancer Center (MCC) Cohort | – | –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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The primary research focus is on non-small cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases and remains a leading cause of cancer mortality in the United States. This study will use de-identified EHR data from two large nationwide databases - Flatiron and CancerLinQ - as well as de-identified data from Huntsman Cancer Institute (HCI) and Moffitt Cancer Center (MCC) to gain insights into personalized aNSCLC care.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
4 secondary outcomes
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.
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.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Huntsman Cancer Institute at the University of Utah
Salt Lake City, United States
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-small Cell Lung Cancer, run by University of Utah at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 4 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 May 2026.