Novel INPUT Screening Tool for Lung Cancer Understanding
This study is testing a new screening tool called INPUT. This tool is designed to help patients with lung cancer that has spread (metastatic) or is incurable better understand their illness and treatment options. The goal is to make sure your medical care aligns with your personal values and preferences. We want to see if using this tool helps patients understand their illness better compared to usual care. To join, you must be at least 18 years old, speak English, have a recent diagnosis of stage IV lung cancer, and be receiving treatment at MD Anderson. We are looking for 100 participants. The study will measure changes in your understanding of your illness over three months.
- Study design
- This interventional study plans to enroll 100 participants. It compares a new screening tool to standard care.
- What's involved
- You would undergo standard oncology follow-up visits and complete questionnaires. Your illness understanding will be measured at 3 months.
- Compensation
- Not stated in the trial record.
- Follow-up
- Your illness understanding will be measured at 3 months.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Novel INPUT Screening Tool to Improve Illness Understanding in Patients With Metastatic or Incurable Lung Cancer
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Kayley Ancy, MD · PRINCIPAL_INVESTIGATOR · M.D. Anderson Cancer Center
Who to contact
Opens a ready-to-send draft in your own email app — review before sending.
Do you actually qualify for this trial?
Add a private profile and we'll compare every criterion below against your situation — and tell you which ones are met, uncertain, or excluding.
Inclusion
What this trial measures
- Change in illness understandingAt 3 months
Binary curability status is derived from the response to the INPUT Screening survey question #2. Will be similarly modeled by mixed-effect logistic regression.